diff options
| author | Johannes Ranke <jranke@uni-bremen.de> | 2023-02-13 05:19:08 +0100 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2023-02-13 05:19:08 +0100 |
| commit | 8d1a84ac2190538ed3bac53a303064e281595868 (patch) | |
| tree | acb894d85ab7ec87c4911c355a5264a77e08e34b | |
| parent | 51d63256a7b3020ee11931d61b4db97b9ded02c0 (diff) | |
| parent | 4200e566ad2600f56bc3987669aeab88582139eb (diff) | |
Merge branch 'main' into custom_lsoda_call
494 files changed, 33507 insertions, 14040 deletions
diff --git a/.Rbuildignore b/.Rbuildignore index 24632401..7e13343b 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -9,6 +9,10 @@ ^test.R$ ^mkin_.*\.tar\.gz ^mkin.tar$ +^inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.pdf$ +^inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton_cache +^inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton_files +^inst/rmarkdown/templates/hierarchical_kinetics/skeleton/dlls ^vignettes/.build.timestamp$ ^vignettes/.*_cache$ ^vignettes/.*cache$ @@ -24,6 +28,8 @@ ^vignettes/.*.synctex.gz ^vignettes/.*.toc$ ^vignettes/figure +^vignettes/prebuilt/*_dlls +^vignettes/prebuilt/*.pdf ^vignettes/FOCUS_Z.tex$ ^vignettes/mkin.tex$ ^vignettes/mkin_benchmarks.rda$ @@ -1,5 +1,9 @@ docs/articles/*_cache/ install.log +inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton_cache +inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton_files +inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.pdf +inst/rmarkdown/templates/hierarchical_kinetics/skeleton/dlls mkin*.tar.gz mkin.tar mkin.Rcheck @@ -24,3 +28,7 @@ vignettes/cache/ vignettes/figure/ vignettes/*_cache/ vignettes/*_files/ +vignettes/prebuilt/*_cache +vignettes/prebuilt/*_files +vignettes/prebuilt/*_dlls +vignettes/prebuilt/*.pdf diff --git a/DESCRIPTION b/DESCRIPTION index a006a446..2a3f231e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,7 +2,7 @@ Package: mkin Type: Package Title: Kinetic Evaluation of Chemical Degradation Data Version: 1.3.0 -Date: 2022-11-15 +Date: 2023-02-13 Authors@R: c( person("Johannes", "Ranke", role = c("aut", "cre", "cph"), email = "johannes.ranke@jrwb.de", @@ -17,13 +17,13 @@ Description: Calculation routines based on the FOCUS Kinetics Report (2006, equation models are solved using automatically generated C functions. Heteroscedasticity can be taken into account using variance by variable or two-component error models as described by Ranke and Meinecke (2018) - <doi:10.3390/environments6120124>. Interfaces to several nonlinear - mixed-effects model packages are available, some of which are described by - Ranke et al. (2021) <doi:10.3390/environments8080071>. Please note that no - warranty is implied for correctness of results or fitness for a particular - purpose. -Depends: R (>= 2.15.1), deSolve -Imports: stats, graphics, methods, parallel, R6, inline (>= 0.3.19), + <doi:10.3390/environments6120124>. Hierarchical degradation models can + be fitted using nonlinear mixed-effects model packages as a backend as + described by Ranke et al. (2021) <doi:10.3390/environments8080071>. Please + note that no warranty is implied for correctness of results or fitness for a + particular purpose. +Depends: R (>= 2.15.1), +Imports: stats, graphics, methods, parallel, deSolve, R6, inline (>= 0.3.19), numDeriv, lmtest, pkgbuild, nlme (>= 3.1-151), saemix (>= 3.1), rlang, vctrs Suggests: knitr, rbenchmark, tikzDevice, testthat, rmarkdown, covr, vdiffr, benchmarkme, tibble, stats4, readxl @@ -36,4 +36,4 @@ VignetteBuilder: knitr BugReports: https://github.com/jranke/mkin/issues/ URL: https://pkgdown.jrwb.de/mkin/ Roxygen: list(markdown = TRUE) -RoxygenNote: 7.2.1 +RoxygenNote: 7.2.3 diff --git a/GNUmakefile b/GNUmakefile index 6e75d666..9f268335 100644 --- a/GNUmakefile +++ b/GNUmakefile @@ -24,7 +24,9 @@ pkgfiles = \ DESCRIPTION \ inst/WORDLIST \ inst/dataset_generation/* \ - inst/testdata/fit_old_FOCUS_D.rda \ + inst/rmarkdown/templates/hierarchical_kinetics/template.yaml \ + inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd \ + inst/testdata/* \ man/* \ NAMESPACE \ NEWS.md \ @@ -35,10 +37,6 @@ pkgfiles = \ all: build $(TGZ): $(pkgfiles) vignettes - $(RM) -r vignettes/*_cache - $(RM) -r vignettes/*_files - $(RM) -r vignettes/*.R - $(RM) -r vignettes/web_only/*.R $(RM) Rplots.pdf "$(RBIN)/R" CMD build . 2>&1 | tee log/build.log @@ -80,12 +78,16 @@ clean: $(RM) -r vignettes/web_only/*.R $(RM) Rplots.pdf +# We set PROCESSX_NOTIFY_OLD_SIGCHILD in order to avoid the message +# "Error while shutting down parallel: unable to terminate some child processes", +# which is said to be harmless, see https://processx.r-lib.org/#mixing-processx-and-the-parallel-base-r-package +# and https://github.com/r-lib/processx/issues/236 test: install - "$(RBIN)/Rscript" -e 'options(cli.dynamic = TRUE); devtools::test()' 2>&1 | tee log/test.log + PROCESSX_NOTIFY_OLD_SIGCHLD=true "$(RBIN)/Rscript" -e 'options(cli.dynamic = TRUE); devtools::test()' 2>&1 | tee log/test.log sed -i -e "s/.*\r.*\r//" log/test.log devtest: devinstall - "$(RDEVBIN)/Rscript" -e 'options(cli.dynamic = TRUE); devtools::test()' 2>&1 | tee log/test_dev.log + PROCESSX_NOTIFY_OLD_SIGCHLD=true "$(RDEVBIN)/Rscript" -e 'options(cli.dynamic = TRUE); devtools::test()' 2>&1 | tee log/test_dev.log sed -i -e "s/\r.*\r//" log/test_dev.log slowtests: install @@ -105,7 +107,8 @@ vignettes: vignettes/mkin.html vignettes/FOCUS_D.html vignettes/FOCUS_L.html vig vignettes/web_only/%.html: vignettes/references.bib vignettes/web_only/%.rmd "$(RBIN)/Rscript" -e "tools::buildVignette(file = 'vignettes/web_only/$*.rmd', dir = 'vignettes/web_only', keep=c('mkin_benchmarks.rda', 'saem_benchmarks.rda'))" -articles: vignettes/web_only/FOCUS_Z.html vignettes/web_only/compiled_models.html vignettes/web_only/benchmarks.html vignettes/web_only/dimethenamid_2018.html vignettes/web_only/multistart.html +vignettes/prebuilt/%.pdf: vignettes/prebuilt/references.bib vignettes/prebuilt/%.rmd + "$(RBIN)/Rscript" -e "rmarkdown::render('vignettes/prebuilt/$*.rmd')" pd: roxygen "$(RBIN)/Rscript" -e "pkgdown::build_site(run_dont_run = TRUE, lazy = TRUE)" @@ -96,6 +96,8 @@ export(create_deg_func) export(endpoints) export(f_time_norm_focus) export(get_deg_func) +export(hierarchical_kinetics) +export(html_listing) export(illparms) export(ilr) export(intervals) @@ -144,6 +146,7 @@ export(set_nd_nq) export(set_nd_nq_focus) export(sigma_twocomp) export(status) +export(summary_listing) export(tex_listing) export(transform_odeparms) export(which.best) @@ -186,6 +189,7 @@ importFrom(stats,qf) importFrom(stats,qlogis) importFrom(stats,qnorm) importFrom(stats,qt) +importFrom(stats,quantile) importFrom(stats,residuals) importFrom(stats,rnorm) importFrom(stats,shapiro.test) @@ -1,12 +1,36 @@ -# mkin 1.2.0 (unreleased) +# mkin 1.2.2 -- 'R/mhmkin.R': New method for performing multiple hierarchical mkin fits in one function call, optionally in parallel. +- 'inst/rmarkdown/templates/hier': R markdown template to facilitate the application of hierarchical kinetic models. -- 'R/mhmkin.R': 'anova.mhmkin' for conveniently comparing the resulting fits. +- 'inst/testdata/lambda-cyhalothrin_soil_efsa_2014.xlsx': Example spreadsheet for use with 'read_spreadsheet()'. -- 'R/illparms.R': New generic to show ill-defined parameters with methods for 'mkinfit', 'mmkin', 'saem.mmkin' and 'mhmkin' objects. +- 'R/mhmkin.R': Allow an 'illparms.mhmkin' object or a list with suitable dimensions as value of the argument 'no_random_effects', making it possible to exclude random effects that were ill-defined in simpler variants of the set of degradation models. Remove the possibility to exclude random effects based on separate fits, as it did not work well. -- 'R/multistart.R': New method for testing multiple start parameters for hierarchical model fits, with function 'llhist' and new generic 'parplot' for diagnostics, and new generics 'which.best' and 'best' for extracting the fit with the highest likelihood +- 'R/summary.saem.mmkin.R': List all initial parameter values in the summary, including random effects and error model parameters. Avoid redundant warnings that occurred in the calculation of correlations of the fixed effects in the case that the Fisher information matrix could not be inverted. List correlations of random effects if specified by the user in the covariance model. + +- 'R/parplot.R': Possibility to select the top 'llquant' fraction of the fits for the parameter plots, and improved legend text. + +- 'R/illparms.R': Also check if confidence intervals for slope parameters in covariate models include zero. Only implemented for fits obtained with the saemix backend. + +- 'R/parplot.R': Make the function work also in the case that some of the multistart runs failed. + +- 'R/intervals.R': Include correlations of random effects in the model in case there are any. + +# mkin 1.2.1 (2022-11-19) + +- '{data,R}/ds_mixed.rda': Include the test data in the package instead of generating it in 'tests/testthat/setup_script.R'. Refactor the generating code to make it consistent and update tests. + +- 'tests/testthat/setup_script.R': Excluded another ill-defined random effect for the DFOP fit with 'saem', in an attempt to avoid a platform dependence that surfaced on Fedora systems on the CRAN check farm + +- 'tests/testthat/test_mixed.R': Round parameters found by saemix to two significant digits before printing, to also help to avoid platform dependence of tests + +- 'R/saem.R': Fix a bug that prevented that 'error.ini' is passed to 'saemix_model', and set default to c(1, 1) to avoid changing test results + +- 'R/parplot.R': Show initial values for error model parameters + +- 'R/loglik.mkinfit.R': Add 'nobs' attribute to the resulting 'logLik' object, in order to make test_AIC.R succeed on current R-devel + +# mkin 1.2.0 (2022-11-17) - 'R/saem.R': 'logLik', 'update' and 'anova' methods for 'saem.mmkin' objects. @@ -14,6 +38,14 @@ - 'R/status.R': New generic to show status information for fit array objects with methods for 'mmkin', 'mhmkin' and 'multistart' objects. +- 'R/mhmkin.R': New method for performing multiple hierarchical mkin fits in one function call, optionally in parallel. + +- 'R/mhmkin.R': 'anova.mhmkin' for conveniently comparing the resulting fits. + +- 'R/illparms.R': New generic to show ill-defined parameters with methods for 'mkinfit', 'mmkin', 'saem.mmkin' and 'mhmkin' objects. + +- 'R/multistart.R': New method for testing multiple start parameters for hierarchical model fits, with function 'llhist' and new generic 'parplot' for diagnostics, and new generics 'which.best' and 'best' for extracting the fit with the highest likelihood + - 'R/summary.mmkin.R': Summary method for mmkin objects. - 'R/saem.R': Implement and test saemix transformations for FOMC and HS. Also, error out if saemix transformations are requested but not supported. diff --git a/R/ds_mixed.R b/R/ds_mixed.R new file mode 100644 index 00000000..c5055712 --- /dev/null +++ b/R/ds_mixed.R @@ -0,0 +1,17 @@ +#' Synthetic data for hierarchical kinetic degradation models +#' +#' The R code used to create this data object is installed with this package in +#' the 'dataset_generation' directory. +#' +#' @name ds_mixed +#' @aliases ds_sfo ds_fomc ds_dfop ds_hs ds_dfop_sfo +#' @examples +#' \dontrun{ +#' sfo_mmkin <- mmkin("SFO", ds_sfo, quiet = TRUE, error_model = "tc", cores = 15) +#' sfo_saem <- saem(sfo_mmkin, no_random_effect = "parent_0") +#' plot(sfo_saem) +#' } +#' +#' # This is the code used to generate the datasets +#' cat(readLines(system.file("dataset_generation/ds_mixed.R", package = "mkin")), sep = "\n") +NULL diff --git a/R/hierarchical_kinetics.R b/R/hierarchical_kinetics.R new file mode 100644 index 00000000..e545754a --- /dev/null +++ b/R/hierarchical_kinetics.R @@ -0,0 +1,40 @@ +#' Hierarchical kinetics template +#' +#' R markdown format for setting up hierarchical kinetics based on a template +#' provided with the mkin package. +#' +#' @inheritParams rmarkdown::pdf_document +#' @param ... Arguments to \code{rmarkdown::pdf_document} +#' +#' @return R Markdown output format to pass to +#' \code{\link[rmarkdown:render]{render}} +#' +#' @examples +#' +#' \dontrun{ +#' library(rmarkdown) +#' draft("example_analysis.rmd", template = "hierarchical_kinetics", package = "mkin") +#' } +#' +#' @export +hierarchical_kinetics <- function(..., keep_tex = FALSE) { + + if (getRversion() < "4.1.0") + stop("You need R with version > 4.1.0 to compile this document") + + if (!requireNamespace("knitr")) stop("Please install the knitr package to use this template") + if (!requireNamespace("rmarkdown")) stop("Please install the rmarkdown package to use this template") + knitr::opts_chunk$set(echo = FALSE, cache = TRUE, comment = "", tidy = FALSE, echo = TRUE) + knitr::opts_chunk$set(fig.align = "center", fig.pos = "H") + options(knitr.kable.NA = "") + + fmt <- rmarkdown::pdf_document(..., + keep_tex = keep_tex, + toc = TRUE, + toc_depth = 3, + includes = rmarkdown::includes(in_header = "header.tex"), + extra_dependencies = c("float", "listing", "framed") + ) + + return(fmt) +} diff --git a/R/illparms.R b/R/illparms.R index 01e75cf1..eef4bd33 100644 --- a/R/illparms.R +++ b/R/illparms.R @@ -20,6 +20,9 @@ #' @param random For hierarchical fits, should random effects be tested? #' @param errmod For hierarchical fits, should error model parameters be #' tested? +#' @param slopes For hierarchical [saem] fits using saemix as backend, +#' should slope parameters in the covariate model(starting with 'beta_') be +#' tested? #' @return For [mkinfit] or [saem] objects, a character vector of parameter #' names. For [mmkin] or [mhmkin] objects, a matrix like object of class #' 'illparms.mmkin' or 'illparms.mhmkin'. @@ -92,7 +95,7 @@ print.illparms.mmkin <- function(x, ...) { #' @rdname illparms #' @export -illparms.saem.mmkin <- function(object, conf.level = 0.95, random = TRUE, errmod = TRUE, ...) { +illparms.saem.mmkin <- function(object, conf.level = 0.95, random = TRUE, errmod = TRUE, slopes = TRUE, ...) { if (inherits(object$so, "try-error")) { ill_parms <- NA } else { @@ -106,6 +109,15 @@ illparms.saem.mmkin <- function(object, conf.level = 0.95, random = TRUE, errmod ill_parms_errmod <- ints$errmod[, "lower"] < 0 & ints$errmod[, "est."] > 0 ill_parms <- c(ill_parms, names(which(ill_parms_errmod))) } + if (slopes) { + if (is.null(object$so)) stop("Slope testing is only implemented for the saemix backend") + slope_names <- grep("^beta_", object$so@model@name.fixed, value = TRUE) + ci <- object$so@results@conf.int + rownames(ci) <- ci$name + slope_ci <- ci[slope_names, ] + ill_parms_slopes <- slope_ci[, "lower"] < 0 & slope_ci[, "estimate"] > 0 + ill_parms <- c(ill_parms, slope_names[ill_parms_slopes]) + } } class(ill_parms) <- "illparms.saem.mmkin" return(ill_parms) diff --git a/R/intervals.R b/R/intervals.R index 705ef6eb..fcdbaea9 100644 --- a/R/intervals.R +++ b/R/intervals.R @@ -78,8 +78,12 @@ intervals.saem.mmkin <- function(object, level = 0.95, backtransform = TRUE, ... # Random effects sdnames <- intersect(rownames(conf.int), paste("SD", pnames, sep = ".")) - ranef_ret <- as.matrix(conf.int[sdnames, c("lower", "est.", "upper")]) - rownames(ranef_ret) <- paste0(gsub("SD\\.", "sd(", sdnames), ")") + corrnames <- grep("^Corr.", rownames(conf.int), value = TRUE) + ranef_ret <- as.matrix(conf.int[c(sdnames, corrnames), c("lower", "est.", "upper")]) + sdnames_ret <- paste0(gsub("SD\\.", "sd(", sdnames), ")") + corrnames_ret <- gsub("Corr\\.(.*)\\.(.*)", "corr(\\1,\\2)", corrnames) + rownames(ranef_ret) <- c(sdnames_ret, corrnames_ret) + attr(ranef_ret, "label") <- "Random effects:" diff --git a/R/logLik.mkinfit.R b/R/logLik.mkinfit.R index 7cc10234..abf8517c 100644 --- a/R/logLik.mkinfit.R +++ b/R/logLik.mkinfit.R @@ -37,6 +37,7 @@ logLik.mkinfit <- function(object, ...) { val <- object$logLik # Number of estimated parameters attr(val, "df") <- length(object$bparms.optim) + length(object$errparms) + attr(val, "nobs") <- nobs(object) class(val) <- "logLik" return(val) } @@ -12,13 +12,14 @@ #' degradation models to the same data #' @param backend The backend to be used for fitting. Currently, only saemix is #' supported -#' @param no_random_effect Default is NULL and will be passed to [saem]. If -#' you specify "auto", random effects are only included if the number -#' of datasets in which the parameter passed the t-test is at least 'auto_ranef_threshold'. -#' Beware that while this may make for convenient model reduction or even -#' numerical stability of the algorithm, it will likely lead to -#' underparameterised models. -#' @param auto_ranef_threshold See 'no_random_effect. +#' @param no_random_effect Default is NULL and will be passed to [saem]. If a +#' character vector is supplied, it will be passed to all calls to [saem], +#' which will exclude random effects for all matching parameters. Alternatively, +#' a list of character vectors or an object of class [illparms.mhmkin] can be +#' specified. They have to have the same dimensions that the return object of +#' the current call will have, i.e. the number of rows must match the number +#' of degradation models in the mmkin object(s), and the number of columns must +#' match the number of error models used in the mmkin object(s). #' @param algorithm The algorithm to be used for fitting (currently not used) #' @param \dots Further arguments that will be passed to the nonlinear mixed-effects #' model fitting function. @@ -50,8 +51,44 @@ mhmkin.mmkin <- function(objects, ...) { #' @export #' @rdname mhmkin +#' @examples +#' \dontrun{ +#' # We start with separate evaluations of all the first six datasets with two +#' # degradation models and two error models +#' f_sep_const <- mmkin(c("SFO", "FOMC"), ds_fomc[1:6], cores = 2, quiet = TRUE) +#' f_sep_tc <- update(f_sep_const, error_model = "tc") +#' # The mhmkin function sets up hierarchical degradation models aka +#' # nonlinear mixed-effects models for all four combinations, specifying +#' # uncorrelated random effects for all degradation parameters +#' f_saem_1 <- mhmkin(list(f_sep_const, f_sep_tc), cores = 2) +#' status(f_saem_1) +#' # The 'illparms' function shows that in all hierarchical fits, at least +#' # one random effect is ill-defined (the confidence interval for the +#' # random effect expressed as standard deviation includes zero) +#' illparms(f_saem_1) +#' # Therefore we repeat the fits, excluding the ill-defined random effects +#' f_saem_2 <- update(f_saem_1, no_random_effect = illparms(f_saem_1)) +#' status(f_saem_2) +#' illparms(f_saem_2) +#' # Model comparisons show that FOMC with two-component error is preferable, +#' # and confirms our reduction of the default parameter model +#' anova(f_saem_1) +#' anova(f_saem_2) +#' # The convergence plot for the selected model looks fine +#' saemix::plot(f_saem_2[["FOMC", "tc"]]$so, plot.type = "convergence") +#' # The plot of predictions versus data shows that we have a pretty data-rich +#' # situation with homogeneous distribution of residuals, because we used the +#' # same degradation model, error model and parameter distribution model that +#' # was used in the data generation. +#' plot(f_saem_2[["FOMC", "tc"]]) +#' # We can specify the same parameter model reductions manually +#' no_ranef <- list("parent_0", "log_beta", "parent_0", c("parent_0", "log_beta")) +#' dim(no_ranef) <- c(2, 2) +#' f_saem_2m <- update(f_saem_1, no_random_effect = no_ranef) +#' anova(f_saem_2m) +#' } mhmkin.list <- function(objects, backend = "saemix", algorithm = "saem", - no_random_effect = NULL, auto_ranef_threshold = 3, + no_random_effect = NULL, ..., cores = if (Sys.info()["sysname"] == "Windows") 1 else parallel::detectCores(), cluster = NULL) { @@ -97,27 +134,42 @@ mhmkin.list <- function(objects, backend = "saemix", algorithm = "saem", dimnames(fit_indices) <- list(degradation = names(deg_models), error = error_models) + if (is.null(no_random_effect) || is.null(dim(no_random_effect))) { + no_ranef <- rep(list(no_random_effect), n.fits) + dim(no_ranef) <- dim(fit_indices) + } else { + if (!identical(dim(no_random_effect), dim(fit_indices))) { + stop("Dimensions of argument 'no_random_effect' are not suitable") + } + if (is(no_random_effect, "illparms.mhmkin")) { + no_ranef_dim <- dim(no_random_effect) + no_ranef <- lapply(no_random_effect, function(x) { + no_ranef_split <- strsplit(x, ", ") + ret <- sapply(no_ranef_split, function(y) { + gsub("sd\\((.*)\\)", "\\1", y) + }) + return(ret) + }) + dim(no_ranef) <- no_ranef_dim + } else { + no_ranef <- no_random_effect + } + } + fit_function <- function(fit_index) { w <- which(fit_indices == fit_index, arr.ind = TRUE) deg_index <- w[1] error_index <- w[2] mmkin_row <- objects[[error_index]][deg_index, ] - if (identical(no_random_effect, "auto")) { - ip <- illparms(mmkin_row) - ipt <- table(unlist(lapply(as.list(ip), strsplit, ", "))) - no_ranef <- names(ipt)[which((length(ds) - ipt) <= auto_ranef_threshold)] - transparms <- rownames(mmkin_row[[1]]$start_transformed) - bparms <- rownames(mmkin_row[[1]]$start) - names(transparms) <- bparms - no_ranef_trans <- transparms[no_ranef] - } else { - no_ranef_trans <- no_random_effect - } res <- try(do.call(backend_function, - args = c(list(mmkin_row), dot_args, list(no_random_effect = no_ranef_trans)))) + args = c( + list(mmkin_row), + dot_args, + list(no_random_effect = no_ranef[[deg_index, error_index]])))) return(res) } + fit_time <- system.time({ if (is.null(cluster)) { results <- parallel::mclapply(as.list(1:n.fits), fit_function, @@ -143,15 +195,16 @@ mhmkin.list <- function(objects, backend = "saemix", algorithm = "saem", #' @param j Column index selecting the fits to specific datasets #' @param drop If FALSE, the method always returns an mhmkin object, otherwise #' either a list of fit objects or a single fit object. -#' @return An object of class \code{\link{mhmkin}}. +#' @return An object inheriting from \code{\link{mhmkin}}. #' @rdname mhmkin #' @export `[.mhmkin` <- function(x, i, j, ..., drop = FALSE) { + original_class <- class(x) class(x) <- NULL x_sub <- x[i, j, drop = drop] if (!drop) { - class(x_sub) <- "mhmkin" + class(x_sub) <- original_class } return(x_sub) } diff --git a/R/mkinmod.R b/R/mkinmod.R index b1fb57cb..215dbed6 100644 --- a/R/mkinmod.R +++ b/R/mkinmod.R @@ -26,7 +26,7 @@ #' Additionally, [mkinsub()] has an argument \code{to}, specifying names of #' variables to which a transfer is to be assumed in the model. #' If the argument \code{use_of_ff} is set to "min" -#' (default) and the model for the compartment is "SFO" or "SFORB", an +#' and the model for the compartment is "SFO" or "SFORB", an #' additional [mkinsub()] argument can be \code{sink = FALSE}, effectively #' fixing the flux to sink to zero. #' In print.mkinmod, this argument is currently not used. diff --git a/R/multistart.R b/R/multistart.R index 61ef43dc..bdfbfe63 100644 --- a/R/multistart.R +++ b/R/multistart.R @@ -100,9 +100,12 @@ multistart.saem.mmkin <- function(object, n = 50, cores = 1, } if (is.null(cluster)) { - res <- parallel::mclapply(1:n, fit_function, mc.cores = cores) + res <- parallel::mclapply(1:n, fit_function, + mc.cores = cores, mc.preschedule = FALSE) } else { - res <- parallel::parLapply(cluster, 1:n, fit_function) + res <- parallel::parLapplyLB(cluster, 1:n, fit_function, + mc.preschedule = FALSE + ) } attr(res, "orig") <- object attr(res, "start_parms") <- start_parms @@ -77,6 +77,6 @@ parms.multistart <- function(object, exclude_failed = TRUE, ...) { successful <- which(!is.na(res[, 1])) first_success <- successful[1] colnames(res) <- names(parms(object[[first_success]])) - if (exclude_failed) res <- res[successful, ] + if (exclude_failed[1]) res <- res[successful, ] return(res) } diff --git a/R/parplot.R b/R/parplot.R index 627a4eb8..3da4b51a 100644 --- a/R/parplot.R +++ b/R/parplot.R @@ -4,9 +4,16 @@ #' either by the parameters of the run with the highest likelihood, #' or by their medians as proposed in the paper by Duchesne et al. (2021). #' +#' Starting values of degradation model parameters and error model parameters +#' are shown as green circles. The results obtained in the original run +#' are shown as red circles. +#' #' @param object The [multistart] object #' @param llmin The minimum likelihood of objects to be shown -#' @param scale By default, scale parameters using the best available fit. +#' @param llquant Fractional value for selecting only the fits with higher +#' likelihoods. Overrides 'llmin'. +#' @param scale By default, scale parameters using the best +#' available fit. #' If 'median', parameters are scaled using the median parameters from all fits. #' @param main Title of the plot #' @param lpos Positioning of the legend. @@ -16,7 +23,7 @@ #' of the in vitro erythropoiesis. BMC Bioinformatics. 2021 Oct 4;22(1):478. #' doi: 10.1186/s12859-021-04373-4. #' @seealso [multistart] -#' @importFrom stats median +#' @importFrom stats median quantile #' @export parplot <- function(object, ...) { UseMethod("parplot") @@ -24,7 +31,8 @@ parplot <- function(object, ...) { #' @rdname parplot #' @export -parplot.multistart.saem.mmkin <- function(object, llmin = -Inf, scale = c("best", "median"), +parplot.multistart.saem.mmkin <- function(object, llmin = -Inf, llquant = NA, + scale = c("best", "median"), lpos = "bottomleft", main = "", ...) { oldpar <- par(no.readonly = TRUE) @@ -32,8 +40,8 @@ parplot.multistart.saem.mmkin <- function(object, llmin = -Inf, scale = c("best" orig <- attr(object, "orig") orig_parms <- parms(orig) - start_parms <- orig$mean_dp_start - all_parms <- parms(object) + start_degparms <- orig$mean_dp_start + all_parms <- parms(object, exclude_failed = FALSE) if (inherits(object, "multistart.saem.mmkin")) { llfunc <- function(object) { @@ -44,23 +52,27 @@ parplot.multistart.saem.mmkin <- function(object, llmin = -Inf, scale = c("best" stop("parplot is only implemented for multistart.saem.mmkin objects") } ll <- sapply(object, llfunc) + if (!is.na(llquant[1])) { + if (llmin != -Inf) warning("Overriding 'llmin' because 'llquant' was specified") + llmin <- quantile(ll, 1 - llquant) + } selected <- which(ll > llmin) selected_parms <- all_parms[selected, ] par(las = 1) if (orig$transformations == "mkin") { - degparm_names_transformed <- names(start_parms) + degparm_names_transformed <- names(start_degparms) degparm_index <- which(names(orig_parms) %in% degparm_names_transformed) orig_parms[degparm_names_transformed] <- backtransform_odeparms( orig_parms[degparm_names_transformed], orig$mmkin[[1]]$mkinmod, transform_rates = orig$mmkin[[1]]$transform_rates, transform_fractions = orig$mmkin[[1]]$transform_fractions) - start_parms <- backtransform_odeparms(start_parms, + start_degparms <- backtransform_odeparms(start_degparms, orig$mmkin[[1]]$mkinmod, transform_rates = orig$mmkin[[1]]$transform_rates, transform_fractions = orig$mmkin[[1]]$transform_fractions) - degparm_names <- names(start_parms) + degparm_names <- names(start_degparms) names(orig_parms) <- c(degparm_names, names(orig_parms[-degparm_index])) @@ -72,6 +84,13 @@ parplot.multistart.saem.mmkin <- function(object, llmin = -Inf, scale = c("best" colnames(selected_parms)[1:length(degparm_names)] <- degparm_names } + start_errparms <- orig$so@model@error.init + names(start_errparms) <- orig$so@model@name.sigma + + start_omegaparms <- orig$so@model@omega.init + + start_parms <- c(start_degparms, start_errparms) + scale <- match.arg(scale) parm_scale <- switch(scale, best = selected_parms[which.best(object[selected]), ], @@ -99,7 +118,7 @@ parplot.multistart.saem.mmkin <- function(object, llmin = -Inf, scale = c("best" legend(lpos, inset = c(0.05, 0.05), bty = "n", pch = 1, col = 3:1, lty = c(NA, NA, 1), legend = c( - "Starting parameters", - "Original run", + "Original start", + "Original results", "Multistart runs")) } diff --git a/R/read_spreadsheet.R b/R/read_spreadsheet.R index a20af6db..7ad09c3e 100644 --- a/R/read_spreadsheet.R +++ b/R/read_spreadsheet.R @@ -37,7 +37,7 @@ #' and moisture normalisation factors in the sheet 'Datasets'? #' @export read_spreadsheet <- function(path, valid_datasets = "all", - parent_only = TRUE, normalize = TRUE) + parent_only = FALSE, normalize = TRUE) { if (!requireNamespace("readxl", quietly = TRUE)) stop("Please install the readxl package to use this function") @@ -149,7 +149,7 @@ saem.mmkin <- function(object, covariates = NULL, covariate_models = NULL, no_random_effect = NULL, - error.init = c(3, 0.1), + error.init = c(1, 1), nbiter.saemix = c(300, 100), control = list(displayProgress = FALSE, print = FALSE, nbiter.saemix = nbiter.saemix, @@ -718,6 +718,7 @@ saemix_model <- function(object, solution_type = "auto", covariance.model = covariance.model, covariate.model = covariate.model, omega.init = omega.init, + error.init = error.init, ... ) attr(res, "mean_dp_start") <- degparms_optim diff --git a/R/summary.saem.mmkin.R b/R/summary.saem.mmkin.R index 2754e9f0..49b02a50 100644 --- a/R/summary.saem.mmkin.R +++ b/R/summary.saem.mmkin.R @@ -75,10 +75,21 @@ #' f_saem_dfop_sfo <- saem(f_mmkin_dfop_sfo) #' print(f_saem_dfop_sfo) #' illparms(f_saem_dfop_sfo) -#' f_saem_dfop_sfo_2 <- update(f_saem_dfop_sfo, covariance.model = diag(c(0, 0, 1, 1, 1, 0))) +#' f_saem_dfop_sfo_2 <- update(f_saem_dfop_sfo, +#' no_random_effect = c("parent_0", "log_k_m1")) #' illparms(f_saem_dfop_sfo_2) #' intervals(f_saem_dfop_sfo_2) #' summary(f_saem_dfop_sfo_2, data = TRUE) +#' # Add a correlation between random effects of g and k2 +#' cov_model_3 <- f_saem_dfop_sfo_2$so@model@covariance.model +#' cov_model_3["log_k2", "g_qlogis"] <- 1 +#' cov_model_3["g_qlogis", "log_k2"] <- 1 +#' f_saem_dfop_sfo_3 <- update(f_saem_dfop_sfo, +#' covariance.model = cov_model_3) +#' intervals(f_saem_dfop_sfo_3) +#' # The correlation does not improve the fit judged by AIC and BIC, although +#' # the likelihood is higher with the additional parameter +#' anova(f_saem_dfop_sfo, f_saem_dfop_sfo_2, f_saem_dfop_sfo_3) #' } #' #' @export @@ -136,10 +147,11 @@ summary.saem.mmkin <- function(object, data = FALSE, verbose = FALSE, distimes = } # Correlation of fixed effects (inspired by summary.nlme) - varFix <- try(vcov(object$so)[1:n_fixed, 1:n_fixed]) - if (inherits(varFix, "try-error")) { + cov_so <- try(solve(object$so@results@fim), silent = TRUE) + if (inherits(cov_so, "try-error")) { object$corFixed <- NA } else { + varFix <- cov_so[1:n_fixed, 1:n_fixed] stdFix <- sqrt(diag(varFix)) object$corFixed <- array( t(varFix/stdFix)/stdFix, @@ -149,7 +161,8 @@ summary.saem.mmkin <- function(object, data = FALSE, verbose = FALSE, distimes = # Random effects sdnames <- intersect(rownames(conf.int), paste0("SD.", pnames)) - confint_ranef <- as.matrix(conf.int[sdnames, c("estimate", "lower", "upper")]) + corrnames <- grep("^Corr.", rownames(conf.int), value = TRUE) + confint_ranef <- as.matrix(conf.int[c(sdnames, corrnames), c("estimate", "lower", "upper")]) colnames(confint_ranef)[1] <- "est." # Error model @@ -225,13 +238,22 @@ print.summary.saem.mmkin <- function(x, digits = max(3, getOption("digits") - 3) obs = "Variance unique to each observed variable", tc = "Two-component variance function"), "\n") - cat("\nMean of starting values for individual parameters:\n") + cat("\nStarting values for degradation parameters:\n") print(x$mean_dp_start, digits = digits) cat("\nFixed degradation parameter values:\n") if(length(x$fixed$value) == 0) cat("None\n") else print(x$fixed, digits = digits) + cat("\nStarting values for random effects (square root of initial entries in omega):\n") + print(sqrt(x$so@model@omega.init), digits = digits) + + cat("\nStarting values for error model parameters:\n") + errparms <- x$so@model@error.init + names(errparms) <- x$so@model@name.sigma + errparms <- errparms[x$so@model@indx.res] + print(errparms, digits = digits) + cat("\nResults:\n\n") cat("Likelihood computed by importance sampling\n") print(data.frame(AIC = x$AIC, BIC = x$BIC, logLik = x$logLik, diff --git a/R/summary_listing.R b/R/summary_listing.R new file mode 100644 index 00000000..38b52240 --- /dev/null +++ b/R/summary_listing.R @@ -0,0 +1,59 @@ +#' Display the output of a summary function according to the output format +#' +#' This function is intended for use in a R markdown code chunk with the chunk +#' option `results = "asis"`. +#' +#' @param object The object for which the summary is to be listed +#' @param caption An optional caption +#' @param label An optional label, ignored in html output +#' @param clearpage Should a new page be started after the listing? Ignored in html output +#' @export +summary_listing <- function(object, caption = NULL, label = NULL, + clearpage = TRUE) { + if (knitr::is_latex_output()) { + tex_listing(object = object, caption = caption, label = label, + clearpage = clearpage) + } + if (knitr::is_html_output()) { + html_listing(object = object, caption = caption) + } +} + +#' @rdname summary_listing +#' @export +tex_listing <- function(object, caption = NULL, label = NULL, + clearpage = TRUE) { + cat("\n") + cat("\\begin{listing}", "\n") + if (!is.null(caption)) { + cat("\\caption{", caption, "}", "\n", sep = "") + } + if (!is.null(label)) { + cat("\\caption{", label, "}", "\n", sep = "") + } + cat("\\begin{snugshade}", "\n") + cat("\\scriptsize", "\n") + cat("\\begin{verbatim}", "\n") + cat(capture.output(suppressWarnings(summary(object))), sep = "\n") + cat("\n") + cat("\\end{verbatim}", "\n") + cat("\\end{snugshade}", "\n") + cat("\\end{listing}", "\n") + if (clearpage) { + cat("\\clearpage", "\n") + } +} + +#' @rdname summary_listing +#' @export +html_listing <- function(object, caption = NULL) { + cat("\n") + if (!is.null(caption)) { + cat("<caption>", caption, "</caption>", "\n", sep = "") + } + cat("<pre><code>\n") + cat(capture.output(suppressWarnings(summary(object))), sep = "\n") + cat("\n") + cat("</pre></code>\n") +} + diff --git a/R/tex_listing.R b/R/tex_listing.R deleted file mode 100644 index 05f662e4..00000000 --- a/R/tex_listing.R +++ /dev/null @@ -1,32 +0,0 @@ -#' Wrap the output of a summary function in tex listing environment -#' -#' This function can be used in a R markdown code chunk with the chunk -#' option `results = "asis"`. -#' -#' @param object The object for which the summary is to be listed -#' @param caption An optional caption -#' @param label An optional label -#' @param clearpage Should a new page be started after the listing? -#' @export -tex_listing <- function(object, caption = NULL, label = NULL, - clearpage = TRUE) { - cat("\n") - cat("\\begin{listing}", 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) 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ke2C8vfjgV/Y9kkRQOJIn/xM9INZSpiBnqX0Q9GlQPpPKAyO5y+W5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W/N6l54qOynCqD3DpWQ+mpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE+7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv/8lbTIkkYpqKM9VOhp65ktYk+Q46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI+ejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5+7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu+vtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY/iOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v+6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ/qDQK+bfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam+WpHG+0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3+J1eCBay8IYZ0wQRKGAqvCuZ/UgbQPyllosq+XtfKIZOzmeJqRazpmmoP/76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t/I4Jktu0XSgifO2ozFOiwd/0SssJDn0dn4xqk4GDTTKX73/wQyBLdqgJ+Wx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy/qqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh/wkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml/R4yrzow1Q2A5G+kzo/RhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj/UMMc34IBqTKLDTp76WzL/dMjCxK7MjhiGjeYAC/kj/jY/Rde7hpSM1xChrog6yZ7OWTuD56xBJnGFE+pT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G+9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36/dBySis4m9/DR8izaLJW6bWCkVgm5T+ius3ZXq4xI+GnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te/r4dPYMCl5qtiHNTn+TPbh1jCBHH+dMJNhwNgs3nT+OhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF+uRIWyXjQMAs2chdpaKPNaB+kSezYt0+CA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6/TzoA1/ZBG9bIUVHLAbi/kdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh+epgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF+zkJHIA7PwCDk1gGVmGUZSSoPhNf+Tklauz98QofOlCIQ/tCD4dosHYPqtPCXB3agggQQIqQJsSkB+qn0rkQ1toJjON/OtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU+TQ6NIw3ej+AtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb++W6Uk4q6F7/rg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK+EfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l+wM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l+DMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg+EWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb+Mw76Qy29iQ5up/X7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa/xnsMYcIO/vEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz/Chp/Vlpj2P7jJQmQRwGnltkTV5dbF9fE3/fxoSqTROgq9wFUlbuYzYcasE0ouzBo+dDCDzxKAfhbAZYxQiHrLzV2iVexnDX/QnT1fsT/xuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR+CtGdkPwYN2o7DOw/VGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO/f9Qua+pDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD/prQ84B1pVGkIpVUAHCG+iz3Bn3qm2AVrYcYWhock4jso5+J7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk+UILT7+VoX5mdxxA5fS42gISQVi/HTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8/6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk/wdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt+Su9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ/BfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk/gc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B//lHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO+CvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd/p1gi/07h8qfwHrByuSxglc9cI4QIg2oqvC/qm0i7tjPLTgDhoWTAKDO2ONW5oe+/eKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2/j4ODUwRkqrtBBCrDsDpt8jhZdXoy/1BCqw3sSGhgGGy0a5Jw6BP/TExoCmNFYjZl248A0osgPyGEmRA+fAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd/ocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE/EZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ/Ugd/JZQK8lvAm43uDRAbyW8gZ+ZGq0EVerVGUKUSm/Idn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ+QATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm+oOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724+zUQ7+vkCpZB+pGA562hYQiDxHVWOq0oDQl/QsoiY+cuI7iWq/ZIBtHcXJ7kks+h2fCNUPA82BzjnqktNts+RLdk1VSu+tqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy/44XYXdI5noQoRcvjZ1RMPACRqYg2V1+OwOepcOknRLLFdYgTkT5UApt/JhLM3jeFYprZV+Zow2g8fP+U68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr/A1SY9dXFz4RjzoU9ExfJCmx/I9FKEGT3n2cmzl2X42L3Jh+AbQq6sA+Ss1kitoa4TAYgKHaoybHUDJ51oETdeI/9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB+Un44zExFE4vLytcu5NwpWrUxO/0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL+BDqMyaN3RJPh/anbx+Iv+qgQdAa3M9Z5JmvYlh4qop+Ho1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs+gs37sFvi0PPVvA5dnCBgILTwoKd/+DoL9F6inlM7H4rOTzD79KJgKlZO/Zgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno+gBoKVXgIL/VI8dB1O5o/R3Suez/gD7M781ShjKpIIORM/nxG+jjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4/QftDbEn+AucIr1oxrLabRj9q4ae0+fXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd+eNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz/6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT+mAUmiYbV3YQVqFVp9dorv+TsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp+xJyYLv1OsxQCZwTB4a8BZ/5EdxTBJthApqyfd9u3ifr/WILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj/qn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9+W8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4/YtowhEmTs0vrvlD/CrisnoBNDAcUi/teY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O/4DcgV/dZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk+tgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb/n7qwhvGnrHuf5bX6Vh/n3xffU3PeHdR+FA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E+YbfL0adwNtHP7dT7t7b46DVZIkzaRJOM+S6KcrzYVg+T3wSRFRQashjfU18NutrKa/7PXbtuJvpIjbgPeqd+pjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir/8tNXJ/OsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG+FZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx/GdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD/Qi/EmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4/asthNMK5UQKCOhU97oaOYNGsTah+jfCKsZnTRn5TbhFX8ghg8CBYt/BjeYYYUrtUZ5jVij/op7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM+3SW6Opll/wgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy+QbSKVJcqkia+GvDefFwMOmgnD7h81TUtMn+mRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d/QK7Cr4uoCeOQ7/8JfKT77KiDzLImESHw/0wf73QeHu74hxv7uihi4fTX+XEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo/oVH5ww5OzLFsiTPDns7fS6EURSSWd/92BxMYQ8sBaH+j+wthQPdVgDGpTfi+JQIWMD8xKqULliRH01rTeyF8x8q/GBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE+/7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV/yTDRRP8Y2ww5RO6d2D94o+6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt+kqdae76ViWe3STan69yaF9+fESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw/A1zEdjWquIsQXXGIVEH0thC5M+W9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF+RVmI8L4HUYk4x+67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8/p5qIQrEo/H+1l/0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud+tJUfdaZ4CWNijzZtlRa8+CkmO/EwHYfPZFU/hzjFWH7vnzHRMo+aF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce/+/9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs/GWJS6SwEN/ULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e+G2zor8l+YaMxSEomDdLHGcD6YVQPegTaA74H8+V4WvJkFUrjMLGLlvSZQWvi8/QA7yzQ8GPno//5SJHRP/OqKObPCo81s/+6WgLqykYpGAgQZhVDEBPXWgU/WzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M+GWn6ASobIWC+LbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg/kpf3+CnAXKiMgIE8Jk/Mhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo+RJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B+SkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE+VMd3b1fhCynD0pQNhCG6/WCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp/YbHjo++7/Wj8S4YNa+ZdqAw1hDrKWFXv9+zaXpf8ZTDSbiqsxnwN/CzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m/NCW/HILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO+5EJ7Z6bCiRoPedRZ/P0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn/LvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl+11PoFYnNv2HwAODeNRkHj+8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij+bsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur/eWHjiB7SOVdqMAVmpBvfRiebsFjger7DC+8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l+kXRZ0KLZaGsFSIdQz/HXDxf3/TE30+DgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH+RptvRMVRaahu4cySjS3P5wxAUCPkmn+rhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik/zbrTQk5KmgxCg/f45L0jywebOWUYFJQAJia7XzCV0x89rpp/f3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl+5tfUWZNwBpEPXghzbBggYCw/dhy0ntds2yeHCDKkF/YxQjNIL/F/37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD+qNOhwMlfARQUdJ2tUX+MNJqOwIciWalZsmEjt07tfa8ma4cji9sqz+Q9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe+jGDiNJQLWnfx+drTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf/bkvo8PLVBsZl152y5S8+HRDfZIMCbYZ1WDp4yrdchOJw8k6R+/2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB+M4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5/iYp3ZdrCf7fL+en+sIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv+NRiJc8JAKqqgCA/PNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN/hCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj+bYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5/zBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8/i+jHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2+JrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk+Mz7wwstg6RFZb+TZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm/7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk+9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx+whVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC+XYuqMBMUun5YezKbRKmEPwuK+CLzijPEQgfhahQswBBLfg/GBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX+RCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk/4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug/AbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM+Zu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX/pcsfwXbLze2+D+u33OGBoJyAAL3jn3RuEcdp5If8O+a4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT++tm+haBCikRUUMrMhYKZJKYoVuv/bsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV/DMUxd9uFZmBfKXMCn/SqkWJyKPnT6lq+4zBZni6fYRByJn6OK+OgPBGRAJluwGSk4wxjOOzyce/PKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO/cEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm+bddRQu5F9s0XftGEJ9JSuSk+ZachCbdU45fEqbugzTIUokwoAKvpUQF/CvLbWW5BNQFqFkJg2f30E/48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J+1pT0tLkvFVZaNzfhs/Kd3+A9YsImlO4XK4vpCo/elHQi/9gkFg07xxnuXLt21unCIpDV+bbRxb7FC6nWYTsMFF8+1LUg4JFjVt3vqbuhHmDKbgQ4e+RGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB/KVijd1ARWkFEf3yiUw1v/WaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G+ZManTqDLPjyrOse7WiiwOJCG+J0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj/6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j/N46f+S2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb/0jQbaHJ2YRK8A+ls6WMhWmpCXYG5jqapGs5/eOJErxi2/2KWVHiPellTgh/fNl/2KYPKb7DUcAg+mCOPQFCiU9Mq/WLcU1xxC8aLePFZZlE+PCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh/nFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW+oKFLvpyvTBMM69tN1Ydwv1LIEhHsC+ueVG+w+kyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw+H/AuOx+aH+tBL88H57D0MsqyiZxhOEQkF/8DR1d2hSPMj/sNOa5rxcUnBgH8ictv2J+cb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd/PjMMtQfyDNZsOPd6XcAsnBE/mRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl/XPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG/VanIvcwycVA7+BE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP/MVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX/5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c/F1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J/5wkjpkre727p5PTRX5FGrSBIfJqhJE/IS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug+oRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U/5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7/BQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN+nA7pvF78/RII5ZHA09OAiE/66MF6HQ+qVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe+hXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz+JV/4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB+K3wBP/ineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q/9uocGsx41O4IZhViw/2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY/cQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE/om7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi/caHSM3FPRGRf7dB7YC+cD2ho6oL2zGDCkjlf/DFoQVl8GS/56wur3rdV6ggtzZW60MRB3g+U1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg/gBQIZMG/YcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK/G7F3mUc3GOAKqh60zM0v34v+ELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND+/GTk6M56Ig4yMsU6LUW1EWE+fIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP/IoRIZ/F6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg/RrPD/d3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl+Mu4xf0ezqeXD2PtPDnwMPo86sbwDV+9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD/OwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d/UfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH+14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC+OA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6+vAUUBoGhY3CT2tgwehdPqU/4Q7ZLYvhRl/ogOvR9O2+wkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn/dFSWBWzQ/VYk+Gezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n/yJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET/Hh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j+DXfpi12m1RbzYLg9J2wFergEwOxFyD0/JstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT/cUP6pE/mujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB+HEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu+vqQ02+KpJBjaLt9ye1Ab+BbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC+UwkXOoAjneU/xHplMQo2cXUlrVNqJYczgYlaOEczVCs/OCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38+xsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9+b8fH6+b8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj/0Q7PFUcC8hDrxESWdfgFRm+7vvWbkEppHB4T/1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y+g3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR/Rrs/JLKXgEx+qkmeDlFOD1/yTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW+xz+5FElFxWB28VjYIGZ0Yd+5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ+lT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ+2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1+JpI9psyNYIFuJogZa0/1AhOWdlHQxdAgbwacsHqPZo8u/ngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2+RdM+MAaYaZ0Y/ADkbNCZuAyAVQa2OcXOeICmDn9Q/eFkDeFQg5MgHEDXq/tVjj+jtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2/Bc0UxvseQCO2pQ2i+Krfhu/WeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt/U0Wf+phiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7+ExseJauyqo30hs+1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j/e1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la/QCTiVelFnU6O/GCvykqS/wZJDhKN9gBtSOp/1SP5VRgJcoVj+kmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn+8p6+vBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H+gDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D/GvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P+jAgN5TB3haSifDcApp6yymEi6Ij/GsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x/pChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11/yxyw0UnGig3MFdZklN5FI/qiT65T+jOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5+bqWiAYiAv6Jsf79/VUs4cIl+n6+WOjcgB/2l5TreoAV2717JzZbQIR0W1cl/dEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW/PhoawJDrGAP0JYWHgAVUByo/bGdiv2T2EMg8gsS14/rAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq+SCcTSx5NDtbFwNlh8VhjGGDu7JG5/TAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9+ogD8Kk/W7QoRN1UWPqM4+xdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c+4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS+o3F8YVVeikw13w+OEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX++7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9/O53DYi/5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD+P8sNh6e+XYHJXT/lkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp+pT0KYV5dGtknU64JhabdqcVQbG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) 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alt="Build Status" /></a> <a href="https://codecov.io/github/jranke/mkin"><img 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alt="codecov" /></a></p> -<p>The R package <strong>mkin</strong> provides calculation routines for the analysis of chemical degradation data, including <b>m</b>ulticompartment <b>kin</b>etics as needed for modelling the formation and decline of transformation products, or if several degradation compartments are involved.</p> +<p><a href="https://cran.r-project.org/package=mkin"><img src="data:image/svg+xml; 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alt="Build Status" /></a> <a href="https://codecov.io/github/jranke/mkin"><img src="data:image/svg+xml;base64,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" alt="codecov" /></a></p> +<p>The <a href="https://r-project.org">R</a> package +<strong>mkin</strong> provides calculation routines for the analysis of +chemical degradation data, including <b>m</b>ulticompartment +<b>kin</b>etics as needed for modelling the formation and decline of +transformation products, or if several degradation compartments are +involved. It provides stable functionality for kinetic evaluations +according to the FOCUS guidance (see below for details). In addition, it +provides functionality to do hierarchical kinetics based on nonlinear +mixed-effects models.</p> <div id="installation" class="section level2"> <h2>Installation</h2> -<p>You can install the latest released version from <a href="https://cran.r-project.org/package=mkin">CRAN</a> from within R:</p> +<p>You can install the latest released version from <a href="https://cran.r-project.org/package=mkin">CRAN</a> from within +R:</p> <pre class="r"><code>install.packages("mkin")</code></pre> </div> <div id="background" class="section level2"> <h2>Background</h2> -<p>In the regulatory evaluation of chemical substances like plant protection products (pesticides), biocides and other chemicals, degradation data play an important role. For the evaluation of pesticide degradation experiments, detailed guidance and helpful tools have been developed as detailed in ‘Credits and historical remarks’ below.</p> +<p>In the regulatory evaluation of chemical substances like plant +protection products (pesticides), biocides and other chemicals, +degradation data play an important role. For the evaluation of pesticide +degradation experiments, detailed guidance and various helpful tools +have been developed as detailed in ‘Credits and historical remarks’ +below. This package aims to provide a one stop solution for degradation +kinetics, addressing modellers that are willing to, or even prefer to +work with R.</p> </div> <div id="usage" class="section level2"> <h2>Usage</h2> -<p>For a start, have a look at the code examples provided for <a href="https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html"><code>plot.mkinfit</code></a> and <a href="https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html"><code>plot.mmkin</code></a>, and at the package vignettes <a href="https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html"><code>FOCUS L</code></a> and <a href="https://pkgdown.jrwb.de/mkin/articles/FOCUS_D.html"><code>FOCUS D</code></a>.</p> +<p>For a start, have a look at the code examples provided for <a href="https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html"><code>plot.mkinfit</code></a> +and <a href="https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html"><code>plot.mmkin</code></a>, +and at the package vignettes <a href="https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html"><code>FOCUS L</code></a> +and <a href="https://pkgdown.jrwb.de/mkin/articles/FOCUS_D.html"><code>FOCUS D</code></a>.</p> </div> <div id="documentation" class="section level2"> <h2>Documentation</h2> -<p>The HTML documentation of the latest version released to CRAN is available at <a href="https://pkgdown.jrwb.de/mkin/">jrwb.de</a> and <a href="https://jranke.github.io/mkin/">github</a>. Documentation of the development version is found in the <a href="https://pkgdown.jrwb.de/mkin/dev/">‘dev’ subdirectory</a>.</p> +<p>The HTML documentation of the latest version released to CRAN is +available at <a href="https://pkgdown.jrwb.de/mkin/">jrwb.de</a> and <a href="https://jranke.github.io/mkin/">github</a>. Documentation of the +development version is found in the <a href="https://pkgdown.jrwb.de/mkin/dev/">‘dev’ subdirectory</a>.</p> </div> <div id="features" class="section level2"> <h2>Features</h2> <div id="general" class="section level3"> <h3>General</h3> <ul> -<li>Highly flexible model specification using <a href="https://pkgdown.jrwb.de/mkin/reference/mkinmod.html"><code>mkinmod</code></a>, including equilibrium reactions and using the single first-order reversible binding (SFORB) model, which will automatically create two latent state variables for the observed variable.</li> -<li>Model solution (forward modelling) in the function <a href="https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html"><code>mkinpredict</code></a> is performed either using the analytical solution for the case of parent only degradation, an eigenvalue based solution if only simple first-order (SFO) or SFORB kinetics are used in the model, or using a numeric solver from the <code>deSolve</code> package (default is <code>lsoda</code>).</li> -<li>The usual one-sided t-test for significant difference from zero is nevertheless shown based on estimators for the untransformed parameters.</li> -<li>Summary and plotting functions. The <code>summary</code> of an <code>mkinfit</code> object is in fact a full report that should give enough information to be able to approximately reproduce the fit with other tools.</li> -<li>The chi-squared error level as defined in the FOCUS kinetics guidance (see below) is calculated for each observed variable.</li> -<li>The ‘variance by variable’ error model which is often fitted using Iteratively Reweighted Least Squares (IRLS) can be specified as <code>error_model = "obs"</code>.</li> +<li>Highly flexible model specification using <a href="https://pkgdown.jrwb.de/mkin/reference/mkinmod.html"><code>mkinmod</code></a>, +including equilibrium reactions and using the single first-order +reversible binding (SFORB) model, which will automatically create two +state variables for the observed variable.</li> +<li>Model solution (forward modelling) in the function <a href="https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html"><code>mkinpredict</code></a> +is performed either using the analytical solution for the case of parent +only degradation or some simple models involving a single transformation +product, , an eigenvalue based solution if only simple first-order (SFO) +or SFORB kinetics are used in the model, or using a numeric solver from +the <code>deSolve</code> package (default is <code>lsoda</code>).</li> +<li>The usual one-sided t-test for significant difference from zero is +shown based on estimators for the untransformed parameters.</li> +<li>Summary and plotting functions. The <code>summary</code> of an +<code>mkinfit</code> object is in fact a full report that should give +enough information to be able to approximately reproduce the fit with +other tools.</li> +<li>The chi-squared error level as defined in the FOCUS kinetics +guidance (see below) is calculated for each observed variable.</li> +<li>The ‘variance by variable’ error model which is often fitted using +Iteratively Reweighted Least Squares (IRLS) can be specified as +<code>error_model = "obs"</code>.</li> </ul> </div> <div id="unique-in-mkin" class="section level3"> <h3>Unique in mkin</h3> <ul> -<li>Three different error models can be selected using the argument <code>error_model</code> to the <a href="https://pkgdown.jrwb.de/mkin/reference/mkinfit.html"><code>mkinfit</code></a> function. A two-component error model similar to the one proposed by <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">Rocke and Lorenzato</a> can be selected using the argument <code>error_model = "tc"</code>.</li> -<li>Model comparisons using the Akaike Information Criterion (AIC) are supported which can also be used for non-constant variance. In such cases the FOCUS chi-squared error level is not meaningful.</li> -<li>By default, kinetic rate constants and kinetic formation fractions are transformed internally using <a href="https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html"><code>transform_odeparms</code></a> so their estimators can more reasonably be expected to follow a normal distribution.</li> -<li>When parameter estimates are backtransformed to match the model definition, confidence intervals calculated from standard errors are also backtransformed to the correct scale, and will not include meaningless values like negative rate constants or formation fractions adding up to more than 1, which cannot occur in a single experiment with a single defined radiolabel position.</li> -<li>When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite. Mathematically, the SFORB model is equivalent to the DFOP model used by other tools for biphasic metabolite curves. However, the SFORB model has the advantage that there is a mechanistic interpretation of the model parameters.</li> -<li>Nonlinear mixed-effects models can be created from fits of the same degradation model to different datasets for the same compound by using the <a href="https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html">nlme.mmkin</a> and <a href="https://pkgdown.jrwb.de/mkin/reference/saem.html">saem.mmkin</a> and methods. Note that the convergence of the nlme fits depends on the quality of the data. Convergence is better for simple models and data for many groups (e.g. soils). The saem method uses the <code>saemix</code> package as a backend. Analytical solutions suitable for use with this package have been implemented for parent only models and the most important models including one metabolite (SFO-SFO and DFOP-SFO). Fitting other models with <code>saem.mmkin</code>, while it makes use of the compiled ODE models that mkin provides, has longer run times (at least six minutes on my system).</li> +<li>Three different error models can be selected using the argument +<code>error_model</code> to the <a href="https://pkgdown.jrwb.de/mkin/reference/mkinfit.html"><code>mkinfit</code></a> +function. A two-component error model similar to the one proposed by <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">Rocke +and Lorenzato</a> can be selected using the argument +<code>error_model = "tc"</code>.</li> +<li>Model comparisons using the Akaike Information Criterion (AIC) are +supported which can also be used for non-constant variance. In such +cases the FOCUS chi-squared error level is not meaningful.</li> +<li>By default, kinetic rate constants and kinetic formation fractions +are transformed internally using <a href="https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html"><code>transform_odeparms</code></a> +so their estimators can more reasonably be expected to follow a normal +distribution.</li> +<li>When parameter estimates are backtransformed to match the model +definition, confidence intervals calculated from standard errors are +also backtransformed to the correct scale, and will not include +meaningless values like negative rate constants or formation fractions +adding up to more than 1, which cannot occur in a single experiment with +a single defined radiolabel position.</li> +<li>When a metabolite decline phase is not described well by SFO +kinetics, SFORB kinetics can be used for the metabolite. Mathematically, +the SFORB model is equivalent to the DFOP model. However, the SFORB +model has the advantage that there is a mechanistic interpretation of +the model parameters.</li> +<li>Nonlinear mixed-effects models (hierarchical models) can be created +from fits of the same degradation model to different datasets for the +same compound by using the <a href="https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html">nlme.mmkin</a> +and <a href="https://pkgdown.jrwb.de/mkin/reference/saem.html">saem.mmkin</a> +methods. Note that the convergence of the nlme fits depends on the +quality of the data. Convergence is better for simple models and data +for many groups (e.g. soils). The saem method uses the +<code>saemix</code> package as a backend. Analytical solutions suitable +for use with this package have been implemented for parent only models +and the most important models including one metabolite (SFO-SFO and +DFOP-SFO). Fitting other models with <code>saem.mmkin</code>, while it +makes use of the compiled ODE models that mkin provides, has longer run +times (from a couple of minutes to more than an hour).</li> </ul> </div> <div id="performance" class="section level3"> <h3>Performance</h3> <ul> -<li>Parallel fitting of several models to several datasets is supported, see for example <a href="https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html"><code>plot.mmkin</code></a>.</li> -<li>If a C compiler is installed, the kinetic models are compiled from automatically generated C code, see <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html">vignette <code>compiled_models</code></a>. The autogeneration of C code was inspired by the <a href="https://github.com/karlines/ccSolve"><code>ccSolve</code></a> package. Thanks to Karline Soetaert for her work on that.</li> -<li>Even if no compiler is installed, many degradation models still give <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html">very good performance</a>, as current versions of mkin also have <a href="https://jrwb.de/performance-improvements-mkin/">analytical solutions for some models with one metabolite</a>, and if SFO or SFORB are used for the parent compound, Eigenvalue based solutions of the degradation model are available.</li> +<li>Parallel fitting of several models to several datasets is supported, +see for example <a href="https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html"><code>plot.mmkin</code></a>.</li> +<li>If a C compiler is installed, the kinetic models are compiled from +automatically generated C code, see <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html">vignette +<code>compiled_models</code></a>. The autogeneration of C code was +inspired by the <a href="https://github.com/karlines/ccSolve"><code>ccSolve</code></a> +package. Thanks to Karline Soetaert for her work on that.</li> +<li>Even if no compiler is installed, many degradation models still give +<a href="https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html">very +good performance</a>, as current versions of mkin also have <a href="https://jrwb.de/performance-improvements-mkin/">analytical +solutions for some models with one metabolite</a>, and if SFO or SFORB +are used for the parent compound, Eigenvalue based solutions of the +degradation model are available.</li> </ul> </div> </div> <div id="gui" class="section level2"> <h2>GUI</h2> -<p>There is a graphical user interface that may be useful. Please refer to its <a href="https://pkgdown.jrwb.de/gmkin/">documentation page</a> for installation instructions and a manual.</p> +<p>There is a graphical user interface that may be useful. Please refer +to its <a href="https://pkgdown.jrwb.de/gmkin/">documentation page</a> +for installation instructions and a manual. It only supports evaluations +using (generalised) nonlinear regression, but not simultaneous fits +using nonlinear mixed-effects models.</p> </div> <div id="news" class="section level2"> <h2>News</h2> -<p>There is a list of changes for the latest <a href="https://cran.r-project.org/package=mkin/news/news.html">CRAN release</a> and one for each github branch, e.g. <a href="https://github.com/jranke/mkin/blob/main/NEWS.md">the main branch</a>.</p> +<p>There is a list of changes for the latest <a href="https://cran.r-project.org/package=mkin/news/news.html">CRAN +release</a> and one for each github branch, e.g. <a href="https://github.com/jranke/mkin/blob/main/NEWS.md">the main +branch</a>.</p> </div> <div id="credits-and-historical-remarks" class="section level2"> <h2>Credits and historical remarks</h2> -<p><code>mkin</code> would not be possible without the underlying software stack consisting of, among others, R and the package <a href="https://cran.r-project.org/package=deSolve">deSolve</a>. In previous version, <code>mkin</code> was also using the functionality of the <a href="https://cran.r-project.org/package=FME">FME</a> package. Please refer to the <a href="https://cran.r-project.org/package=mkin">package page on CRAN</a> for the full list of imported and suggested R packages. Also, <a href="https://debian.org">Debian Linux</a>, the vim editor and the <a href="https://github.com/jalvesaq/Nvim-R">Nvim-R</a> plugin have been invaluable in its development.</p> -<p><code>mkin</code> could not have been written without me being introduced to regulatory fate modelling of pesticides by Adrian Gurney during my time at Harlan Laboratories Ltd (formerly RCC Ltd). <code>mkin</code> greatly profits from and largely follows the work done by the <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">FOCUS Degradation Kinetics Workgroup</a>, as detailed in their guidance document from 2006, slightly updated in 2011 and in 2014.</p> -<p>Also, it was inspired by the first version of KinGUI developed by BayerCropScience, which is based on the MatLab runtime environment.</p> -<p>The companion package <a href="http://kinfit.r-forge.r-project.org/kinfit_static/index.html">kinfit</a> (now deprecated) was <a href="https://r-forge.r-project.org/scm/viewvc.php?view=rev&root=kinfit&revision=2">started in 2008</a> and <a href="https://cran.r-project.org/src/contrib/Archive/kinfit/">first published</a> on CRAN on 01 May 2010.</p> -<p>The first <code>mkin</code> code was <a href="https://r-forge.r-project.org/scm/viewvc.php?view=rev&root=kinfit&revision=8">published on 11 May 2010</a> and the <a href="https://cran.r-project.org/src/contrib/Archive/mkin/">first CRAN version</a> on 18 May 2010.</p> -<p>In 2011, Bayer Crop Science started to distribute an R based successor to KinGUI named KinGUII whose R code is based on <code>mkin</code>, but which added, among other refinements, a closed source graphical user interface (GUI), iteratively reweighted least squares (IRLS) optimisation of the variance for each of the observed variables, and Markov Chain Monte Carlo (MCMC) simulation functionality, similar to what is available e.g. in the <code>FME</code> package.</p> -<p>Somewhat in parallel, Syngenta has sponsored the development of an <code>mkin</code> and KinGUII based GUI application called CAKE, which also adds IRLS and MCMC, is more limited in the model formulation, but puts more weight on usability. CAKE is available for download from the <a href="https://cake-kinetics.org">CAKE website</a>, where you can also find a zip archive of the R scripts derived from <code>mkin</code>, published under the GPL license.</p> -<p>Finally, there is <a href="https://github.com/zhenglei-gao/KineticEval">KineticEval</a>, which contains a further development of the scripts used for KinGUII, so the different tools will hopefully be able to learn from each other in the future as well.</p> -<p>Thanks to René Lehmann, formerly working at the Umweltbundesamt, for the nice cooperation cooperation on parameter transformations, especially the isometric log-ratio transformation that is now used for formation fractions in case there are more than two transformation targets.</p> -<p>Many inspirations for improvements of mkin resulted from doing kinetic evaluations of degradation data for my clients while working at Harlan Laboratories and at Eurofins Regulatory AG, and now as an independent consultant.</p> -<p>Funding was received from the Umweltbundesamt in the course of the projects</p> +<p><code>mkin</code> would not be possible without the underlying +software stack consisting of, among others, R and the package <a href="https://cran.r-project.org/package=deSolve">deSolve</a>. In +previous version, <code>mkin</code> was also using the functionality of +the <a href="https://cran.r-project.org/package=FME">FME</a> package. +Please refer to the <a href="https://cran.r-project.org/package=mkin">package page on CRAN</a> +for the full list of imported and suggested R packages. Also, <a href="https://debian.org">Debian Linux</a>, the vim editor and the <a href="https://github.com/jalvesaq/Nvim-R">Nvim-R</a> plugin have been +invaluable in its development.</p> +<p><code>mkin</code> could not have been written without me being +introduced to regulatory fate modelling of pesticides by Adrian Gurney +during my time at Harlan Laboratories Ltd (formerly RCC Ltd). +<code>mkin</code> greatly profits from and largely follows the work done +by the <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">FOCUS +Degradation Kinetics Workgroup</a>, as detailed in their guidance +document from 2006, slightly updated in 2011 and in 2014.</p> +<p>Also, it was inspired by the first version of KinGUI developed by +BayerCropScience, which is based on the MatLab runtime environment.</p> +<p>The companion package <a href="http://kinfit.r-forge.r-project.org/kinfit_static/index.html">kinfit</a> +(now deprecated) was <a href="https://r-forge.r-project.org/scm/viewvc.php?view=rev&root=kinfit&revision=2">started +in 2008</a> and <a href="https://cran.r-project.org/src/contrib/Archive/kinfit/">first +published</a> on CRAN on 01 May 2010.</p> +<p>The first <code>mkin</code> code was <a href="https://r-forge.r-project.org/scm/viewvc.php?view=rev&root=kinfit&revision=8">published +on 11 May 2010</a> and the <a href="https://cran.r-project.org/src/contrib/Archive/mkin/">first CRAN +version</a> on 18 May 2010.</p> +<p>In 2011, Bayer Crop Science started to distribute an R based +successor to KinGUI named KinGUII whose R code is based on +<code>mkin</code>, but which added, among other refinements, a closed +source graphical user interface (GUI), iteratively reweighted least +squares (IRLS) optimisation of the variance for each of the observed +variables, and Markov Chain Monte Carlo (MCMC) simulation functionality, +similar to what is available e.g. in the <code>FME</code> package.</p> +<p>Somewhat in parallel, Syngenta has sponsored the development of an +<code>mkin</code> and KinGUII based GUI application called CAKE, which +also adds IRLS and MCMC, is more limited in the model formulation, but +puts more weight on usability. CAKE is available for download from the +<a href="https://cake-kinetics.org">CAKE website</a>, where you can also +find a zip archive of the R scripts derived from <code>mkin</code>, +published under the GPL license.</p> +<p>Finally, there is <a href="https://github.com/zhenglei-gao/KineticEval">KineticEval</a>, +which contains some further development of the scripts used for +KinGUII.</p> +<p>Thanks to René Lehmann, formerly working at the Umweltbundesamt, for +the nice cooperation on parameter transformations, especially the +isometric log-ratio transformation that is now used for formation +fractions in case there are more than two transformation targets.</p> +<p>Many inspirations for improvements of mkin resulted from doing +kinetic evaluations of degradation data for my clients while working at +Harlan Laboratories and at Eurofins Regulatory AG, and now as an +independent consultant.</p> +<p>Funding was received from the Umweltbundesamt in the course of the +projects</p> <ul> -<li>Project Number 27452 (Testing and validation of modelling software as an alternative to ModelMaker 4.0, 2014-2015)</li> -<li>Project Number 56703 (Optimization of gmkin for routine use in the Umweltbundesamt, 2015)</li> -<li>Project Number 92570 (Update of Project Number 27452, 2017-2018)</li> -<li>Project Number 112407 (Testing the feasibility of using an error model according to Rocke and Lorenzato for more realistic parameter estimates in the kinetic evaluation of degradation data, 2018-2019)</li> -<li>Project Number 120667 (Development of objective criteria for the evaluation of the visual fit in the kinetic evaluation of degradation data, 2019-2020)</li> -<li>Project Number 146839 (Checking the feasibility of using mixed-effects models for the derivation of kinetic modelling parameters from degradation studies, 2020-2021)</li> +<li>Project Number 27452 (Testing and validation of modelling software +as an alternative to ModelMaker 4.0, 2014-2015)</li> +<li>Project Number 56703 (Optimization of gmkin for routine use in the +Umweltbundesamt, 2015)</li> +<li>Project Number 92570 (Update of Project Number 27452, +2017-2018)</li> +<li>Project Number 112407 (Testing the feasibility of using an error +model according to Rocke and Lorenzato for more realistic parameter +estimates in the kinetic evaluation of degradation data, 2018-2019)</li> +<li>Project Number 120667 (Development of objective criteria for the +evaluation of the visual fit in the kinetic evaluation of degradation +data, 2019-2020)</li> +<li>Project Number 146839 (Checking the feasibility of using +mixed-effects models for the derivation of kinetic modelling parameters +from degradation studies, 2020-2021)</li> +<li>Project Number 173340 (Application of nonlinear hierarchical models +to the kinetic evaluation of chemical degradation data)</li> </ul> -<p>Thanks are due also to Emmanuelle Comets, maintainer of the saemix package, for the nice collaboration on using the SAEM algorithm and its implementation in saemix for the evaluation of chemical degradation data.</p> +<p>Thanks to everyone involved for collaboration and support!</p> +<p>Thanks are due also to Emmanuelle Comets, maintainer of the saemix +package, for her interest and support for using the SAEM algorithm and +its implementation in saemix for the evaluation of chemical degradation +data.</p> </div> <div id="references" class="section level2"> <h2>References</h2> <table> <tr> <td> -Ranke J, Wöltjen J, Schmidt J, and Comets E (2021) Taking kinetic evaluations of degradation data to the next level with nonlinear mixed-effects models. <i>Environments</i> <b>8</b> (8) 71 <a href="https://doi.org/10.3390/environments8080071">doi:10.3390/environments8080071</a> +Ranke J, Wöltjen J, Schmidt J, and Comets E (2021) Taking kinetic +evaluations of degradation data to the next level with nonlinear +mixed-effects models. <i>Environments</i> <b>8</b> (8) 71 +<a href="https://doi.org/10.3390/environments8080071">doi:10.3390/environments8080071</a> </td> </tr> <tr> <td> -Ranke J, Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical Degradation Data <i>Environments</i> <b>6</b> (12) 124 <a href="https://doi.org/10.3390/environments6120124">doi:10.3390/environments6120124</a> +Ranke J, Meinecke S (2019) Error Models for the Kinetic Evaluation of +Chemical Degradation Data <i>Environments</i> <b>6</b> (12) 124 +<a href="https://doi.org/10.3390/environments6120124">doi:10.3390/environments6120124</a> </td> </tr> <tr> <td> -Ranke J, Wöltjen J, Meinecke S (2018) Comparison of software tools for kinetic evaluation of chemical degradation data <i>Environmental Sciences Europe</i> <b>30</b> 17 <a href="https://doi.org/10.1186/s12302-018-0145-1">doi:10.1186/s12302-018-0145-1</a> +Ranke J, Wöltjen J, Meinecke S (2018) Comparison of software tools for +kinetic evaluation of chemical degradation data <i>Environmental +Sciences Europe</i> <b>30</b> 17 +<a href="https://doi.org/10.1186/s12302-018-0145-1">doi:10.1186/s12302-018-0145-1</a> </td> </tr> </table> @@ -5,10 +5,13 @@ [](https://app.travis-ci.com/github/jranke/mkin) [](https://codecov.io/github/jranke/mkin) -The R package **mkin** provides calculation routines for the analysis of +The [R](https://r-project.org) package **mkin** provides calculation routines for the analysis of chemical degradation data, including <b>m</b>ulticompartment <b>kin</b>etics as needed for modelling the formation and decline of transformation products, or -if several degradation compartments are involved. +if several degradation compartments are involved. It provides stable functionality +for kinetic evaluations according to the FOCUS guidance (see below for details). +In addition, it provides functionality to do hierarchical kinetics based on +nonlinear mixed-effects models. ## Installation @@ -24,8 +27,10 @@ install.packages("mkin") In the regulatory evaluation of chemical substances like plant protection products (pesticides), biocides and other chemicals, degradation data play an important role. For the evaluation of pesticide degradation experiments, -detailed guidance and helpful tools have been developed as detailed in -'Credits and historical remarks' below. +detailed guidance and various helpful tools have been developed as detailed in +'Credits and historical remarks' below. This package aims to provide a one +stop solution for degradation kinetics, addressing modellers that are willing +to, or even prefer to work with R. ## Usage @@ -50,16 +55,17 @@ version is found in the ['dev' subdirectory](https://pkgdown.jrwb.de/mkin/dev/). * Highly flexible model specification using [`mkinmod`](https://pkgdown.jrwb.de/mkin/reference/mkinmod.html), - including equilibrium reactions and using the single first-order - reversible binding (SFORB) model, which will automatically create - two latent state variables for the observed variable. + including equilibrium reactions and using the single first-order reversible + binding (SFORB) model, which will automatically create two state variables + for the observed variable. * Model solution (forward modelling) in the function [`mkinpredict`](https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html) is performed either using the analytical solution for the case of - parent only degradation, an eigenvalue based solution if only simple - first-order (SFO) or SFORB kinetics are used in the model, or - using a numeric solver from the `deSolve` package (default is `lsoda`). -* The usual one-sided t-test for significant difference from zero is nevertheless + parent only degradation or some simple models involving a single transformation + product, , an eigenvalue based solution if only simple first-order (SFO) or + SFORB kinetics are used in the model, or using a numeric solver from the + `deSolve` package (default is `lsoda`). +* The usual one-sided t-test for significant difference from zero is shown based on estimators for the untransformed parameters. * Summary and plotting functions. The `summary` of an `mkinfit` object is in fact a full report that should give enough information to be able to @@ -77,8 +83,8 @@ version is found in the ['dev' subdirectory](https://pkgdown.jrwb.de/mkin/dev/). function. A two-component error model similar to the one proposed by [Rocke and Lorenzato](https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html) can be selected using the argument `error_model = "tc"`. -* Model comparisons using the Akaike Information Criterion (AIC) are supported which - can also be used for non-constant variance. In such cases the FOCUS +* Model comparisons using the Akaike Information Criterion (AIC) are supported + which can also be used for non-constant variance. In such cases the FOCUS chi-squared error level is not meaningful. * By default, kinetic rate constants and kinetic formation fractions are transformed internally using @@ -92,21 +98,21 @@ version is found in the ['dev' subdirectory](https://pkgdown.jrwb.de/mkin/dev/). occur in a single experiment with a single defined radiolabel position. * When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite. Mathematically, the SFORB model - is equivalent to the DFOP model used by other tools for biphasic metabolite curves. - However, the SFORB model has the advantage that there is a mechanistic - interpretation of the model parameters. -* Nonlinear mixed-effects models can be created from fits of the same degradation - model to different datasets for the same compound by using the + is equivalent to the DFOP model. However, the SFORB model has the advantage + that there is a mechanistic interpretation of the model parameters. +* Nonlinear mixed-effects models (hierarchical models) can be created from fits + of the same degradation model to different datasets for the same compound by + using the [nlme.mmkin](https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html) and - [saem.mmkin](https://pkgdown.jrwb.de/mkin/reference/saem.html) and + [saem.mmkin](https://pkgdown.jrwb.de/mkin/reference/saem.html) methods. Note that the convergence of the nlme fits depends on the quality of the data. Convergence is better for simple models and data for many groups (e.g. soils). The saem method uses the `saemix` package as a backend. Analytical solutions suitable for use with this package have been implemented for parent only models and the most important models including one metabolite (SFO-SFO and DFOP-SFO). Fitting other models with `saem.mmkin`, while it makes use - of the compiled ODE models that mkin provides, has longer run times (at least - six minutes on my system). + of the compiled ODE models that mkin provides, has longer run times (from a couple + of minutes to more than an hour). ### Performance @@ -130,7 +136,9 @@ version is found in the ['dev' subdirectory](https://pkgdown.jrwb.de/mkin/dev/). There is a graphical user interface that may be useful. Please refer to its [documentation page](https://pkgdown.jrwb.de/gmkin/) -for installation instructions and a manual. +for installation instructions and a manual. It only supports +evaluations using (generalised) nonlinear regression, but +not simultaneous fits using nonlinear mixed-effects models. ## News @@ -187,13 +195,12 @@ license. Finally, there is [KineticEval](https://github.com/zhenglei-gao/KineticEval), which contains -a further development of the scripts used for KinGUII, so the different tools -will hopefully be able to learn from each other in the future as well. +some further development of the scripts used for KinGUII. Thanks to René Lehmann, formerly working at the Umweltbundesamt, for the nice -cooperation cooperation on parameter transformations, especially the isometric -log-ratio transformation that is now used for formation fractions in case -there are more than two transformation targets. +cooperation on parameter transformations, especially the isometric log-ratio +transformation that is now used for formation fractions in case there are more +than two transformation targets. Many inspirations for improvements of mkin resulted from doing kinetic evaluations of degradation data for my clients while working at Harlan Laboratories and @@ -212,9 +219,13 @@ to ModelMaker 4.0, 2014-2015) of the visual fit in the kinetic evaluation of degradation data, 2019-2020) - Project Number 146839 (Checking the feasibility of using mixed-effects models for the derivation of kinetic modelling parameters from degradation studies, 2020-2021) +- Project Number 173340 (Application of nonlinear hierarchical models to the + kinetic evaluation of chemical degradation data) + +Thanks to everyone involved for collaboration and support! Thanks are due also to Emmanuelle Comets, maintainer of the saemix package, for -the nice collaboration on using the SAEM algorithm and its implementation in +her interest and support for using the SAEM algorithm and its implementation in saemix for the evaluation of chemical degradation data. ## References diff --git a/_pkgdown.yml b/_pkgdown.yml index bb1ad052..5c67d88f 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -47,6 +47,7 @@ reference: - title: Mixed models desc: Create and work with nonlinear hierarchical models contents: + - hierarchical_kinetics - read_spreadsheet - nlme.mmkin - saem.mmkin @@ -67,7 +68,7 @@ reference: - parplot - title: Datasets and known results contents: - - focus_soil_moisture + - ds_mixed - D24_2014 - dimethenamid_2018 - FOCUS_2006_A @@ -82,6 +83,7 @@ reference: - synthetic_data_for_UBA_2014 - experimental_data_for_UBA_2019 - test_data_from_UBA_2014 + - focus_soil_moisture - mkinds - mkindsg - title: NAFTA guidance @@ -91,7 +93,7 @@ reference: - plot.nafta - title: Utility functions contents: - - tex_listing + - summary_listing - f_time_norm_focus - set_nd_nq - max_twa_parent @@ -133,31 +135,45 @@ navbar: title: ~ type: default left: - - text: Functions and data + - text: Reference href: reference/index.html - text: Articles menu: - text: Introduction to mkin href: articles/mkin.html + - text: "-------" + - text: Example evaluations with (generalised) nonlinear least squares - text: Example evaluation of FOCUS Example Dataset D href: articles/FOCUS_D.html - text: Example evaluation of FOCUS Laboratory Data L1 to L3 href: articles/FOCUS_L.html - - text: Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models + - text: Example evaluation of FOCUS Example Dataset Z + href: articles/web_only/FOCUS_Z.html + - text: "-------" + - text: Example evaluations with hierarchical models (nonlinear mixed-effects models) + - text: Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P + href: articles/prebuilt/2022_dmta_parent.html + - text: Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P + href: articles/prebuilt/2022_dmta_pathway.html + - text: Testing hierarchical pathway kinetics with residue data on cyantraniliprole + href: articles/prebuilt/2022_cyan_pathway.html + - text: Comparison of saemix and nlme evaluations of dimethenamid data from 2018 href: articles/web_only/dimethenamid_2018.html - text: Short demo of the multistart method href: articles/web_only/multistart.html + - text: "-------" + - text: Performance - text: Performance benefit by using compiled model definitions in mkin href: articles/web_only/compiled_models.html - - text: Example evaluation of FOCUS Example Dataset Z - href: articles/web_only/FOCUS_Z.html - - text: Calculation of time weighted average concentrations with mkin - href: articles/twa.html - - text: Example evaluation of NAFTA SOP Attachment examples - href: articles/web_only/NAFTA_examples.html - text: Benchmark timings for mkin href: articles/web_only/benchmarks.html - text: Benchmark timings for saem.mmkin href: articles/web_only/saem_benchmarks.html + - text: "-------" + - text: Miscellaneous + - text: Calculation of time weighted average concentrations with mkin + href: articles/twa.html + - text: Example evaluation of NAFTA SOP Attachment examples + href: articles/web_only/NAFTA_examples.html - text: News href: news/index.html diff --git a/data/ds_mixed.rda b/data/ds_mixed.rda Binary files differnew file mode 100644 index 00000000..cf9f6463 --- /dev/null +++ b/data/ds_mixed.rda diff --git a/docs/404.html b/docs/404.html index 89c41d21..8e7e8f45 100644 --- a/docs/404.html +++ b/docs/404.html @@ -32,7 +32,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="https://pkgdown.jrwb.de/mkin/index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.1</span> </span> </div> @@ -61,19 +61,25 @@ <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="https://pkgdown.jrwb.de/mkin/articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul> </li> diff --git a/docs/articles/FOCUS_D.html b/docs/articles/FOCUS_D.html index 39cf7e1a..3c8ad547 100644 --- a/docs/articles/FOCUS_D.html +++ b/docs/articles/FOCUS_D.html @@ -33,7 +33,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -62,19 +62,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul> </li> @@ -105,7 +111,7 @@ <h1 data-toc-skip>Example evaluation of FOCUS Example Dataset D</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">Last change 31 January 2019 (rebuilt 2022-05-18)</h4> + <h4 data-toc-skip class="date">Last change 31 January 2019 (rebuilt 2022-11-17)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/FOCUS_D.rmd" class="external-link"><code>vignettes/FOCUS_D.rmd</code></a></small> <div class="hidden name"><code>FOCUS_D.rmd</code></div> @@ -116,207 +122,207 @@ <p>This is just a very simple vignette showing how to fit a degradation model for a parent compound with one transformation product using <code>mkin</code>. After loading the library we look at the data. We have observed concentrations in the column named <code>value</code> at the times specified in column <code>time</code> for the two observed variables named <code>parent</code> and <code>m1</code>.</p> <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> -<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## name time value</span> -<span class="co">## 1 parent 0 99.46</span> -<span class="co">## 2 parent 0 102.04</span> -<span class="co">## 3 parent 1 93.50</span> -<span class="co">## 4 parent 1 92.50</span> -<span class="co">## 5 parent 3 63.23</span> -<span class="co">## 6 parent 3 68.99</span> -<span class="co">## 7 parent 7 52.32</span> -<span class="co">## 8 parent 7 55.13</span> -<span class="co">## 9 parent 14 27.27</span> -<span class="co">## 10 parent 14 26.64</span> -<span class="co">## 11 parent 21 11.50</span> -<span class="co">## 12 parent 21 11.64</span> -<span class="co">## 13 parent 35 2.85</span> -<span class="co">## 14 parent 35 2.91</span> -<span class="co">## 15 parent 50 0.69</span> -<span class="co">## 16 parent 50 0.63</span> -<span class="co">## 17 parent 75 0.05</span> -<span class="co">## 18 parent 75 0.06</span> -<span class="co">## 19 parent 100 NA</span> -<span class="co">## 20 parent 100 NA</span> -<span class="co">## 21 parent 120 NA</span> -<span class="co">## 22 parent 120 NA</span> -<span class="co">## 23 m1 0 0.00</span> -<span class="co">## 24 m1 0 0.00</span> -<span class="co">## 25 m1 1 4.84</span> -<span class="co">## 26 m1 1 5.64</span> -<span class="co">## 27 m1 3 12.91</span> -<span class="co">## 28 m1 3 12.96</span> -<span class="co">## 29 m1 7 22.97</span> -<span class="co">## 30 m1 7 24.47</span> -<span class="co">## 31 m1 14 41.69</span> -<span class="co">## 32 m1 14 33.21</span> -<span class="co">## 33 m1 21 44.37</span> -<span class="co">## 34 m1 21 46.44</span> -<span class="co">## 35 m1 35 41.22</span> -<span class="co">## 36 m1 35 37.95</span> -<span class="co">## 37 m1 50 41.19</span> -<span class="co">## 38 m1 50 40.01</span> -<span class="co">## 39 m1 75 40.09</span> -<span class="co">## 40 m1 75 33.85</span> -<span class="co">## 41 m1 100 31.04</span> -<span class="co">## 42 m1 100 33.13</span> -<span class="co">## 43 m1 120 25.15</span> -<span class="co">## 44 m1 120 33.31</span></code></pre> +<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## name time value</span></span> +<span><span class="co">## 1 parent 0 99.46</span></span> +<span><span class="co">## 2 parent 0 102.04</span></span> +<span><span class="co">## 3 parent 1 93.50</span></span> +<span><span class="co">## 4 parent 1 92.50</span></span> +<span><span class="co">## 5 parent 3 63.23</span></span> +<span><span class="co">## 6 parent 3 68.99</span></span> +<span><span class="co">## 7 parent 7 52.32</span></span> +<span><span class="co">## 8 parent 7 55.13</span></span> +<span><span class="co">## 9 parent 14 27.27</span></span> +<span><span class="co">## 10 parent 14 26.64</span></span> +<span><span class="co">## 11 parent 21 11.50</span></span> +<span><span class="co">## 12 parent 21 11.64</span></span> +<span><span class="co">## 13 parent 35 2.85</span></span> +<span><span class="co">## 14 parent 35 2.91</span></span> +<span><span class="co">## 15 parent 50 0.69</span></span> +<span><span class="co">## 16 parent 50 0.63</span></span> +<span><span class="co">## 17 parent 75 0.05</span></span> +<span><span class="co">## 18 parent 75 0.06</span></span> +<span><span class="co">## 19 parent 100 NA</span></span> +<span><span class="co">## 20 parent 100 NA</span></span> +<span><span class="co">## 21 parent 120 NA</span></span> +<span><span class="co">## 22 parent 120 NA</span></span> +<span><span class="co">## 23 m1 0 0.00</span></span> +<span><span class="co">## 24 m1 0 0.00</span></span> +<span><span class="co">## 25 m1 1 4.84</span></span> +<span><span class="co">## 26 m1 1 5.64</span></span> +<span><span class="co">## 27 m1 3 12.91</span></span> +<span><span class="co">## 28 m1 3 12.96</span></span> +<span><span class="co">## 29 m1 7 22.97</span></span> +<span><span class="co">## 30 m1 7 24.47</span></span> +<span><span class="co">## 31 m1 14 41.69</span></span> +<span><span class="co">## 32 m1 14 33.21</span></span> +<span><span class="co">## 33 m1 21 44.37</span></span> +<span><span class="co">## 34 m1 21 46.44</span></span> +<span><span class="co">## 35 m1 35 41.22</span></span> +<span><span class="co">## 36 m1 35 37.95</span></span> +<span><span class="co">## 37 m1 50 41.19</span></span> +<span><span class="co">## 38 m1 50 40.01</span></span> +<span><span class="co">## 39 m1 75 40.09</span></span> +<span><span class="co">## 40 m1 75 33.85</span></span> +<span><span class="co">## 41 m1 100 31.04</span></span> +<span><span class="co">## 42 m1 100 33.13</span></span> +<span><span class="co">## 43 m1 120 25.15</span></span> +<span><span class="co">## 44 m1 120 33.31</span></span></code></pre> <p>Next we specify the degradation model: The parent compound degrades with simple first-order kinetics (SFO) to one metabolite named m1, which also degrades with SFO kinetics.</p> <p>The call to mkinmod returns a degradation model. The differential equations represented in R code can be found in the character vector <code>$diffs</code> of the <code>mkinmod</code> object. If a C compiler (gcc) is installed and functional, the differential equation model will be compiled from auto-generated C code.</p> <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Temporary DLL for differentials generated and loaded</span></code></pre> +<code class="sourceCode R"><span><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">SFO_SFO</span><span class="op">$</span><span class="va">diffs</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## parent </span> -<span class="co">## "d_parent = - k_parent * parent" </span> -<span class="co">## m1 </span> -<span class="co">## "d_m1 = + f_parent_to_m1 * k_parent * parent - k_m1 * m1"</span></code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">SFO_SFO</span><span class="op">$</span><span class="va">diffs</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## parent </span></span> +<span><span class="co">## "d_parent = - k_parent * parent" </span></span> +<span><span class="co">## m1 </span></span> +<span><span class="co">## "d_m1 = + f_parent_to_m1 * k_parent * parent - k_m1 * m1"</span></span></code></pre> <p>We do the fitting without progress report (<code>quiet = TRUE</code>).</p> <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE): Observations with value</span> -<span class="co">## of zero were removed from the data</span></code></pre> +<code class="sourceCode R"><span><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE): Observations with value</span></span> +<span><span class="co">## of zero were removed from the data</span></span></code></pre> <p>A plot of the fit including a residual plot for both observed variables is obtained using the <code>plot_sep</code> method for <code>mkinfit</code> objects, which shows separate graphs for all compounds and their residuals.</p> <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">fit</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"bottomright"</span><span class="op">)</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">fit</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"bottomright"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_D_files/figure-html/plot-1.png" width="768"></p> <p>Confidence intervals for the parameter estimates are obtained using the <code>mkinparplot</code> function.</p> <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../reference/mkinparplot.html">mkinparplot</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../reference/mkinparplot.html">mkinparplot</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_D_files/figure-html/plot_2-1.png" width="768"></p> <p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p> <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## mkin version used for fitting: 1.1.0 </span> -<span class="co">## R version used for fitting: 4.2.0 </span> -<span class="co">## Date of fit: Wed May 18 20:42:29 2022 </span> -<span class="co">## Date of summary: Wed May 18 20:42:30 2022 </span> -<span class="co">## </span> -<span class="co">## Equations:</span> -<span class="co">## d_parent/dt = - k_parent * parent</span> -<span class="co">## d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1</span> -<span class="co">## </span> -<span class="co">## Model predictions using solution type analytical </span> -<span class="co">## </span> -<span class="co">## Fitted using 401 model solutions performed in 0.144 s</span> -<span class="co">## </span> -<span class="co">## Error model: Constant variance </span> -<span class="co">## </span> -<span class="co">## Error model algorithm: OLS </span> -<span class="co">## </span> -<span class="co">## Starting values for parameters to be optimised:</span> -<span class="co">## value type</span> -<span class="co">## parent_0 100.7500 state</span> -<span class="co">## k_parent 0.1000 deparm</span> -<span class="co">## k_m1 0.1001 deparm</span> -<span class="co">## f_parent_to_m1 0.5000 deparm</span> -<span class="co">## </span> -<span class="co">## Starting values for the transformed parameters actually optimised:</span> -<span class="co">## value lower upper</span> -<span class="co">## parent_0 100.750000 -Inf Inf</span> -<span class="co">## log_k_parent -2.302585 -Inf Inf</span> -<span class="co">## log_k_m1 -2.301586 -Inf Inf</span> -<span class="co">## f_parent_qlogis 0.000000 -Inf Inf</span> -<span class="co">## </span> -<span class="co">## Fixed parameter values:</span> -<span class="co">## value type</span> -<span class="co">## m1_0 0 state</span> -<span class="co">## </span> -<span class="co">## </span> -<span class="co">## Warning(s): </span> -<span class="co">## Observations with value of zero were removed from the data</span> -<span class="co">## </span> -<span class="co">## Results:</span> -<span class="co">## </span> -<span class="co">## AIC BIC logLik</span> -<span class="co">## 204.4486 212.6365 -97.22429</span> -<span class="co">## </span> -<span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span> -<span class="co">## Estimate Std. Error Lower Upper</span> -<span class="co">## parent_0 99.60000 1.57000 96.4000 102.8000</span> -<span class="co">## log_k_parent -2.31600 0.04087 -2.3990 -2.2330</span> -<span class="co">## log_k_m1 -5.24700 0.13320 -5.5180 -4.9770</span> -<span class="co">## f_parent_qlogis 0.05792 0.08926 -0.1237 0.2395</span> -<span class="co">## sigma 3.12600 0.35850 2.3960 3.8550</span> -<span class="co">## </span> -<span class="co">## Parameter correlation:</span> -<span class="co">## parent_0 log_k_parent log_k_m1 f_parent_qlogis sigma</span> -<span class="co">## parent_0 1.000e+00 5.174e-01 -1.688e-01 -5.471e-01 -1.174e-06</span> -<span class="co">## log_k_parent 5.174e-01 1.000e+00 -3.263e-01 -5.426e-01 -8.492e-07</span> -<span class="co">## log_k_m1 -1.688e-01 -3.263e-01 1.000e+00 7.478e-01 8.220e-07</span> -<span class="co">## f_parent_qlogis -5.471e-01 -5.426e-01 7.478e-01 1.000e+00 1.307e-06</span> -<span class="co">## sigma -1.174e-06 -8.492e-07 8.220e-07 1.307e-06 1.000e+00</span> -<span class="co">## </span> -<span class="co">## Backtransformed parameters:</span> -<span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span> -<span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span> -<span class="co">## for estimators of untransformed parameters.</span> -<span class="co">## Estimate t value Pr(>t) Lower Upper</span> -<span class="co">## parent_0 99.600000 63.430 2.298e-36 96.400000 1.028e+02</span> -<span class="co">## k_parent 0.098700 24.470 4.955e-23 0.090820 1.073e-01</span> -<span class="co">## k_m1 0.005261 7.510 6.165e-09 0.004012 6.898e-03</span> -<span class="co">## f_parent_to_m1 0.514500 23.070 3.104e-22 0.469100 5.596e-01</span> -<span class="co">## sigma 3.126000 8.718 2.235e-10 2.396000 3.855e+00</span> -<span class="co">## </span> -<span class="co">## FOCUS Chi2 error levels in percent:</span> -<span class="co">## err.min n.optim df</span> -<span class="co">## All data 6.398 4 15</span> -<span class="co">## parent 6.459 2 7</span> -<span class="co">## m1 4.690 2 8</span> -<span class="co">## </span> -<span class="co">## Resulting formation fractions:</span> -<span class="co">## ff</span> -<span class="co">## parent_m1 0.5145</span> -<span class="co">## parent_sink 0.4855</span> -<span class="co">## </span> -<span class="co">## Estimated disappearance times:</span> -<span class="co">## DT50 DT90</span> -<span class="co">## parent 7.023 23.33</span> -<span class="co">## m1 131.761 437.70</span> -<span class="co">## </span> -<span class="co">## Data:</span> -<span class="co">## time variable observed predicted residual</span> -<span class="co">## 0 parent 99.46 99.59848 -1.385e-01</span> -<span class="co">## 0 parent 102.04 99.59848 2.442e+00</span> -<span class="co">## 1 parent 93.50 90.23787 3.262e+00</span> -<span class="co">## 1 parent 92.50 90.23787 2.262e+00</span> -<span class="co">## 3 parent 63.23 74.07319 -1.084e+01</span> -<span class="co">## 3 parent 68.99 74.07319 -5.083e+00</span> -<span class="co">## 7 parent 52.32 49.91207 2.408e+00</span> -<span class="co">## 7 parent 55.13 49.91207 5.218e+00</span> -<span class="co">## 14 parent 27.27 25.01258 2.257e+00</span> -<span class="co">## 14 parent 26.64 25.01258 1.627e+00</span> -<span class="co">## 21 parent 11.50 12.53462 -1.035e+00</span> -<span class="co">## 21 parent 11.64 12.53462 -8.946e-01</span> -<span class="co">## 35 parent 2.85 3.14787 -2.979e-01</span> -<span class="co">## 35 parent 2.91 3.14787 -2.379e-01</span> -<span class="co">## 50 parent 0.69 0.71624 -2.624e-02</span> -<span class="co">## 50 parent 0.63 0.71624 -8.624e-02</span> -<span class="co">## 75 parent 0.05 0.06074 -1.074e-02</span> -<span class="co">## 75 parent 0.06 0.06074 -7.382e-04</span> -<span class="co">## 1 m1 4.84 4.80296 3.704e-02</span> -<span class="co">## 1 m1 5.64 4.80296 8.370e-01</span> -<span class="co">## 3 m1 12.91 13.02400 -1.140e-01</span> -<span class="co">## 3 m1 12.96 13.02400 -6.400e-02</span> -<span class="co">## 7 m1 22.97 25.04476 -2.075e+00</span> -<span class="co">## 7 m1 24.47 25.04476 -5.748e-01</span> -<span class="co">## 14 m1 41.69 36.69003 5.000e+00</span> -<span class="co">## 14 m1 33.21 36.69003 -3.480e+00</span> -<span class="co">## 21 m1 44.37 41.65310 2.717e+00</span> -<span class="co">## 21 m1 46.44 41.65310 4.787e+00</span> -<span class="co">## 35 m1 41.22 43.31313 -2.093e+00</span> -<span class="co">## 35 m1 37.95 43.31313 -5.363e+00</span> -<span class="co">## 50 m1 41.19 41.21832 -2.832e-02</span> -<span class="co">## 50 m1 40.01 41.21832 -1.208e+00</span> -<span class="co">## 75 m1 40.09 36.44704 3.643e+00</span> -<span class="co">## 75 m1 33.85 36.44704 -2.597e+00</span> -<span class="co">## 100 m1 31.04 31.98162 -9.416e-01</span> -<span class="co">## 100 m1 33.13 31.98162 1.148e+00</span> -<span class="co">## 120 m1 25.15 28.78984 -3.640e+00</span> -<span class="co">## 120 m1 33.31 28.78984 4.520e+00</span></code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.0 </span></span> +<span><span class="co">## R version used for fitting: 4.2.2 </span></span> +<span><span class="co">## Date of fit: Thu Nov 17 14:04:21 2022 </span></span> +<span><span class="co">## Date of summary: Thu Nov 17 14:04:21 2022 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Equations:</span></span> +<span><span class="co">## d_parent/dt = - k_parent * parent</span></span> +<span><span class="co">## d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Model predictions using solution type analytical </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Fitted using 401 model solutions performed in 0.154 s</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Error model: Constant variance </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Error model algorithm: OLS </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Starting values for parameters to be optimised:</span></span> +<span><span class="co">## value type</span></span> +<span><span class="co">## parent_0 100.7500 state</span></span> +<span><span class="co">## k_parent 0.1000 deparm</span></span> +<span><span class="co">## k_m1 0.1001 deparm</span></span> +<span><span class="co">## f_parent_to_m1 0.5000 deparm</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Starting values for the transformed parameters actually optimised:</span></span> +<span><span class="co">## value lower upper</span></span> +<span><span class="co">## parent_0 100.750000 -Inf Inf</span></span> +<span><span class="co">## log_k_parent -2.302585 -Inf Inf</span></span> +<span><span class="co">## log_k_m1 -2.301586 -Inf Inf</span></span> +<span><span class="co">## f_parent_qlogis 0.000000 -Inf Inf</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Fixed parameter values:</span></span> +<span><span class="co">## value type</span></span> +<span><span class="co">## m1_0 0 state</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Warning(s): </span></span> +<span><span class="co">## Observations with value of zero were removed from the data</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Results:</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## AIC BIC logLik</span></span> +<span><span class="co">## 204.4486 212.6365 -97.22429</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span></span> +<span><span class="co">## Estimate Std. Error Lower Upper</span></span> +<span><span class="co">## parent_0 99.60000 1.57000 96.4000 102.8000</span></span> +<span><span class="co">## log_k_parent -2.31600 0.04087 -2.3990 -2.2330</span></span> +<span><span class="co">## log_k_m1 -5.24700 0.13320 -5.5180 -4.9770</span></span> +<span><span class="co">## f_parent_qlogis 0.05792 0.08926 -0.1237 0.2395</span></span> +<span><span class="co">## sigma 3.12600 0.35850 2.3960 3.8550</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameter correlation:</span></span> +<span><span class="co">## parent_0 log_k_parent log_k_m1 f_parent_qlogis sigma</span></span> +<span><span class="co">## parent_0 1.000e+00 5.174e-01 -1.688e-01 -5.471e-01 -1.172e-06</span></span> +<span><span class="co">## log_k_parent 5.174e-01 1.000e+00 -3.263e-01 -5.426e-01 -8.483e-07</span></span> +<span><span class="co">## log_k_m1 -1.688e-01 -3.263e-01 1.000e+00 7.478e-01 8.205e-07</span></span> +<span><span class="co">## f_parent_qlogis -5.471e-01 -5.426e-01 7.478e-01 1.000e+00 1.305e-06</span></span> +<span><span class="co">## sigma -1.172e-06 -8.483e-07 8.205e-07 1.305e-06 1.000e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Backtransformed parameters:</span></span> +<span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span> +<span><span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span></span> +<span><span class="co">## for estimators of untransformed parameters.</span></span> +<span><span class="co">## Estimate t value Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 99.600000 63.430 2.298e-36 96.400000 1.028e+02</span></span> +<span><span class="co">## k_parent 0.098700 24.470 4.955e-23 0.090820 1.073e-01</span></span> +<span><span class="co">## k_m1 0.005261 7.510 6.165e-09 0.004012 6.898e-03</span></span> +<span><span class="co">## f_parent_to_m1 0.514500 23.070 3.104e-22 0.469100 5.596e-01</span></span> +<span><span class="co">## sigma 3.126000 8.718 2.235e-10 2.396000 3.855e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## FOCUS Chi2 error levels in percent:</span></span> +<span><span class="co">## err.min n.optim df</span></span> +<span><span class="co">## All data 6.398 4 15</span></span> +<span><span class="co">## parent 6.459 2 7</span></span> +<span><span class="co">## m1 4.690 2 8</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Resulting formation fractions:</span></span> +<span><span class="co">## ff</span></span> +<span><span class="co">## parent_m1 0.5145</span></span> +<span><span class="co">## parent_sink 0.4855</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Estimated disappearance times:</span></span> +<span><span class="co">## DT50 DT90</span></span> +<span><span class="co">## parent 7.023 23.33</span></span> +<span><span class="co">## m1 131.761 437.70</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Data:</span></span> +<span><span class="co">## time variable observed predicted residual</span></span> +<span><span class="co">## 0 parent 99.46 99.59848 -1.385e-01</span></span> +<span><span class="co">## 0 parent 102.04 99.59848 2.442e+00</span></span> +<span><span class="co">## 1 parent 93.50 90.23787 3.262e+00</span></span> +<span><span class="co">## 1 parent 92.50 90.23787 2.262e+00</span></span> +<span><span class="co">## 3 parent 63.23 74.07319 -1.084e+01</span></span> +<span><span class="co">## 3 parent 68.99 74.07319 -5.083e+00</span></span> +<span><span class="co">## 7 parent 52.32 49.91207 2.408e+00</span></span> +<span><span class="co">## 7 parent 55.13 49.91207 5.218e+00</span></span> +<span><span class="co">## 14 parent 27.27 25.01258 2.257e+00</span></span> +<span><span class="co">## 14 parent 26.64 25.01258 1.627e+00</span></span> +<span><span class="co">## 21 parent 11.50 12.53462 -1.035e+00</span></span> +<span><span class="co">## 21 parent 11.64 12.53462 -8.946e-01</span></span> +<span><span class="co">## 35 parent 2.85 3.14787 -2.979e-01</span></span> +<span><span class="co">## 35 parent 2.91 3.14787 -2.379e-01</span></span> +<span><span class="co">## 50 parent 0.69 0.71624 -2.624e-02</span></span> +<span><span class="co">## 50 parent 0.63 0.71624 -8.624e-02</span></span> +<span><span class="co">## 75 parent 0.05 0.06074 -1.074e-02</span></span> +<span><span class="co">## 75 parent 0.06 0.06074 -7.382e-04</span></span> +<span><span class="co">## 1 m1 4.84 4.80296 3.704e-02</span></span> +<span><span class="co">## 1 m1 5.64 4.80296 8.370e-01</span></span> +<span><span class="co">## 3 m1 12.91 13.02400 -1.140e-01</span></span> +<span><span class="co">## 3 m1 12.96 13.02400 -6.400e-02</span></span> +<span><span class="co">## 7 m1 22.97 25.04476 -2.075e+00</span></span> +<span><span class="co">## 7 m1 24.47 25.04476 -5.748e-01</span></span> +<span><span class="co">## 14 m1 41.69 36.69003 5.000e+00</span></span> +<span><span class="co">## 14 m1 33.21 36.69003 -3.480e+00</span></span> +<span><span class="co">## 21 m1 44.37 41.65310 2.717e+00</span></span> +<span><span class="co">## 21 m1 46.44 41.65310 4.787e+00</span></span> +<span><span class="co">## 35 m1 41.22 43.31313 -2.093e+00</span></span> +<span><span class="co">## 35 m1 37.95 43.31313 -5.363e+00</span></span> +<span><span class="co">## 50 m1 41.19 41.21832 -2.832e-02</span></span> +<span><span class="co">## 50 m1 40.01 41.21832 -1.208e+00</span></span> +<span><span class="co">## 75 m1 40.09 36.44704 3.643e+00</span></span> +<span><span class="co">## 75 m1 33.85 36.44704 -2.597e+00</span></span> +<span><span class="co">## 100 m1 31.04 31.98162 -9.416e-01</span></span> +<span><span class="co">## 100 m1 33.13 31.98162 1.148e+00</span></span> +<span><span class="co">## 120 m1 25.15 28.78984 -3.640e+00</span></span> +<span><span class="co">## 120 m1 33.31 28.78984 4.520e+00</span></span></code></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> @@ -334,7 +340,7 @@ <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer> diff --git a/docs/articles/FOCUS_L.html b/docs/articles/FOCUS_L.html index 7d36c77c..f02febc4 100644 --- a/docs/articles/FOCUS_L.html +++ b/docs/articles/FOCUS_L.html @@ -33,7 +33,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.1</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -62,19 +62,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul> </li> @@ -105,7 +111,7 @@ <h1 data-toc-skip>Example evaluation of FOCUS Laboratory Data L1 to L3</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">Last change 18 May 2022 (rebuilt 2022-07-08)</h4> + <h4 data-toc-skip class="date">Last change 18 May 2022 (rebuilt 2022-11-17)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/FOCUS_L.rmd" class="external-link"><code>vignettes/FOCUS_L.rmd</code></a></small> <div class="hidden name"><code>FOCUS_L.rmd</code></div> @@ -130,18 +136,18 @@ <p>Since mkin version 0.9-32 (July 2014), we can use shorthand notation like <code>"SFO"</code> for parent only degradation models. The following two lines fit the model and produce the summary report of the model fit. This covers the numerical analysis given in the FOCUS report.</p> <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">m.L1.SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_L1_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L1.SFO</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.1.1 </span></span> -<span><span class="co">## R version used for fitting: 4.2.1 </span></span> -<span><span class="co">## Date of fit: Fri Jul 8 17:34:00 2022 </span></span> -<span><span class="co">## Date of summary: Fri Jul 8 17:34:00 2022 </span></span> +<span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L1.SFO</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.0 </span></span> +<span><span class="co">## R version used for fitting: 4.2.2 </span></span> +<span><span class="co">## Date of fit: Thu Nov 17 14:04:25 2022 </span></span> +<span><span class="co">## Date of summary: Thu Nov 17 14:04:25 2022 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - k_parent * parent</span></span> <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 133 model solutions performed in 0.028 s</span></span> +<span><span class="co">## Fitted using 133 model solutions performed in 0.033 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -173,9 +179,9 @@ <span><span class="co">## </span></span> <span><span class="co">## Parameter correlation:</span></span> <span><span class="co">## parent_0 log_k_parent sigma</span></span> -<span><span class="co">## parent_0 1.000e+00 6.186e-01 -1.712e-09</span></span> -<span><span class="co">## log_k_parent 6.186e-01 1.000e+00 -3.237e-09</span></span> -<span><span class="co">## sigma -1.712e-09 -3.237e-09 1.000e+00</span></span> +<span><span class="co">## parent_0 1.000e+00 6.186e-01 -1.516e-09</span></span> +<span><span class="co">## log_k_parent 6.186e-01 1.000e+00 -3.124e-09</span></span> +<span><span class="co">## sigma -1.516e-09 -3.124e-09 1.000e+00</span></span> <span><span class="co">## </span></span> <span><span class="co">## Backtransformed parameters:</span></span> <span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span> @@ -217,7 +223,7 @@ <span><span class="co">## 30 parent 4.0 5.251 -1.2513</span></span></code></pre> <p>A plot of the fit is obtained with the plot function for mkinfit objects.</p> <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L1.SFO</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>, main <span class="op">=</span> <span class="st">"FOCUS L1 - SFO"</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L1.SFO</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>, main <span class="op">=</span> <span class="st">"FOCUS L1 - SFO"</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-4-1.png" width="576"></p> <p>The residual plot can be easily obtained by</p> <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> @@ -225,26 +231,29 @@ <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-5-1.png" width="576"></p> <p>For comparison, the FOMC model is fitted as well, and the <span class="math inline">\(\chi^2\)</span> error level is checked.</p> <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="va">m.L1.FOMC</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_L1_mkin</span>, quiet<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span> -<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L1.FOMC</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>, main <span class="op">=</span> <span class="st">"FOCUS L1 - FOMC"</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="va">m.L1.FOMC</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_L1_mkin</span>, quiet<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation did not converge:</span></span> +<span><span class="co">## false convergence (8)</span></span></code></pre> +<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L1.FOMC</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>, main <span class="op">=</span> <span class="st">"FOCUS L1 - FOMC"</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-6-1.png" width="576"></p> -<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L1.FOMC</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> +<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L1.FOMC</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> <pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> <pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span></span> <span><span class="co">## doubtful</span></span></code></pre> -<pre><code><span><span class="co">## mkin version used for fitting: 1.1.1 </span></span> -<span><span class="co">## R version used for fitting: 4.2.1 </span></span> -<span><span class="co">## Date of fit: Fri Jul 8 17:34:00 2022 </span></span> -<span><span class="co">## Date of summary: Fri Jul 8 17:34:00 2022 </span></span> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.0 </span></span> +<span><span class="co">## R version used for fitting: 4.2.2 </span></span> +<span><span class="co">## Date of fit: Thu Nov 17 14:04:25 2022 </span></span> +<span><span class="co">## Date of summary: Thu Nov 17 14:04:25 2022 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span></span> <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 357 model solutions performed in 0.07 s</span></span> +<span><span class="co">## Fitted using 369 model solutions performed in 0.08 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -265,34 +274,39 @@ <span><span class="co">## Fixed parameter values:</span></span> <span><span class="co">## None</span></span> <span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Warning(s): </span></span> +<span><span class="co">## Optimisation did not converge:</span></span> +<span><span class="co">## false convergence (8)</span></span> +<span><span class="co">## </span></span> <span><span class="co">## Results:</span></span> <span><span class="co">## </span></span> -<span><span class="co">## AIC BIC logLik</span></span> -<span><span class="co">## 95.88804 99.44953 -43.94402</span></span> +<span><span class="co">## AIC BIC logLik</span></span> +<span><span class="co">## 95.88781 99.44929 -43.9439</span></span> <span><span class="co">## </span></span> <span><span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span></span> <span><span class="co">## Estimate Std. Error Lower Upper</span></span> <span><span class="co">## parent_0 92.47 1.2820 89.720 95.220</span></span> -<span><span class="co">## log_alpha 11.37 NaN NaN NaN</span></span> -<span><span class="co">## log_beta 13.72 NaN NaN NaN</span></span> -<span><span class="co">## sigma 2.78 0.4621 1.789 3.771</span></span> +<span><span class="co">## log_alpha 13.78 NaN NaN NaN</span></span> +<span><span class="co">## log_beta 16.13 NaN NaN NaN</span></span> +<span><span class="co">## sigma 2.78 0.4598 1.794 3.766</span></span> <span><span class="co">## </span></span> <span><span class="co">## Parameter correlation:</span></span> <span><span class="co">## parent_0 log_alpha log_beta sigma</span></span> -<span><span class="co">## parent_0 1.0000000 NaN NaN 0.0005548</span></span> +<span><span class="co">## parent_0 1.0000000 NaN NaN 0.0001671</span></span> <span><span class="co">## log_alpha NaN 1 NaN NaN</span></span> <span><span class="co">## log_beta NaN NaN 1 NaN</span></span> -<span><span class="co">## sigma 0.0005548 NaN NaN 1.0000000</span></span> +<span><span class="co">## sigma 0.0001671 NaN NaN 1.0000000</span></span> <span><span class="co">## </span></span> <span><span class="co">## Backtransformed parameters:</span></span> <span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span> <span><span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span></span> <span><span class="co">## for estimators of untransformed parameters.</span></span> <span><span class="co">## Estimate t value Pr(>t) Lower Upper</span></span> -<span><span class="co">## parent_0 92.47 NA NA 89.720 95.220</span></span> -<span><span class="co">## alpha 87110.00 NA NA NA NA</span></span> -<span><span class="co">## beta 911100.00 NA NA NA NA</span></span> -<span><span class="co">## sigma 2.78 NA NA 1.789 3.771</span></span> +<span><span class="co">## parent_0 9.247e+01 NA NA 89.720 95.220</span></span> +<span><span class="co">## alpha 9.658e+05 NA NA NA NA</span></span> +<span><span class="co">## beta 1.010e+07 NA NA NA NA</span></span> +<span><span class="co">## sigma 2.780e+00 NA NA 1.794 3.766</span></span> <span><span class="co">## </span></span> <span><span class="co">## FOCUS Chi2 error levels in percent:</span></span> <span><span class="co">## err.min n.optim df</span></span> @@ -300,8 +314,8 @@ <span><span class="co">## parent 3.619 3 6</span></span> <span><span class="co">## </span></span> <span><span class="co">## Estimated disappearance times:</span></span> -<span><span class="co">## DT50 DT90 DT50back</span></span> -<span><span class="co">## parent 7.249 24.08 7.249</span></span></code></pre> +<span><span class="co">## DT50 DT90 DT50back</span></span> +<span><span class="co">## parent 7.25 24.08 7.25</span></span></code></pre> <p>We get a warning that the default optimisation algorithm <code>Port</code> did not converge, which is an indication that the model is overparameterised, <em>i.e.</em> contains too many parameters that are ill-defined as a consequence.</p> <p>And in fact, due to the higher number of parameters, and the lower number of degrees of freedom of the fit, the <span class="math inline">\(\chi^2\)</span> error level is actually higher for the FOMC model (3.6%) than for the SFO model (3.4%). Additionally, the parameters <code>log_alpha</code> and <code>log_beta</code> internally fitted in the model have excessive confidence intervals, that span more than 25 orders of magnitude (!) when backtransformed to the scale of <code>alpha</code> and <code>beta</code>. Also, the t-test for significant difference from zero does not indicate such a significant difference, with p-values greater than 0.1, and finally, the parameter correlation of <code>log_alpha</code> and <code>log_beta</code> is 1.000, clearly indicating that the model is overparameterised.</p> <p>The <span class="math inline">\(\chi^2\)</span> error levels reported in Appendix 3 and Appendix 7 to the FOCUS kinetics report are rounded to integer percentages and partly deviate by one percentage point from the results calculated by mkin. The reason for this is not known. However, mkin gives the same <span class="math inline">\(\chi^2\)</span> error levels as the kinfit package and the calculation routines of the kinfit package have been extensively compared to the results obtained by the KinGUI software, as documented in the kinfit package vignette. KinGUI was the first widely used standard package in this field. Also, the calculation of <span class="math inline">\(\chi^2\)</span> error levels was compared with KinGUII, CAKE and DegKin manager in a project sponsored by the German Umweltbundesamt <span class="citation">(Ranke 2014)</span>.</p> @@ -310,7 +324,7 @@ <h2 id="laboratory-data-l2">Laboratory Data L2<a class="anchor" aria-label="anchor" href="#laboratory-data-l2"></a> </h2> <p>The following code defines example dataset L2 from the FOCUS kinetics report, p. 287:</p> -<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">FOCUS_2006_L2</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span> <span> t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span><span class="op">)</span>, each <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>,</span> <span> parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">96.1</span>, <span class="fl">91.8</span>, <span class="fl">41.4</span>, <span class="fl">38.7</span>,</span> @@ -321,9 +335,9 @@ <h3 id="sfo-fit-for-l2">SFO fit for L2<a class="anchor" aria-label="anchor" href="#sfo-fit-for-l2"></a> </h3> <p>Again, the SFO model is fitted and the result is plotted. The residual plot can be obtained simply by adding the argument <code>show_residuals</code> to the plot command.</p> -<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">m.L2.SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_L2_mkin</span>, quiet<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span> -<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L2.SFO</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L2.SFO</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>,</span> <span> main <span class="op">=</span> <span class="st">"FOCUS L2 - SFO"</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-8-1.png" width="672"></p> <p>The <span class="math inline">\(\chi^2\)</span> error level of 14% suggests that the model does not fit very well. This is also obvious from the plots of the fit, in which we have included the residual plot.</p> @@ -334,24 +348,24 @@ <h3 id="fomc-fit-for-l2">FOMC fit for L2<a class="anchor" aria-label="anchor" href="#fomc-fit-for-l2"></a> </h3> <p>For comparison, the FOMC model is fitted as well, and the <span class="math inline">\(\chi^2\)</span> error level is checked.</p> -<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">m.L2.FOMC</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_L2_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L2.FOMC</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L2.FOMC</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>,</span> <span> main <span class="op">=</span> <span class="st">"FOCUS L2 - FOMC"</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-9-1.png" width="672"></p> -<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L2.FOMC</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.1.1 </span></span> -<span><span class="co">## R version used for fitting: 4.2.1 </span></span> -<span><span class="co">## Date of fit: Fri Jul 8 17:34:01 2022 </span></span> -<span><span class="co">## Date of summary: Fri Jul 8 17:34:01 2022 </span></span> +<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L2.FOMC</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.0 </span></span> +<span><span class="co">## R version used for fitting: 4.2.2 </span></span> +<span><span class="co">## Date of fit: Thu Nov 17 14:04:26 2022 </span></span> +<span><span class="co">## Date of summary: Thu Nov 17 14:04:26 2022 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span></span> <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 239 model solutions performed in 0.044 s</span></span> +<span><span class="co">## Fitted using 239 model solutions performed in 0.048 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -386,10 +400,10 @@ <span><span class="co">## </span></span> <span><span class="co">## Parameter correlation:</span></span> <span><span class="co">## parent_0 log_alpha log_beta sigma</span></span> -<span><span class="co">## parent_0 1.000e+00 -1.151e-01 -2.085e-01 -7.637e-09</span></span> -<span><span class="co">## log_alpha -1.151e-01 1.000e+00 9.741e-01 -1.617e-07</span></span> -<span><span class="co">## log_beta -2.085e-01 9.741e-01 1.000e+00 -1.387e-07</span></span> -<span><span class="co">## sigma -7.637e-09 -1.617e-07 -1.387e-07 1.000e+00</span></span> +<span><span class="co">## parent_0 1.000e+00 -1.151e-01 -2.085e-01 -7.828e-09</span></span> +<span><span class="co">## log_alpha -1.151e-01 1.000e+00 9.741e-01 -1.602e-07</span></span> +<span><span class="co">## log_beta -2.085e-01 9.741e-01 1.000e+00 -1.372e-07</span></span> +<span><span class="co">## sigma -7.828e-09 -1.602e-07 -1.372e-07 1.000e+00</span></span> <span><span class="co">## </span></span> <span><span class="co">## Backtransformed parameters:</span></span> <span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span> @@ -415,17 +429,17 @@ <h3 id="dfop-fit-for-l2">DFOP fit for L2<a class="anchor" aria-label="anchor" href="#dfop-fit-for-l2"></a> </h3> <p>Fitting the four parameter DFOP model further reduces the <span class="math inline">\(\chi^2\)</span> error level.</p> -<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">m.L2.DFOP</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">FOCUS_2006_L2_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L2.DFOP</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L2.DFOP</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>,</span> <span> main <span class="op">=</span> <span class="st">"FOCUS L2 - DFOP"</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-10-1.png" width="672"></p> -<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L2.DFOP</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.1.1 </span></span> -<span><span class="co">## R version used for fitting: 4.2.1 </span></span> -<span><span class="co">## Date of fit: Fri Jul 8 17:34:01 2022 </span></span> -<span><span class="co">## Date of summary: Fri Jul 8 17:34:01 2022 </span></span> +<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L2.DFOP</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.0 </span></span> +<span><span class="co">## R version used for fitting: 4.2.2 </span></span> +<span><span class="co">## Date of fit: Thu Nov 17 14:04:27 2022 </span></span> +<span><span class="co">## Date of summary: Thu Nov 17 14:04:27 2022 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span></span> @@ -434,7 +448,7 @@ <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 581 model solutions performed in 0.119 s</span></span> +<span><span class="co">## Fitted using 581 model solutions performed in 0.128 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -465,18 +479,18 @@ <span><span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span></span> <span><span class="co">## Estimate Std. Error Lower Upper</span></span> <span><span class="co">## parent_0 93.950 9.998e-01 91.5900 96.3100</span></span> -<span><span class="co">## log_k1 3.113 1.845e+03 -4360.0000 4367.0000</span></span> +<span><span class="co">## log_k1 3.112 1.842e+03 -4353.0000 4359.0000</span></span> <span><span class="co">## log_k2 -1.088 6.285e-02 -1.2370 -0.9394</span></span> <span><span class="co">## g_qlogis -0.399 9.946e-02 -0.6342 -0.1638</span></span> <span><span class="co">## sigma 1.414 2.886e-01 0.7314 2.0960</span></span> <span><span class="co">## </span></span> <span><span class="co">## Parameter correlation:</span></span> <span><span class="co">## parent_0 log_k1 log_k2 g_qlogis sigma</span></span> -<span><span class="co">## parent_0 1.000e+00 6.784e-07 -5.188e-10 2.665e-01 -5.800e-10</span></span> -<span><span class="co">## log_k1 6.784e-07 1.000e+00 1.114e-04 -2.191e-04 -1.029e-05</span></span> -<span><span class="co">## log_k2 -5.188e-10 1.114e-04 1.000e+00 -7.903e-01 5.080e-09</span></span> -<span><span class="co">## g_qlogis 2.665e-01 -2.191e-04 -7.903e-01 1.000e+00 -7.991e-09</span></span> -<span><span class="co">## sigma -5.800e-10 -1.029e-05 5.080e-09 -7.991e-09 1.000e+00</span></span> +<span><span class="co">## parent_0 1.000e+00 6.783e-07 -3.390e-10 2.665e-01 -2.967e-10</span></span> +<span><span class="co">## log_k1 6.783e-07 1.000e+00 1.116e-04 -2.196e-04 -1.031e-05</span></span> +<span><span class="co">## log_k2 -3.390e-10 1.116e-04 1.000e+00 -7.903e-01 2.917e-09</span></span> +<span><span class="co">## g_qlogis 2.665e-01 -2.196e-04 -7.903e-01 1.000e+00 -4.408e-09</span></span> +<span><span class="co">## sigma -2.967e-10 -1.031e-05 2.917e-09 -4.408e-09 1.000e+00</span></span> <span><span class="co">## </span></span> <span><span class="co">## Backtransformed parameters:</span></span> <span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span> @@ -484,7 +498,7 @@ <span><span class="co">## for estimators of untransformed parameters.</span></span> <span><span class="co">## Estimate t value Pr(>t) Lower Upper</span></span> <span><span class="co">## parent_0 93.9500 9.397e+01 2.036e-12 91.5900 96.3100</span></span> -<span><span class="co">## k1 22.4800 5.544e-04 4.998e-01 0.0000 Inf</span></span> +<span><span class="co">## k1 22.4800 5.553e-04 4.998e-01 0.0000 Inf</span></span> <span><span class="co">## k2 0.3369 1.591e+01 4.697e-07 0.2904 0.3909</span></span> <span><span class="co">## g 0.4016 1.680e+01 3.238e-07 0.3466 0.4591</span></span> <span><span class="co">## sigma 1.4140 4.899e+00 8.776e-04 0.7314 2.0960</span></span> @@ -496,7 +510,7 @@ <span><span class="co">## </span></span> <span><span class="co">## Estimated disappearance times:</span></span> <span><span class="co">## DT50 DT90 DT50back DT50_k1 DT50_k2</span></span> -<span><span class="co">## parent 0.5335 5.311 1.599 0.03083 2.058</span></span></code></pre> +<span><span class="co">## parent 0.5335 5.311 1.599 0.03084 2.058</span></span></code></pre> <p>Here, the DFOP model is clearly the best-fit model for dataset L2 based on the chi^2 error level criterion.</p> </div> </div> @@ -504,7 +518,7 @@ <h2 id="laboratory-data-l3">Laboratory Data L3<a class="anchor" aria-label="anchor" href="#laboratory-data-l3"></a> </h2> <p>The following code defines example dataset L3 from the FOCUS kinetics report, p. 290.</p> -<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">FOCUS_2006_L3</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span> <span> t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">30</span>, <span class="fl">60</span>, <span class="fl">91</span>, <span class="fl">120</span><span class="op">)</span>,</span> <span> parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">97.8</span>, <span class="fl">60</span>, <span class="fl">51</span>, <span class="fl">43</span>, <span class="fl">35</span>, <span class="fl">22</span>, <span class="fl">15</span>, <span class="fl">12</span><span class="op">)</span><span class="op">)</span></span> @@ -513,11 +527,11 @@ <h3 id="fit-multiple-models">Fit multiple models<a class="anchor" aria-label="anchor" href="#fit-multiple-models"></a> </h3> <p>As of mkin version 0.9-39 (June 2015), we can fit several models to one or more datasets in one call to the function <code>mmkin</code>. The datasets have to be passed in a list, in this case a named list holding only the L3 dataset prepared above.</p> -<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="co"># Only use one core here, not to offend the CRAN checks</span></span> <span><span class="va">mm.L3</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">1</span>,</span> <span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS L3"</span> <span class="op">=</span> <span class="va">FOCUS_2006_L3_mkin</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">)</span></span></code></pre></div> +<span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-12-1.png" width="700"></p> <p>The <span class="math inline">\(\chi^2\)</span> error level of 21% as well as the plot suggest that the SFO model does not fit very well. The FOMC model performs better, with an error level at which the <span class="math inline">\(\chi^2\)</span> test passes of 7%. Fitting the four parameter DFOP model further reduces the <span class="math inline">\(\chi^2\)</span> error level considerably.</p> </div> @@ -526,12 +540,12 @@ </h3> <p>The objects returned by mmkin are arranged like a matrix, with models as a row index and datasets as a column index.</p> <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.</p> -<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.1.1 </span></span> -<span><span class="co">## R version used for fitting: 4.2.1 </span></span> -<span><span class="co">## Date of fit: Fri Jul 8 17:34:02 2022 </span></span> -<span><span class="co">## Date of summary: Fri Jul 8 17:34:02 2022 </span></span> +<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.0 </span></span> +<span><span class="co">## R version used for fitting: 4.2.2 </span></span> +<span><span class="co">## Date of fit: Thu Nov 17 14:04:27 2022 </span></span> +<span><span class="co">## Date of summary: Thu Nov 17 14:04:28 2022 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span></span> @@ -540,7 +554,7 @@ <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 376 model solutions performed in 0.072 s</span></span> +<span><span class="co">## Fitted using 376 model solutions performed in 0.078 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -578,11 +592,11 @@ <span><span class="co">## </span></span> <span><span class="co">## Parameter correlation:</span></span> <span><span class="co">## parent_0 log_k1 log_k2 g_qlogis sigma</span></span> -<span><span class="co">## parent_0 1.000e+00 1.732e-01 2.282e-02 4.009e-01 -9.632e-08</span></span> -<span><span class="co">## log_k1 1.732e-01 1.000e+00 4.945e-01 -5.809e-01 7.145e-07</span></span> -<span><span class="co">## log_k2 2.282e-02 4.945e-01 1.000e+00 -6.812e-01 1.021e-06</span></span> -<span><span class="co">## g_qlogis 4.009e-01 -5.809e-01 -6.812e-01 1.000e+00 -7.925e-07</span></span> -<span><span class="co">## sigma -9.632e-08 7.145e-07 1.021e-06 -7.925e-07 1.000e+00</span></span> +<span><span class="co">## parent_0 1.000e+00 1.732e-01 2.282e-02 4.009e-01 -9.664e-08</span></span> +<span><span class="co">## log_k1 1.732e-01 1.000e+00 4.945e-01 -5.809e-01 7.147e-07</span></span> +<span><span class="co">## log_k2 2.282e-02 4.945e-01 1.000e+00 -6.812e-01 1.022e-06</span></span> +<span><span class="co">## g_qlogis 4.009e-01 -5.809e-01 -6.812e-01 1.000e+00 -7.926e-07</span></span> +<span><span class="co">## sigma -9.664e-08 7.147e-07 1.022e-06 -7.926e-07 1.000e+00</span></span> <span><span class="co">## </span></span> <span><span class="co">## Backtransformed parameters:</span></span> <span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span> @@ -614,8 +628,8 @@ <span><span class="co">## 60 parent 22.0 23.26 -1.25919</span></span> <span><span class="co">## 91 parent 15.0 15.18 -0.18181</span></span> <span><span class="co">## 120 parent 12.0 10.19 1.81395</span></span></code></pre> -<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-13-1.png" width="700"></p> <p>Here, a look to the model plot, the confidence intervals of the parameters and the correlation matrix suggest that the parameter estimates are reliable, and the DFOP model can be used as the best-fit model based on the <span class="math inline">\(\chi^2\)</span> error level criterion for laboratory data L3.</p> <p>This is also an example where the standard t-test for the parameter <code>g_ilr</code> is misleading, as it tests for a significant difference from zero. In this case, zero appears to be the correct value for this parameter, and the confidence interval for the backtransformed parameter <code>g</code> is quite narrow.</p> @@ -625,33 +639,33 @@ <h2 id="laboratory-data-l4">Laboratory Data L4<a class="anchor" aria-label="anchor" href="#laboratory-data-l4"></a> </h2> <p>The following code defines example dataset L4 from the FOCUS kinetics report, p. 293:</p> -<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">FOCUS_2006_L4</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span> <span> t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">30</span>, <span class="fl">60</span>, <span class="fl">91</span>, <span class="fl">120</span><span class="op">)</span>,</span> <span> parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">96.6</span>, <span class="fl">96.3</span>, <span class="fl">94.3</span>, <span class="fl">88.8</span>, <span class="fl">74.9</span>, <span class="fl">59.9</span>, <span class="fl">53.5</span>, <span class="fl">49.0</span><span class="op">)</span><span class="op">)</span></span> <span><span class="va">FOCUS_2006_L4_mkin</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">FOCUS_2006_L4</span><span class="op">)</span></span></code></pre></div> <p>Fits of the SFO and FOMC models, plots and summaries are produced below:</p> -<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="co"># Only use one core here, not to offend the CRAN checks</span></span> <span><span class="va">mm.L4</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">1</span>,</span> <span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS L4"</span> <span class="op">=</span> <span class="va">FOCUS_2006_L4_mkin</span><span class="op">)</span>,</span> <span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">)</span></span></code></pre></div> +<span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-15-1.png" width="700"></p> <p>The <span class="math inline">\(\chi^2\)</span> error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the <span class="math inline">\(\chi^2\)</span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p> -<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">[[</span><span class="st">"SFO"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.1.1 </span></span> -<span><span class="co">## R version used for fitting: 4.2.1 </span></span> -<span><span class="co">## Date of fit: Fri Jul 8 17:34:02 2022 </span></span> -<span><span class="co">## Date of summary: Fri Jul 8 17:34:02 2022 </span></span> +<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">[[</span><span class="st">"SFO"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.0 </span></span> +<span><span class="co">## R version used for fitting: 4.2.2 </span></span> +<span><span class="co">## Date of fit: Thu Nov 17 14:04:28 2022 </span></span> +<span><span class="co">## Date of summary: Thu Nov 17 14:04:29 2022 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - k_parent * parent</span></span> <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 142 model solutions performed in 0.026 s</span></span> +<span><span class="co">## Fitted using 142 model solutions performed in 0.029 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -683,9 +697,9 @@ <span><span class="co">## </span></span> <span><span class="co">## Parameter correlation:</span></span> <span><span class="co">## parent_0 log_k_parent sigma</span></span> -<span><span class="co">## parent_0 1.000e+00 5.938e-01 3.440e-07</span></span> -<span><span class="co">## log_k_parent 5.938e-01 1.000e+00 5.885e-07</span></span> -<span><span class="co">## sigma 3.440e-07 5.885e-07 1.000e+00</span></span> +<span><span class="co">## parent_0 1.000e+00 5.938e-01 3.387e-07</span></span> +<span><span class="co">## log_k_parent 5.938e-01 1.000e+00 5.830e-07</span></span> +<span><span class="co">## sigma 3.387e-07 5.830e-07 1.000e+00</span></span> <span><span class="co">## </span></span> <span><span class="co">## Backtransformed parameters:</span></span> <span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span> @@ -704,19 +718,19 @@ <span><span class="co">## Estimated disappearance times:</span></span> <span><span class="co">## DT50 DT90</span></span> <span><span class="co">## parent 106 352</span></span></code></pre> -<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.1.1 </span></span> -<span><span class="co">## R version used for fitting: 4.2.1 </span></span> -<span><span class="co">## Date of fit: Fri Jul 8 17:34:02 2022 </span></span> -<span><span class="co">## Date of summary: Fri Jul 8 17:34:02 2022 </span></span> +<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.0 </span></span> +<span><span class="co">## R version used for fitting: 4.2.2 </span></span> +<span><span class="co">## Date of fit: Thu Nov 17 14:04:28 2022 </span></span> +<span><span class="co">## Date of summary: Thu Nov 17 14:04:29 2022 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span></span> <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 224 model solutions performed in 0.041 s</span></span> +<span><span class="co">## Fitted using 224 model solutions performed in 0.046 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -751,10 +765,10 @@ <span><span class="co">## </span></span> <span><span class="co">## Parameter correlation:</span></span> <span><span class="co">## parent_0 log_alpha log_beta sigma</span></span> -<span><span class="co">## parent_0 1.000e+00 -4.696e-01 -5.543e-01 -2.563e-07</span></span> -<span><span class="co">## log_alpha -4.696e-01 1.000e+00 9.889e-01 4.066e-08</span></span> -<span><span class="co">## log_beta -5.543e-01 9.889e-01 1.000e+00 6.818e-08</span></span> -<span><span class="co">## sigma -2.563e-07 4.066e-08 6.818e-08 1.000e+00</span></span> +<span><span class="co">## parent_0 1.000e+00 -4.696e-01 -5.543e-01 -2.468e-07</span></span> +<span><span class="co">## log_alpha -4.696e-01 1.000e+00 9.889e-01 2.478e-08</span></span> +<span><span class="co">## log_beta -5.543e-01 9.889e-01 1.000e+00 5.211e-08</span></span> +<span><span class="co">## sigma -2.468e-07 2.478e-08 5.211e-08 1.000e+00</span></span> <span><span class="co">## </span></span> <span><span class="co">## Backtransformed parameters:</span></span> <span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span> @@ -803,7 +817,7 @@ <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.5.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer> diff --git a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png Binary files differindex b56e91e1..b6130527 100644 --- a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png +++ b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png diff --git a/docs/articles/index.html b/docs/articles/index.html index c3a39708..f4f6d557 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.1</span> </span> </div> @@ -44,19 +44,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -102,6 +108,10 @@ <dd> </dd><dt><a href="web_only/dimethenamid_2018.html">Example evaluations of the dimethenamid data from 2018</a></dt> <dd> + </dd><dt><a href="web_only/multistart.html">Short demo of the multistart method</a></dt> + <dd> + </dd><dt><a href="web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></dt> + <dd> </dd></dl></div> </div> </div> diff --git a/docs/articles/mkin.html b/docs/articles/mkin.html index a32f4b41..da499501 100644 --- a/docs/articles/mkin.html +++ b/docs/articles/mkin.html @@ -33,7 +33,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -62,19 +62,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul> </li> @@ -105,7 +111,7 @@ <h1 data-toc-skip>Introduction to mkin</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">Last change 15 February 2021 (rebuilt 2022-05-18)</h4> + <h4 data-toc-skip class="date">Last change 15 February 2021 (rebuilt 2022-11-17)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/mkin.rmd" class="external-link"><code>vignettes/mkin.rmd</code></a></small> <div class="hidden name"><code>mkin.rmd</code></div> @@ -120,34 +126,34 @@ </h2> <p>In the regulatory evaluation of chemical substances like plant protection products (pesticides), biocides and other chemicals, degradation data play an important role. For the evaluation of pesticide degradation experiments, detailed guidance has been developed, based on nonlinear optimisation. The <code>R</code> add-on package <code>mkin</code> implements fitting some of the models recommended in this guidance from within R and calculates some statistical measures for data series within one or more compartments, for parent and metabolites.</p> <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="st"><a href="https://pkgdown.jrwb.de/mkin/">"mkin"</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> -<span class="co"># Define the kinetic model</span> -<span class="va">m_SFO_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M1"</span><span class="op">)</span>, - M1 <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M2"</span><span class="op">)</span>, - M2 <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, - use_of_ff <span class="op">=</span> <span class="st">"max"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> - - -<span class="co"># Produce model predictions using some arbitrary parameters</span> -<span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span> -<span class="va">d_SFO_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO_SFO_SFO</span>, - <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.03</span>, - f_parent_to_M1 <span class="op">=</span> <span class="fl">0.5</span>, k_M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">/</span><span class="fl">100</span>, - f_M1_to_M2 <span class="op">=</span> <span class="fl">0.9</span>, k_M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">/</span><span class="fl">50</span><span class="op">)</span>, - <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span>, M2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, - <span class="va">sampling_times</span><span class="op">)</span> - -<span class="co"># Generate a dataset by adding normally distributed errors with</span> -<span class="co"># standard deviation 3, for two replicates at each sampling time</span> -<span class="va">d_SFO_SFO_SFO_err</span> <span class="op"><-</span> <span class="fu"><a href="../reference/add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_SFO_SFO_SFO</span>, reps <span class="op">=</span> <span class="fl">2</span>, - sdfunc <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fl">3</span>, - n <span class="op">=</span> <span class="fl">1</span>, seed <span class="op">=</span> <span class="fl">123456789</span> <span class="op">)</span> - -<span class="co"># Fit the model to the dataset</span> -<span class="va">f_SFO_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_SFO_SFO_SFO</span>, <span class="va">d_SFO_SFO_SFO_err</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> - -<span class="co"># Plot the results separately for parent and metabolites</span> -<span class="fu"><a href="../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">f_SFO_SFO_SFO</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"bottomright"</span>, <span class="st">"bottomright"</span><span class="op">)</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="st"><a href="https://pkgdown.jrwb.de/mkin/">"mkin"</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span><span class="co"># Define the kinetic model</span></span> +<span><span class="va">m_SFO_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M1"</span><span class="op">)</span>,</span> +<span> M1 <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span> +<span> M2 <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> use_of_ff <span class="op">=</span> <span class="st">"max"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span></span> +<span></span> +<span><span class="co"># Produce model predictions using some arbitrary parameters</span></span> +<span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span> +<span><span class="va">d_SFO_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO_SFO_SFO</span>,</span> +<span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.03</span>,</span> +<span> f_parent_to_M1 <span class="op">=</span> <span class="fl">0.5</span>, k_M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">/</span><span class="fl">100</span>,</span> +<span> f_M1_to_M2 <span class="op">=</span> <span class="fl">0.9</span>, k_M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">/</span><span class="fl">50</span><span class="op">)</span>,</span> +<span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span>, M2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span> +<span> <span class="va">sampling_times</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># Generate a dataset by adding normally distributed errors with</span></span> +<span><span class="co"># standard deviation 3, for two replicates at each sampling time</span></span> +<span><span class="va">d_SFO_SFO_SFO_err</span> <span class="op"><-</span> <span class="fu"><a href="../reference/add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_SFO_SFO_SFO</span>, reps <span class="op">=</span> <span class="fl">2</span>,</span> +<span> sdfunc <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fl">3</span>,</span> +<span> n <span class="op">=</span> <span class="fl">1</span>, seed <span class="op">=</span> <span class="fl">123456789</span> <span class="op">)</span></span> +<span></span> +<span><span class="co"># Fit the model to the dataset</span></span> +<span><span class="va">f_SFO_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_SFO_SFO_SFO</span>, <span class="va">d_SFO_SFO_SFO_err</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># Plot the results separately for parent and metabolites</span></span> +<span><span class="fu"><a href="../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">f_SFO_SFO_SFO</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"bottomright"</span>, <span class="st">"bottomright"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> <p><img src="mkin_files/figure-html/unnamed-chunk-2-1.png" width="768"></p> </div> <div class="section level2"> @@ -264,7 +270,7 @@ <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer> diff --git a/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png b/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png Binary files differindex d1e7048d..63246387 100644 --- a/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png +++ b/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png diff --git a/docs/articles/twa.html b/docs/articles/twa.html index dad8ee44..41340e88 100644 --- a/docs/articles/twa.html +++ b/docs/articles/twa.html @@ -33,7 +33,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -62,19 +62,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul> </li> @@ -105,7 +111,7 @@ <h1 data-toc-skip>Calculation of time weighted average concentrations with mkin</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">Last change 18 September 2019 (rebuilt 2022-05-18)</h4> + <h4 data-toc-skip class="date">Last change 18 September 2019 (rebuilt 2022-11-17)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/twa.rmd" class="external-link"><code>vignettes/twa.rmd</code></a></small> <div class="hidden name"><code>twa.rmd</code></div> @@ -168,7 +174,7 @@ <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer> diff --git a/docs/articles/web_only/FOCUS_Z.html b/docs/articles/web_only/FOCUS_Z.html index 0dafb98a..ea20ecd9 100644 --- a/docs/articles/web_only/FOCUS_Z.html +++ b/docs/articles/web_only/FOCUS_Z.html @@ -33,7 +33,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -62,19 +62,25 @@ <a href="../../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul> </li> @@ -105,7 +111,7 @@ <h1 data-toc-skip>Example evaluation of FOCUS dataset Z</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">Last change 16 January 2018 (rebuilt 2022-05-18)</h4> + <h4 data-toc-skip class="date">Last change 16 January 2018 (rebuilt 2022-11-17)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/FOCUS_Z.rmd" class="external-link"><code>vignettes/web_only/FOCUS_Z.rmd</code></a></small> <div class="hidden name"><code>FOCUS_Z.rmd</code></div> @@ -120,88 +126,88 @@ </h2> <p>The following code defines the example dataset from Appendix 7 to the FOCUS kinetics report <span class="citation">(FOCUS Work Group on Degradation Kinetics 2014, 354)</span>.</p> <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> -<span class="va">LOD</span> <span class="op">=</span> <span class="fl">0.5</span> -<span class="va">FOCUS_2006_Z</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span> - t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">0.04</span>, <span class="fl">0.125</span>, <span class="fl">0.29</span>, <span class="fl">0.54</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">7</span>, <span class="fl">10</span>, <span class="fl">14</span>, <span class="fl">21</span>, - <span class="fl">42</span>, <span class="fl">61</span>, <span class="fl">96</span>, <span class="fl">124</span><span class="op">)</span>, - Z0 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">100</span>, <span class="fl">81.7</span>, <span class="fl">70.4</span>, <span class="fl">51.1</span>, <span class="fl">41.2</span>, <span class="fl">6.6</span>, <span class="fl">4.6</span>, <span class="fl">3.9</span>, <span class="fl">4.6</span>, <span class="fl">4.3</span>, <span class="fl">6.8</span>, - <span class="fl">2.9</span>, <span class="fl">3.5</span>, <span class="fl">5.3</span>, <span class="fl">4.4</span>, <span class="fl">1.2</span>, <span class="fl">0.7</span><span class="op">)</span>, - Z1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">18.3</span>, <span class="fl">29.6</span>, <span class="fl">46.3</span>, <span class="fl">55.1</span>, <span class="fl">65.7</span>, <span class="fl">39.1</span>, <span class="fl">36</span>, <span class="fl">15.3</span>, <span class="fl">5.6</span>, <span class="fl">1.1</span>, - <span class="fl">1.6</span>, <span class="fl">0.6</span>, <span class="fl">0.5</span> <span class="op">*</span> <span class="va">LOD</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="cn">NA</span><span class="op">)</span>, - Z2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="cn">NA</span>, <span class="fl">0.5</span> <span class="op">*</span> <span class="va">LOD</span>, <span class="fl">2.6</span>, <span class="fl">3.8</span>, <span class="fl">15.3</span>, <span class="fl">37.2</span>, <span class="fl">31.7</span>, <span class="fl">35.6</span>, <span class="fl">14.5</span>, - <span class="fl">0.8</span>, <span class="fl">2.1</span>, <span class="fl">1.9</span>, <span class="fl">0.5</span> <span class="op">*</span> <span class="va">LOD</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="cn">NA</span><span class="op">)</span>, - Z3 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="fl">0.5</span> <span class="op">*</span> <span class="va">LOD</span>, <span class="fl">9.2</span>, <span class="fl">13.1</span>, <span class="fl">22.3</span>, <span class="fl">28.4</span>, <span class="fl">32.5</span>, - <span class="fl">25.2</span>, <span class="fl">17.2</span>, <span class="fl">4.8</span>, <span class="fl">4.5</span>, <span class="fl">2.8</span>, <span class="fl">4.4</span><span class="op">)</span><span class="op">)</span> - -<span class="va">FOCUS_2006_Z_mkin</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">FOCUS_2006_Z</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span><span class="va">LOD</span> <span class="op">=</span> <span class="fl">0.5</span></span> +<span><span class="va">FOCUS_2006_Z</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span> +<span> t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">0.04</span>, <span class="fl">0.125</span>, <span class="fl">0.29</span>, <span class="fl">0.54</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">7</span>, <span class="fl">10</span>, <span class="fl">14</span>, <span class="fl">21</span>,</span> +<span> <span class="fl">42</span>, <span class="fl">61</span>, <span class="fl">96</span>, <span class="fl">124</span><span class="op">)</span>,</span> +<span> Z0 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">100</span>, <span class="fl">81.7</span>, <span class="fl">70.4</span>, <span class="fl">51.1</span>, <span class="fl">41.2</span>, <span class="fl">6.6</span>, <span class="fl">4.6</span>, <span class="fl">3.9</span>, <span class="fl">4.6</span>, <span class="fl">4.3</span>, <span class="fl">6.8</span>,</span> +<span> <span class="fl">2.9</span>, <span class="fl">3.5</span>, <span class="fl">5.3</span>, <span class="fl">4.4</span>, <span class="fl">1.2</span>, <span class="fl">0.7</span><span class="op">)</span>,</span> +<span> Z1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">18.3</span>, <span class="fl">29.6</span>, <span class="fl">46.3</span>, <span class="fl">55.1</span>, <span class="fl">65.7</span>, <span class="fl">39.1</span>, <span class="fl">36</span>, <span class="fl">15.3</span>, <span class="fl">5.6</span>, <span class="fl">1.1</span>,</span> +<span> <span class="fl">1.6</span>, <span class="fl">0.6</span>, <span class="fl">0.5</span> <span class="op">*</span> <span class="va">LOD</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="cn">NA</span><span class="op">)</span>,</span> +<span> Z2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="cn">NA</span>, <span class="fl">0.5</span> <span class="op">*</span> <span class="va">LOD</span>, <span class="fl">2.6</span>, <span class="fl">3.8</span>, <span class="fl">15.3</span>, <span class="fl">37.2</span>, <span class="fl">31.7</span>, <span class="fl">35.6</span>, <span class="fl">14.5</span>,</span> +<span> <span class="fl">0.8</span>, <span class="fl">2.1</span>, <span class="fl">1.9</span>, <span class="fl">0.5</span> <span class="op">*</span> <span class="va">LOD</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="cn">NA</span><span class="op">)</span>,</span> +<span> Z3 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="fl">0.5</span> <span class="op">*</span> <span class="va">LOD</span>, <span class="fl">9.2</span>, <span class="fl">13.1</span>, <span class="fl">22.3</span>, <span class="fl">28.4</span>, <span class="fl">32.5</span>,</span> +<span> <span class="fl">25.2</span>, <span class="fl">17.2</span>, <span class="fl">4.8</span>, <span class="fl">4.5</span>, <span class="fl">2.8</span>, <span class="fl">4.4</span><span class="op">)</span><span class="op">)</span></span> +<span></span> +<span><span class="va">FOCUS_2006_Z_mkin</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">FOCUS_2006_Z</span><span class="op">)</span></span></code></pre></div> </div> <div class="section level2"> <h2 id="parent-and-one-metabolite">Parent and one metabolite<a class="anchor" aria-label="anchor" href="#parent-and-one-metabolite"></a> </h2> <p>The next step is to set up the models used for the kinetic analysis. As the simultaneous fit of parent and the first metabolite is usually straightforward, Step 1 (SFO for parent only) is skipped here. We start with the model 2a, with formation and decline of metabolite Z1 and the pathway from parent directly to sink included (default in mkin).</p> <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">Z.2a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span><span class="op">)</span>, - Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Temporary DLL for differentials generated and loaded</span></code></pre> +<code class="sourceCode R"><span><span class="va">Z.2a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span><span class="op">)</span>,</span> +<span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">m.Z.2a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.2a</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Warning in mkinfit(Z.2a, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span> -<span class="co">## value of zero were removed from the data</span></code></pre> +<code class="sourceCode R"><span><span class="va">m.Z.2a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.2a</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit(Z.2a, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span></span> +<span><span class="co">## value of zero were removed from the data</span></span></code></pre> <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.2a</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.2a</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png" width="700"></p> <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.2a</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></code></pre></div> -<pre><code><span class="co">## Estimate se_notrans t value Pr(>t) Lower Upper</span> -<span class="co">## Z0_0 97.01488 3.301084 29.3888 3.2971e-21 91.66556 102.3642</span> -<span class="co">## k_Z0 2.23601 0.207078 10.7979 3.3309e-11 1.95303 2.5600</span> -<span class="co">## k_Z1 0.48212 0.063265 7.6207 2.8154e-08 0.40341 0.5762</span> -<span class="co">## f_Z0_to_Z1 1.00000 0.094764 10.5525 5.3560e-11 0.00000 1.0000</span> -<span class="co">## sigma 4.80411 0.635638 7.5579 3.2592e-08 3.52677 6.0815</span></code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.2a</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></code></pre></div> +<pre><code><span><span class="co">## Estimate se_notrans t value Pr(>t) Lower Upper</span></span> +<span><span class="co">## Z0_0 97.01488 3.301084 29.3888 3.2971e-21 91.66556 102.3642</span></span> +<span><span class="co">## k_Z0 2.23601 0.207078 10.7979 3.3309e-11 1.95303 2.5600</span></span> +<span><span class="co">## k_Z1 0.48212 0.063265 7.6207 2.8154e-08 0.40341 0.5762</span></span> +<span><span class="co">## f_Z0_to_Z1 1.00000 0.094764 10.5525 5.3560e-11 0.00000 1.0000</span></span> +<span><span class="co">## sigma 4.80411 0.635638 7.5579 3.2592e-08 3.52677 6.0815</span></span></code></pre> <p>As obvious from the parameter summary (the component of the summary), the kinetic rate constant from parent compound Z to sink is very small and the t-test for this parameter suggests that it is not significantly different from zero. This suggests, in agreement with the analysis in the FOCUS kinetics report, to simplify the model by removing the pathway to sink.</p> <p>A similar result can be obtained when formation fractions are used in the model formulation:</p> <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">Z.2a.ff</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span><span class="op">)</span>, - Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, - use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Temporary DLL for differentials generated and loaded</span></code></pre> +<code class="sourceCode R"><span><span class="va">Z.2a.ff</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span><span class="op">)</span>,</span> +<span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">m.Z.2a.ff</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.2a.ff</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Warning in mkinfit(Z.2a.ff, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span> -<span class="co">## value of zero were removed from the data</span></code></pre> +<code class="sourceCode R"><span><span class="va">m.Z.2a.ff</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.2a.ff</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit(Z.2a.ff, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span></span> +<span><span class="co">## value of zero were removed from the data</span></span></code></pre> <div class="sourceCode" id="cb13"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.2a.ff</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.2a.ff</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png" width="700"></p> <div class="sourceCode" id="cb14"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.2a.ff</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></code></pre></div> -<pre><code><span class="co">## Estimate se_notrans t value Pr(>t) Lower Upper</span> -<span class="co">## Z0_0 97.01488 3.301084 29.3888 3.2971e-21 91.66556 102.3642</span> -<span class="co">## k_Z0 2.23601 0.207078 10.7979 3.3309e-11 1.95303 2.5600</span> -<span class="co">## k_Z1 0.48212 0.063265 7.6207 2.8154e-08 0.40341 0.5762</span> -<span class="co">## f_Z0_to_Z1 1.00000 0.094764 10.5525 5.3560e-11 0.00000 1.0000</span> -<span class="co">## sigma 4.80411 0.635638 7.5579 3.2592e-08 3.52677 6.0815</span></code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.2a.ff</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></code></pre></div> +<pre><code><span><span class="co">## Estimate se_notrans t value Pr(>t) Lower Upper</span></span> +<span><span class="co">## Z0_0 97.01488 3.301084 29.3888 3.2971e-21 91.66556 102.3642</span></span> +<span><span class="co">## k_Z0 2.23601 0.207078 10.7979 3.3309e-11 1.95303 2.5600</span></span> +<span><span class="co">## k_Z1 0.48212 0.063265 7.6207 2.8154e-08 0.40341 0.5762</span></span> +<span><span class="co">## f_Z0_to_Z1 1.00000 0.094764 10.5525 5.3560e-11 0.00000 1.0000</span></span> +<span><span class="co">## sigma 4.80411 0.635638 7.5579 3.2592e-08 3.52677 6.0815</span></span></code></pre> <p>Here, the ilr transformed formation fraction fitted in the model takes a very large value, and the backtransformed formation fraction from parent Z to Z1 is practically unity. Here, the covariance matrix used for the calculation of confidence intervals is not returned as the model is overparameterised.</p> <p>A simplified model is obtained by removing the pathway to the sink. </p> <p>In the following, we use the parameterisation with formation fractions in order to be able to compare with the results in the FOCUS guidance, and as it makes it easier to use parameters obtained in a previous fit when adding a further metabolite.</p> <div class="sourceCode" id="cb16"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">Z.3</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Temporary DLL for differentials generated and loaded</span></code></pre> +<code class="sourceCode R"><span><span class="va">Z.3</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <div class="sourceCode" id="cb18"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">m.Z.3</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.3</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Warning in mkinfit(Z.3, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span> -<span class="co">## value of zero were removed from the data</span></code></pre> +<code class="sourceCode R"><span><span class="va">m.Z.3</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.3</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit(Z.3, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span></span> +<span><span class="co">## value of zero were removed from the data</span></span></code></pre> <div class="sourceCode" id="cb20"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.3</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.3</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png" width="700"></p> <div class="sourceCode" id="cb21"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.3</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></code></pre></div> -<pre><code><span class="co">## Estimate se_notrans t value Pr(>t) Lower Upper</span> -<span class="co">## Z0_0 97.01488 2.597342 37.352 2.0106e-24 91.67597 102.3538</span> -<span class="co">## k_Z0 2.23601 0.146904 15.221 9.1477e-15 1.95354 2.5593</span> -<span class="co">## k_Z1 0.48212 0.041727 11.554 4.8268e-12 0.40355 0.5760</span> -<span class="co">## sigma 4.80411 0.620208 7.746 1.6110e-08 3.52925 6.0790</span></code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.3</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></code></pre></div> +<pre><code><span><span class="co">## Estimate se_notrans t value Pr(>t) Lower Upper</span></span> +<span><span class="co">## Z0_0 97.01488 2.597342 37.352 2.0106e-24 91.67597 102.3538</span></span> +<span><span class="co">## k_Z0 2.23601 0.146904 15.221 9.1477e-15 1.95354 2.5593</span></span> +<span><span class="co">## k_Z1 0.48212 0.041727 11.554 4.8268e-12 0.40355 0.5760</span></span> +<span><span class="co">## sigma 4.80411 0.620208 7.746 1.6110e-08 3.52925 6.0790</span></span></code></pre> <p>As there is only one transformation product for Z0 and no pathway to sink, the formation fraction is internally fixed to unity.</p> </div> <div class="section level2"> @@ -209,56 +215,58 @@ </h2> <p>As suggested in the FOCUS report, the pathway to sink was removed for metabolite Z1 as well in the next step. While this step appears questionable on the basis of the above results, it is followed here for the purpose of comparison. Also, in the FOCUS report, it is assumed that there is additional empirical evidence that Z1 quickly and exclusively hydrolyses to Z2.</p> <div class="sourceCode" id="cb23"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">Z.5</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Temporary DLL for differentials generated and loaded</span></code></pre> +<code class="sourceCode R"><span><span class="va">Z.5</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <div class="sourceCode" id="cb25"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">m.Z.5</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.5</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Warning in mkinfit(Z.5, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span> -<span class="co">## value of zero were removed from the data</span></code></pre> +<code class="sourceCode R"><span><span class="va">m.Z.5</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.5</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit(Z.5, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span></span> +<span><span class="co">## value of zero were removed from the data</span></span></code></pre> <div class="sourceCode" id="cb27"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.5</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.5</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png" width="700"></p> <p>Finally, metabolite Z3 is added to the model. We use the optimised differential equation parameter values from the previous fit in order to accelerate the optimization.</p> <div class="sourceCode" id="cb28"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">Z.FOCUS</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z3"</span><span class="op">)</span>, - Z3 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, - use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Temporary DLL for differentials generated and loaded</span></code></pre> +<code class="sourceCode R"><span><span class="va">Z.FOCUS</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z3"</span><span class="op">)</span>,</span> +<span> Z3 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <div class="sourceCode" id="cb30"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">m.Z.FOCUS</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.FOCUS</span>, <span class="va">FOCUS_2006_Z_mkin</span>, - parms.ini <span class="op">=</span> <span class="va">m.Z.5</span><span class="op">$</span><span class="va">bparms.ode</span>, - quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Warning in mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, parms.ini = m.Z.5$bparms.ode, :</span> -<span class="co">## Observations with value of zero were removed from the data</span></code></pre> -<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.FOCUS</span><span class="op">)</span></code></pre></div> -<p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png" width="700"></p> +<code class="sourceCode R"><span><span class="va">m.Z.FOCUS</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.FOCUS</span>, <span class="va">FOCUS_2006_Z_mkin</span>,</span> +<span> parms.ini <span class="op">=</span> <span class="va">m.Z.5</span><span class="op">$</span><span class="va">bparms.ode</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, parms.ini = m.Z.5$bparms.ode, :</span></span> +<span><span class="co">## Observations with value of zero were removed from the data</span></span></code></pre> +<pre><code><span><span class="co">## Warning in mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, parms.ini = m.Z.5$bparms.ode, : Optimisation did not converge:</span></span> +<span><span class="co">## false convergence (8)</span></span></code></pre> <div class="sourceCode" id="cb33"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.FOCUS</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></code></pre></div> -<pre><code><span class="co">## Estimate se_notrans t value Pr(>t) Lower Upper</span> -<span class="co">## Z0_0 96.838397 1.994270 48.5583 4.0284e-42 92.826435 100.850359</span> -<span class="co">## k_Z0 2.215406 0.118459 18.7018 1.0416e-23 1.989466 2.467005</span> -<span class="co">## k_Z1 0.478300 0.028257 16.9267 6.2409e-22 0.424702 0.538662</span> -<span class="co">## k_Z2 0.451616 0.042137 10.7178 1.6305e-14 0.374328 0.544863</span> -<span class="co">## k_Z3 0.058693 0.015245 3.8499 1.7803e-04 0.034805 0.098976</span> -<span class="co">## f_Z2_to_Z3 0.471509 0.058352 8.0804 9.6622e-11 0.357739 0.588317</span> -<span class="co">## sigma 3.984431 0.383402 10.3923 4.5575e-14 3.213126 4.755736</span></code></pre> -<div class="sourceCode" id="cb35"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">m.Z.FOCUS</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## $ff</span> -<span class="co">## Z2_Z3 Z2_sink </span> -<span class="co">## 0.47151 0.52849 </span> -<span class="co">## </span> -<span class="co">## $distimes</span> -<span class="co">## DT50 DT90</span> -<span class="co">## Z0 0.31288 1.0394</span> -<span class="co">## Z1 1.44919 4.8141</span> -<span class="co">## Z2 1.53481 5.0985</span> -<span class="co">## Z3 11.80971 39.2310</span></code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.FOCUS</span><span class="op">)</span></span></code></pre></div> +<p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png" width="700"></p> +<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.FOCUS</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></code></pre></div> +<pre><code><span><span class="co">## Estimate se_notrans t value Pr(>t) Lower Upper</span></span> +<span><span class="co">## Z0_0 96.838822 1.994274 48.5584 4.0280e-42 92.826981 100.850664</span></span> +<span><span class="co">## k_Z0 2.215393 0.118458 18.7019 1.0413e-23 1.989456 2.466989</span></span> +<span><span class="co">## k_Z1 0.478305 0.028258 16.9266 6.2418e-22 0.424708 0.538666</span></span> +<span><span class="co">## k_Z2 0.451627 0.042139 10.7176 1.6314e-14 0.374339 0.544872</span></span> +<span><span class="co">## k_Z3 0.058692 0.015245 3.8499 1.7803e-04 0.034808 0.098965</span></span> +<span><span class="co">## f_Z2_to_Z3 0.471502 0.058351 8.0805 9.6608e-11 0.357769 0.588274</span></span> +<span><span class="co">## sigma 3.984431 0.383402 10.3923 4.5575e-14 3.213126 4.755736</span></span></code></pre> +<div class="sourceCode" id="cb36"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">m.Z.FOCUS</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## $ff</span></span> +<span><span class="co">## Z2_Z3 Z2_sink </span></span> +<span><span class="co">## 0.4715 0.5285 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $distimes</span></span> +<span><span class="co">## DT50 DT90</span></span> +<span><span class="co">## Z0 0.31288 1.0394</span></span> +<span><span class="co">## Z1 1.44917 4.8141</span></span> +<span><span class="co">## Z2 1.53478 5.0984</span></span> +<span><span class="co">## Z3 11.80986 39.2315</span></span></code></pre> <p>This fit corresponds to the final result chosen in Appendix 7 of the FOCUS report. Confidence intervals returned by mkin are based on internally transformed parameters, however.</p> </div> <div class="section level2"> @@ -266,107 +274,105 @@ </h2> <p>As the FOCUS report states, there is a certain tailing of the time course of metabolite Z3. Also, the time course of the parent compound is not fitted very well using the SFO model, as residues at a certain low level remain.</p> <p>Therefore, an additional model is offered here, using the single first-order reversible binding (SFORB) model for metabolite Z3. As expected, the <span class="math inline">\(\chi^2\)</span> error level is lower for metabolite Z3 using this model and the graphical fit for Z3 is improved. However, the covariance matrix is not returned.</p> -<div class="sourceCode" id="cb37"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">Z.mkin.1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z3"</span><span class="op">)</span>, - Z3 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span><span class="op">)</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Temporary DLL for differentials generated and loaded</span></code></pre> -<div class="sourceCode" id="cb39"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">m.Z.mkin.1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.1</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Warning in mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span> -<span class="co">## value of zero were removed from the data</span></code></pre> -<div class="sourceCode" id="cb41"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.1</span><span class="op">)</span></code></pre></div> -<p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png" width="700"></p> +<div class="sourceCode" id="cb38"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">Z.mkin.1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z3"</span><span class="op">)</span>,</span> +<span> Z3 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> +<div class="sourceCode" id="cb40"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">m.Z.mkin.1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.1</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span></span> +<span><span class="co">## value of zero were removed from the data</span></span></code></pre> <div class="sourceCode" id="cb42"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.mkin.1</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">cov.unscaled</span></code></pre></div> -<pre><code><span class="co">## NULL</span></code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.1</span><span class="op">)</span></span></code></pre></div> +<p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png" width="700"></p> +<div class="sourceCode" id="cb43"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.mkin.1</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">cov.unscaled</span></span></code></pre></div> +<pre><code><span><span class="co">## NULL</span></span></code></pre> <p>Therefore, a further stepwise model building is performed starting from the stage of parent and two metabolites, starting from the assumption that the model fit for the parent compound can be improved by using the SFORB model.</p> -<div class="sourceCode" id="cb44"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">Z.mkin.3</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Temporary DLL for differentials generated and loaded</span></code></pre> -<div class="sourceCode" id="cb46"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">m.Z.mkin.3</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.3</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Warning in mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span> -<span class="co">## value of zero were removed from the data</span></code></pre> -<pre><code><span class="co">## Warning in mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE): Optimisation did not converge:</span> -<span class="co">## false convergence (8)</span></code></pre> +<div class="sourceCode" id="cb45"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">Z.mkin.3</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> +<div class="sourceCode" id="cb47"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">m.Z.mkin.3</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.3</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span></span> +<span><span class="co">## value of zero were removed from the data</span></span></code></pre> <div class="sourceCode" id="cb49"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.3</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.3</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png" width="700"></p> <p>This results in a much better representation of the behaviour of the parent compound Z0.</p> <p>Finally, Z3 is added as well. These models appear overparameterised (no covariance matrix returned) if the sink for Z1 is left in the models.</p> <div class="sourceCode" id="cb50"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">Z.mkin.4</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z3"</span><span class="op">)</span>, - Z3 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Temporary DLL for differentials generated and loaded</span></code></pre> +<code class="sourceCode R"><span><span class="va">Z.mkin.4</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z3"</span><span class="op">)</span>,</span> +<span> Z3 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <div class="sourceCode" id="cb52"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">m.Z.mkin.4</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.4</span>, <span class="va">FOCUS_2006_Z_mkin</span>, - parms.ini <span class="op">=</span> <span class="va">m.Z.mkin.3</span><span class="op">$</span><span class="va">bparms.ode</span>, - quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Warning in mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin, parms.ini =</span> -<span class="co">## m.Z.mkin.3$bparms.ode, : Observations with value of zero were removed from the</span> -<span class="co">## data</span></code></pre> +<code class="sourceCode R"><span><span class="va">m.Z.mkin.4</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.4</span>, <span class="va">FOCUS_2006_Z_mkin</span>,</span> +<span> parms.ini <span class="op">=</span> <span class="va">m.Z.mkin.3</span><span class="op">$</span><span class="va">bparms.ode</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin, parms.ini =</span></span> +<span><span class="co">## m.Z.mkin.3$bparms.ode, : Observations with value of zero were removed from the</span></span> +<span><span class="co">## data</span></span></code></pre> <div class="sourceCode" id="cb54"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.4</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.4</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png" width="700"></p> <p>The error level of the fit, but especially of metabolite Z3, can be improved if the SFORB model is chosen for this metabolite, as this model is capable of representing the tailing of the metabolite decline phase.</p> <div class="sourceCode" id="cb55"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">Z.mkin.5</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z3"</span><span class="op">)</span>, - Z3 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span><span class="op">)</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Temporary DLL for differentials generated and loaded</span></code></pre> +<code class="sourceCode R"><span><span class="va">Z.mkin.5</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z3"</span><span class="op">)</span>,</span> +<span> Z3 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <div class="sourceCode" id="cb57"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">m.Z.mkin.5</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.5</span>, <span class="va">FOCUS_2006_Z_mkin</span>, - parms.ini <span class="op">=</span> <span class="va">m.Z.mkin.4</span><span class="op">$</span><span class="va">bparms.ode</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">4</span><span class="op">]</span>, - quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini =</span> -<span class="co">## m.Z.mkin.4$bparms.ode[1:4], : Observations with value of zero were removed from</span> -<span class="co">## the data</span></code></pre> +<code class="sourceCode R"><span><span class="va">m.Z.mkin.5</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.5</span>, <span class="va">FOCUS_2006_Z_mkin</span>,</span> +<span> parms.ini <span class="op">=</span> <span class="va">m.Z.mkin.4</span><span class="op">$</span><span class="va">bparms.ode</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">4</span><span class="op">]</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini =</span></span> +<span><span class="co">## m.Z.mkin.4$bparms.ode[1:4], : Observations with value of zero were removed from</span></span> +<span><span class="co">## the data</span></span></code></pre> <div class="sourceCode" id="cb59"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.5</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.5</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png" width="700"></p> <p>The summary view of the backtransformed parameters shows that we get no confidence intervals due to overparameterisation. As the optimized is excessively small, it seems reasonable to fix it to zero.</p> <div class="sourceCode" id="cb60"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">m.Z.mkin.5a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.5</span>, <span class="va">FOCUS_2006_Z_mkin</span>, - parms.ini <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">m.Z.mkin.5</span><span class="op">$</span><span class="va">bparms.ode</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span><span class="op">]</span>, - k_Z3_bound_free <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, - fixed_parms <span class="op">=</span> <span class="st">"k_Z3_bound_free"</span>, - quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini =</span> -<span class="co">## c(m.Z.mkin.5$bparms.ode[1:7], : Observations with value of zero were removed</span> -<span class="co">## from the data</span></code></pre> +<code class="sourceCode R"><span><span class="va">m.Z.mkin.5a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.5</span>, <span class="va">FOCUS_2006_Z_mkin</span>,</span> +<span> parms.ini <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">m.Z.mkin.5</span><span class="op">$</span><span class="va">bparms.ode</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span><span class="op">]</span>,</span> +<span> k_Z3_bound_free <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span> +<span> fixed_parms <span class="op">=</span> <span class="st">"k_Z3_bound_free"</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini =</span></span> +<span><span class="co">## c(m.Z.mkin.5$bparms.ode[1:7], : Observations with value of zero were removed</span></span> +<span><span class="co">## from the data</span></span></code></pre> <div class="sourceCode" id="cb62"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.5a</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.5a</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png" width="700"></p> <p>As expected, the residual plots for Z0 and Z3 are more random than in the case of the all SFO model for which they were shown above. In conclusion, the model is proposed as the best-fit model for the dataset from Appendix 7 of the FOCUS report.</p> <p>A graphical representation of the confidence intervals can finally be obtained.</p> <div class="sourceCode" id="cb63"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/mkinparplot.html">mkinparplot</a></span><span class="op">(</span><span class="va">m.Z.mkin.5a</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/mkinparplot.html">mkinparplot</a></span><span class="op">(</span><span class="va">m.Z.mkin.5a</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png" width="700"></p> <p>The endpoints obtained with this model are</p> <div class="sourceCode" id="cb64"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">m.Z.mkin.5a</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## $ff</span> -<span class="co">## Z0_free Z2_Z3 Z2_sink Z3_free </span> -<span class="co">## 1.00000 0.53656 0.46344 1.00000 </span> -<span class="co">## </span> -<span class="co">## $SFORB</span> -<span class="co">## Z0_b1 Z0_b2 Z3_b1 Z3_b2 </span> -<span class="co">## 2.4471376 0.0075126 0.0800073 0.0000000 </span> -<span class="co">## </span> -<span class="co">## $distimes</span> -<span class="co">## DT50 DT90 DT50back DT50_Z0_b1 DT50_Z0_b2 DT50_Z3_b1 DT50_Z3_b2</span> -<span class="co">## Z0 0.3043 1.1848 0.35666 0.28325 92.264 NA NA</span> -<span class="co">## Z1 1.5148 5.0320 NA NA NA NA NA</span> -<span class="co">## Z2 1.6414 5.4526 NA NA NA NA NA</span> -<span class="co">## Z3 NA NA NA NA NA 8.6636 Inf</span></code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">m.Z.mkin.5a</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## $ff</span></span> +<span><span class="co">## Z0_free Z2_Z3 Z2_sink Z3_free </span></span> +<span><span class="co">## 1.00000 0.53656 0.46344 1.00000 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $SFORB</span></span> +<span><span class="co">## Z0_b1 Z0_b2 Z0_g Z3_b1 Z3_b2 Z3_g </span></span> +<span><span class="co">## 2.4471322 0.0075125 0.9519862 0.0800069 0.0000000 0.9347820 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $distimes</span></span> +<span><span class="co">## DT50 DT90 DT50back DT50_Z0_b1 DT50_Z0_b2 DT50_Z3_b1 DT50_Z3_b2</span></span> +<span><span class="co">## Z0 0.3043 1.1848 0.35666 0.28325 92.266 NA NA</span></span> +<span><span class="co">## Z1 1.5148 5.0320 NA NA NA NA NA</span></span> +<span><span class="co">## Z2 1.6414 5.4526 NA NA NA NA NA</span></span> +<span><span class="co">## Z3 NA NA NA NA NA 8.6636 Inf</span></span></code></pre> <p>It is clear the degradation rate of Z3 towards the end of the experiment is very low as DT50_Z3_b2 (the second Eigenvalue of the system of two differential equations representing the SFORB system for Z3, corresponding to the slower rate constant of the DFOP model) is reported to be infinity. However, this appears to be a feature of the data.</p> </div> <div class="section level2"> @@ -398,7 +404,7 @@ <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer> diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png Binary files differindex 229bae82..bc6efaf7 100644 --- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png +++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png Binary files differindex e13ad9aa..55c1b645 100644 --- 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a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png Binary files differindex 77965455..d95cac25 100644 --- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png +++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png Binary files differindex 250d0df5..cb333a1c 100644 --- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png +++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png Binary files differindex 5a01c03e..db807f14 100644 --- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png +++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png diff --git a/docs/articles/web_only/NAFTA_examples.html b/docs/articles/web_only/NAFTA_examples.html index df1e06db..b8ec5059 100644 --- a/docs/articles/web_only/NAFTA_examples.html +++ b/docs/articles/web_only/NAFTA_examples.html @@ -33,7 +33,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -62,19 +62,25 @@ <a href="../../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul> </li> @@ -105,7 +111,7 @@ <h1 data-toc-skip>Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">26 February 2019 (rebuilt 2022-05-18)</h4> + <h4 data-toc-skip class="date">26 February 2019 (rebuilt 2022-11-17)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/NAFTA_examples.rmd" class="external-link"><code>vignettes/web_only/NAFTA_examples.rmd</code></a></small> <div class="hidden name"><code>NAFTA_examples.rmd</code></div> @@ -128,205 +134,205 @@ <h3 id="example-on-page-5-upper-panel">Example on page 5, upper panel<a class="anchor" aria-label="anchor" href="#example-on-page-5-upper-panel"></a> </h3> <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p5a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p5a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></code></pre> -<pre><code><span class="co">## The half-life obtained from the IORE model may be used</span></code></pre> +<code class="sourceCode R"><span><span class="va">p5a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p5a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p5a</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p5a</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p5a-1.png" width="700"></p> <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p5a</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Sums of squares:</span> -<span class="co">## SFO IORE DFOP </span> -<span class="co">## 465.21753 56.27506 32.06401 </span> -<span class="co">## </span> -<span class="co">## Critical sum of squares for checking the SFO model:</span> -<span class="co">## [1] 64.4304</span> -<span class="co">## </span> -<span class="co">## Parameters:</span> -<span class="co">## $SFO</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 95.8401 4.67e-21 92.245 99.4357</span> -<span class="co">## k_parent 0.0102 3.92e-12 0.009 0.0117</span> -<span class="co">## sigma 4.8230 3.81e-06 3.214 6.4318</span> -<span class="co">## </span> -<span class="co">## $IORE</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 1.01e+02 NA 9.91e+01 1.02e+02</span> -<span class="co">## k__iore_parent 1.54e-05 NA 4.08e-06 5.84e-05</span> -<span class="co">## N_parent 2.57e+00 NA 2.25e+00 2.89e+00</span> -<span class="co">## sigma 1.68e+00 NA 1.12e+00 2.24e+00</span> -<span class="co">## </span> -<span class="co">## $DFOP</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 9.99e+01 1.41e-26 98.8116 101.0810</span> -<span class="co">## k1 2.67e-02 5.05e-06 0.0243 0.0295</span> -<span class="co">## k2 2.95e-12 5.00e-01 0.0000 Inf</span> -<span class="co">## g 6.47e-01 3.67e-06 0.6248 0.6677</span> -<span class="co">## sigma 1.27e+00 8.91e-06 0.8395 1.6929</span> -<span class="co">## </span> -<span class="co">## </span> -<span class="co">## DTx values:</span> -<span class="co">## DT50 DT90 DT50_rep</span> -<span class="co">## SFO 67.7 2.25e+02 6.77e+01</span> -<span class="co">## IORE 58.2 1.07e+03 3.22e+02</span> -<span class="co">## DFOP 55.5 4.28e+11 2.35e+11</span> -<span class="co">## </span> -<span class="co">## Representative half-life:</span> -<span class="co">## [1] 321.51</span></code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p5a</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 465.21753 56.27506 32.06401 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 64.4304</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 95.8401 4.67e-21 92.245 99.4357</span></span> +<span><span class="co">## k_parent 0.0102 3.92e-12 0.009 0.0117</span></span> +<span><span class="co">## sigma 4.8230 3.81e-06 3.214 6.4318</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 1.01e+02 NA 9.91e+01 1.02e+02</span></span> +<span><span class="co">## k__iore_parent 1.54e-05 NA 4.08e-06 5.84e-05</span></span> +<span><span class="co">## N_parent 2.57e+00 NA 2.25e+00 2.89e+00</span></span> +<span><span class="co">## sigma 1.68e+00 NA 1.12e+00 2.24e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 9.99e+01 1.41e-26 98.8116 101.0810</span></span> +<span><span class="co">## k1 2.67e-02 5.05e-06 0.0243 0.0295</span></span> +<span><span class="co">## k2 2.26e-12 5.00e-01 0.0000 Inf</span></span> +<span><span class="co">## g 6.47e-01 3.67e-06 0.6248 0.6677</span></span> +<span><span class="co">## sigma 1.27e+00 8.91e-06 0.8395 1.6929</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 67.7 2.25e+02 6.77e+01</span></span> +<span><span class="co">## IORE 58.2 1.07e+03 3.22e+02</span></span> +<span><span class="co">## DFOP 55.5 5.59e+11 3.07e+11</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 321.51</span></span></code></pre> </div> <div class="section level3"> <h3 id="example-on-page-5-lower-panel">Example on page 5, lower panel<a class="anchor" aria-label="anchor" href="#example-on-page-5-lower-panel"></a> </h3> <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p5b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p5b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></code></pre> -<pre><code><span class="co">## The half-life obtained from the IORE model may be used</span></code></pre> +<code class="sourceCode R"><span><span class="va">p5b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p5b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p5b</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p5b</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p5b-1.png" width="700"></p> <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p5b</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Sums of squares:</span> -<span class="co">## SFO IORE DFOP </span> -<span class="co">## 94.81123 10.10936 7.55871 </span> -<span class="co">## </span> -<span class="co">## Critical sum of squares for checking the SFO model:</span> -<span class="co">## [1] 11.77879</span> -<span class="co">## </span> -<span class="co">## Parameters:</span> -<span class="co">## $SFO</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 96.497 2.32e-24 94.85271 98.14155</span> -<span class="co">## k_parent 0.008 3.42e-14 0.00737 0.00869</span> -<span class="co">## sigma 2.295 1.22e-05 1.47976 3.11036</span> -<span class="co">## </span> -<span class="co">## $IORE</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 9.85e+01 1.17e-28 9.79e+01 9.92e+01</span> -<span class="co">## k__iore_parent 1.53e-04 6.50e-03 7.21e-05 3.26e-04</span> -<span class="co">## N_parent 1.94e+00 5.88e-13 1.76e+00 2.12e+00</span> -<span class="co">## sigma 7.49e-01 1.63e-05 4.82e-01 1.02e+00</span> -<span class="co">## </span> -<span class="co">## $DFOP</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 9.84e+01 1.24e-27 97.8078 98.9187</span> -<span class="co">## k1 1.55e-02 4.10e-04 0.0143 0.0167</span> -<span class="co">## k2 9.41e-12 5.00e-01 0.0000 Inf</span> -<span class="co">## g 6.89e-01 2.92e-03 0.6626 0.7142</span> -<span class="co">## sigma 6.48e-01 2.38e-05 0.4147 0.8813</span> -<span class="co">## </span> -<span class="co">## </span> -<span class="co">## DTx values:</span> -<span class="co">## DT50 DT90 DT50_rep</span> -<span class="co">## SFO 86.6 2.88e+02 8.66e+01</span> -<span class="co">## IORE 85.5 7.17e+02 2.16e+02</span> -<span class="co">## DFOP 83.6 1.21e+11 7.36e+10</span> -<span class="co">## </span> -<span class="co">## Representative half-life:</span> -<span class="co">## [1] 215.87</span></code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p5b</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 94.81123 10.10936 7.55871 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 11.77879</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 96.497 2.32e-24 94.85271 98.14155</span></span> +<span><span class="co">## k_parent 0.008 3.42e-14 0.00737 0.00869</span></span> +<span><span class="co">## sigma 2.295 1.22e-05 1.47976 3.11036</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 9.85e+01 1.17e-28 9.79e+01 9.92e+01</span></span> +<span><span class="co">## k__iore_parent 1.53e-04 6.50e-03 7.21e-05 3.26e-04</span></span> +<span><span class="co">## N_parent 1.94e+00 5.88e-13 1.76e+00 2.12e+00</span></span> +<span><span class="co">## sigma 7.49e-01 1.63e-05 4.82e-01 1.02e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 9.84e+01 1.24e-27 97.8078 98.9187</span></span> +<span><span class="co">## k1 1.55e-02 4.10e-04 0.0143 0.0167</span></span> +<span><span class="co">## k2 8.63e-12 5.00e-01 0.0000 Inf</span></span> +<span><span class="co">## g 6.89e-01 2.92e-03 0.6626 0.7142</span></span> +<span><span class="co">## sigma 6.48e-01 2.38e-05 0.4147 0.8813</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 86.6 2.88e+02 8.66e+01</span></span> +<span><span class="co">## IORE 85.5 7.17e+02 2.16e+02</span></span> +<span><span class="co">## DFOP 83.6 1.32e+11 8.04e+10</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 215.87</span></span></code></pre> </div> <div class="section level3"> <h3 id="example-on-page-6">Example on page 6<a class="anchor" aria-label="anchor" href="#example-on-page-6"></a> </h3> <div class="sourceCode" id="cb13"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p6</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p6"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></code></pre> -<pre><code><span class="co">## The half-life obtained from the IORE model may be used</span></code></pre> +<code class="sourceCode R"><span><span class="va">p6</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p6"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb16"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p6</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p6</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p6-1.png" width="700"></p> <div class="sourceCode" id="cb17"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p6</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Sums of squares:</span> -<span class="co">## SFO IORE DFOP </span> -<span class="co">## 188.45361 51.00699 42.46931 </span> -<span class="co">## </span> -<span class="co">## Critical sum of squares for checking the SFO model:</span> -<span class="co">## [1] 58.39888</span> -<span class="co">## </span> -<span class="co">## Parameters:</span> -<span class="co">## $SFO</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 94.7759 7.29e-24 92.3478 97.2039</span> -<span class="co">## k_parent 0.0179 8.02e-16 0.0166 0.0194</span> -<span class="co">## sigma 3.0696 3.81e-06 2.0456 4.0936</span> -<span class="co">## </span> -<span class="co">## $IORE</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 97.12446 2.63e-26 95.62461 98.62431</span> -<span class="co">## k__iore_parent 0.00252 1.95e-03 0.00134 0.00472</span> -<span class="co">## N_parent 1.49587 4.07e-13 1.33896 1.65279</span> -<span class="co">## sigma 1.59698 5.05e-06 1.06169 2.13227</span> -<span class="co">## </span> -<span class="co">## $DFOP</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 9.66e+01 1.57e-25 95.3476 97.8979</span> -<span class="co">## k1 2.55e-02 7.33e-06 0.0233 0.0278</span> -<span class="co">## k2 4.40e-11 5.00e-01 0.0000 Inf</span> -<span class="co">## g 8.61e-01 7.55e-06 0.8314 0.8867</span> -<span class="co">## sigma 1.46e+00 6.93e-06 0.9661 1.9483</span> -<span class="co">## </span> -<span class="co">## </span> -<span class="co">## DTx values:</span> -<span class="co">## DT50 DT90 DT50_rep</span> -<span class="co">## SFO 38.6 1.28e+02 3.86e+01</span> -<span class="co">## IORE 34.0 1.77e+02 5.32e+01</span> -<span class="co">## DFOP 34.1 7.43e+09 1.58e+10</span> -<span class="co">## </span> -<span class="co">## Representative half-life:</span> -<span class="co">## [1] 53.17</span></code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p6</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 188.45361 51.00699 42.46931 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 58.39888</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 94.7759 7.29e-24 92.3478 97.2039</span></span> +<span><span class="co">## k_parent 0.0179 8.02e-16 0.0166 0.0194</span></span> +<span><span class="co">## sigma 3.0696 3.81e-06 2.0456 4.0936</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 97.12446 2.63e-26 95.62461 98.62431</span></span> +<span><span class="co">## k__iore_parent 0.00252 1.95e-03 0.00134 0.00472</span></span> +<span><span class="co">## N_parent 1.49587 4.07e-13 1.33896 1.65279</span></span> +<span><span class="co">## sigma 1.59698 5.05e-06 1.06169 2.13227</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 9.66e+01 1.57e-25 95.3476 97.8979</span></span> +<span><span class="co">## k1 2.55e-02 7.33e-06 0.0233 0.0278</span></span> +<span><span class="co">## k2 3.22e-11 5.00e-01 0.0000 Inf</span></span> +<span><span class="co">## g 8.61e-01 7.55e-06 0.8314 0.8867</span></span> +<span><span class="co">## sigma 1.46e+00 6.93e-06 0.9661 1.9483</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 38.6 1.28e+02 3.86e+01</span></span> +<span><span class="co">## IORE 34.0 1.77e+02 5.32e+01</span></span> +<span><span class="co">## DFOP 34.1 1.01e+10 2.15e+10</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 53.17</span></span></code></pre> </div> <div class="section level3"> <h3 id="example-on-page-7">Example on page 7<a class="anchor" aria-label="anchor" href="#example-on-page-7"></a> </h3> <div class="sourceCode" id="cb19"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p7</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p7"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></code></pre> -<pre><code><span class="co">## The half-life obtained from the IORE model may be used</span></code></pre> +<code class="sourceCode R"><span><span class="va">p7</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p7"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb22"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p7</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p7</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p7-1.png" width="700"></p> <div class="sourceCode" id="cb23"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p7</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Sums of squares:</span> -<span class="co">## SFO IORE DFOP </span> -<span class="co">## 3661.661 3195.030 3174.145 </span> -<span class="co">## </span> -<span class="co">## Critical sum of squares for checking the SFO model:</span> -<span class="co">## [1] 3334.194</span> -<span class="co">## </span> -<span class="co">## Parameters:</span> -<span class="co">## $SFO</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 96.41796 4.80e-53 93.32245 99.51347</span> -<span class="co">## k_parent 0.00735 7.64e-21 0.00641 0.00843</span> -<span class="co">## sigma 7.94557 1.83e-15 6.46713 9.42401</span> -<span class="co">## </span> -<span class="co">## $IORE</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 9.92e+01 NA 9.55e+01 1.03e+02</span> -<span class="co">## k__iore_parent 1.60e-05 NA 1.45e-07 1.77e-03</span> -<span class="co">## N_parent 2.45e+00 NA 1.35e+00 3.54e+00</span> -<span class="co">## sigma 7.42e+00 NA 6.04e+00 8.80e+00</span> -<span class="co">## </span> -<span class="co">## $DFOP</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 9.89e+01 9.44e-49 95.4640 102.2573</span> -<span class="co">## k1 1.81e-02 1.75e-01 0.0116 0.0281</span> -<span class="co">## k2 2.81e-10 5.00e-01 0.0000 Inf</span> -<span class="co">## g 6.06e-01 2.19e-01 0.4826 0.7178</span> -<span class="co">## sigma 7.40e+00 2.97e-15 6.0201 8.7754</span> -<span class="co">## </span> -<span class="co">## </span> -<span class="co">## DTx values:</span> -<span class="co">## DT50 DT90 DT50_rep</span> -<span class="co">## SFO 94.3 3.13e+02 9.43e+01</span> -<span class="co">## IORE 96.7 1.51e+03 4.55e+02</span> -<span class="co">## DFOP 96.4 4.87e+09 2.46e+09</span> -<span class="co">## </span> -<span class="co">## Representative half-life:</span> -<span class="co">## [1] 454.55</span></code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p7</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 3661.661 3195.030 3174.145 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 3334.194</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 96.41796 4.80e-53 93.32245 99.51347</span></span> +<span><span class="co">## k_parent 0.00735 7.64e-21 0.00641 0.00843</span></span> +<span><span class="co">## sigma 7.94557 1.83e-15 6.46713 9.42401</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 9.92e+01 NA 9.55e+01 1.03e+02</span></span> +<span><span class="co">## k__iore_parent 1.60e-05 NA 1.45e-07 1.77e-03</span></span> +<span><span class="co">## N_parent 2.45e+00 NA 1.35e+00 3.54e+00</span></span> +<span><span class="co">## sigma 7.42e+00 NA 6.04e+00 8.80e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 9.89e+01 9.44e-49 95.4640 102.2573</span></span> +<span><span class="co">## k1 1.81e-02 1.75e-01 0.0116 0.0281</span></span> +<span><span class="co">## k2 3.63e-10 5.00e-01 0.0000 Inf</span></span> +<span><span class="co">## g 6.06e-01 2.19e-01 0.4826 0.7178</span></span> +<span><span class="co">## sigma 7.40e+00 2.97e-15 6.0201 8.7754</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 94.3 3.13e+02 9.43e+01</span></span> +<span><span class="co">## IORE 96.7 1.51e+03 4.55e+02</span></span> +<span><span class="co">## DFOP 96.4 3.77e+09 1.91e+09</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 454.55</span></span></code></pre> </div> </div> <div class="section level2"> @@ -337,52 +343,52 @@ </h3> <p>For this dataset, the IORE fit does not converge when the default starting values used by mkin for the IORE model are used. Therefore, a lower value for the rate constant is used here.</p> <div class="sourceCode" id="cb25"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p8</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p8"</span><span class="op">]</span><span class="op">]</span>, parms.ini <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k__iore_parent <span class="op">=</span> <span class="fl">1e-3</span><span class="op">)</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></code></pre> -<pre><code><span class="co">## The half-life obtained from the IORE model may be used</span></code></pre> +<code class="sourceCode R"><span><span class="va">p8</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p8"</span><span class="op">]</span><span class="op">]</span>, parms.ini <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k__iore_parent <span class="op">=</span> <span class="fl">1e-3</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb28"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p8</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p8</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p8-1.png" width="700"></p> <div class="sourceCode" id="cb29"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p8</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Sums of squares:</span> -<span class="co">## SFO IORE DFOP </span> -<span class="co">## 1996.9408 444.9237 547.5616 </span> -<span class="co">## </span> -<span class="co">## Critical sum of squares for checking the SFO model:</span> -<span class="co">## [1] 477.4924</span> -<span class="co">## </span> -<span class="co">## Parameters:</span> -<span class="co">## $SFO</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 88.16549 6.53e-29 83.37344 92.95754</span> -<span class="co">## k_parent 0.00803 1.67e-13 0.00674 0.00957</span> -<span class="co">## sigma 7.44786 4.17e-10 5.66209 9.23363</span> -<span class="co">## </span> -<span class="co">## $IORE</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 9.77e+01 7.03e-35 9.44e+01 1.01e+02</span> -<span class="co">## k__iore_parent 6.14e-05 3.20e-02 2.12e-05 1.78e-04</span> -<span class="co">## N_parent 2.27e+00 4.23e-18 2.00e+00 2.54e+00</span> -<span class="co">## sigma 3.52e+00 5.36e-10 2.67e+00 4.36e+00</span> -<span class="co">## </span> -<span class="co">## $DFOP</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 95.70619 8.99e-32 91.87941 99.53298</span> -<span class="co">## k1 0.02500 5.25e-04 0.01422 0.04394</span> -<span class="co">## k2 0.00273 6.84e-03 0.00125 0.00597</span> -<span class="co">## g 0.58835 2.84e-06 0.36595 0.77970</span> -<span class="co">## sigma 3.90001 6.94e-10 2.96260 4.83741</span> -<span class="co">## </span> -<span class="co">## </span> -<span class="co">## DTx values:</span> -<span class="co">## DT50 DT90 DT50_rep</span> -<span class="co">## SFO 86.3 287 86.3</span> -<span class="co">## IORE 53.4 668 201.0</span> -<span class="co">## DFOP 55.6 517 253.0</span> -<span class="co">## </span> -<span class="co">## Representative half-life:</span> -<span class="co">## [1] 201.03</span></code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p8</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 1996.9408 444.9237 547.5616 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 477.4924</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 88.16549 6.53e-29 83.37344 92.95754</span></span> +<span><span class="co">## k_parent 0.00803 1.67e-13 0.00674 0.00957</span></span> +<span><span class="co">## sigma 7.44786 4.17e-10 5.66209 9.23363</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 9.77e+01 7.03e-35 9.44e+01 1.01e+02</span></span> +<span><span class="co">## k__iore_parent 6.14e-05 3.20e-02 2.12e-05 1.78e-04</span></span> +<span><span class="co">## N_parent 2.27e+00 4.23e-18 2.00e+00 2.54e+00</span></span> +<span><span class="co">## sigma 3.52e+00 5.36e-10 2.67e+00 4.36e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 95.70619 8.99e-32 91.87941 99.53298</span></span> +<span><span class="co">## k1 0.02500 5.25e-04 0.01422 0.04394</span></span> +<span><span class="co">## k2 0.00273 6.84e-03 0.00125 0.00597</span></span> +<span><span class="co">## g 0.58835 2.84e-06 0.36595 0.77970</span></span> +<span><span class="co">## sigma 3.90001 6.94e-10 2.96260 4.83741</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 86.3 287 86.3</span></span> +<span><span class="co">## IORE 53.4 668 201.0</span></span> +<span><span class="co">## DFOP 55.6 517 253.0</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 201.03</span></span></code></pre> </div> </div> <div class="section level2"> @@ -392,160 +398,165 @@ <h3 id="example-on-page-9-upper-panel">Example on page 9, upper panel<a class="anchor" aria-label="anchor" href="#example-on-page-9-upper-panel"></a> </h3> <div class="sourceCode" id="cb31"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p9a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p9a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></code></pre> -<pre><code><span class="co">## The half-life obtained from the IORE model may be used</span></code></pre> +<code class="sourceCode R"><span><span class="va">p9a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p9a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb34"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p9a</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p9a</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p9a-1.png" width="700"></p> <div class="sourceCode" id="cb35"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p9a</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Sums of squares:</span> -<span class="co">## SFO IORE DFOP </span> -<span class="co">## 839.35238 88.57064 9.93363 </span> -<span class="co">## </span> -<span class="co">## Critical sum of squares for checking the SFO model:</span> -<span class="co">## [1] 105.5678</span> -<span class="co">## </span> -<span class="co">## Parameters:</span> -<span class="co">## $SFO</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 88.1933 3.06e-12 79.9447 96.4419</span> -<span class="co">## k_parent 0.0409 2.07e-07 0.0324 0.0516</span> -<span class="co">## sigma 7.2429 3.92e-05 4.4768 10.0090</span> -<span class="co">## </span> -<span class="co">## $IORE</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 9.89e+01 1.12e-16 9.54e+01 1.02e+02</span> -<span class="co">## k__iore_parent 1.93e-05 1.13e-01 3.49e-06 1.06e-04</span> -<span class="co">## N_parent 2.91e+00 1.45e-09 2.50e+00 3.32e+00</span> -<span class="co">## sigma 2.35e+00 5.31e-05 1.45e+00 3.26e+00</span> -<span class="co">## </span> -<span class="co">## $DFOP</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 9.85e+01 2.54e-20 97.390 99.672</span> -<span class="co">## k1 1.38e-01 3.52e-05 0.131 0.146</span> -<span class="co">## k2 9.02e-13 5.00e-01 0.000 Inf</span> -<span class="co">## g 6.52e-01 8.13e-06 0.642 0.661</span> -<span class="co">## sigma 7.88e-01 6.13e-02 0.481 1.095</span> -<span class="co">## </span> -<span class="co">## </span> -<span class="co">## DTx values:</span> -<span class="co">## DT50 DT90 DT50_rep</span> -<span class="co">## SFO 16.9 5.63e+01 1.69e+01</span> -<span class="co">## IORE 11.6 3.37e+02 1.01e+02</span> -<span class="co">## DFOP 10.5 1.38e+12 7.68e+11</span> -<span class="co">## </span> -<span class="co">## Representative half-life:</span> -<span class="co">## [1] 101.43</span></code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p9a</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 839.35238 88.57064 9.93363 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 105.5678</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 88.1933 3.06e-12 79.9447 96.4419</span></span> +<span><span class="co">## k_parent 0.0409 2.07e-07 0.0324 0.0516</span></span> +<span><span class="co">## sigma 7.2429 3.92e-05 4.4768 10.0090</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 9.89e+01 1.12e-16 9.54e+01 1.02e+02</span></span> +<span><span class="co">## k__iore_parent 1.93e-05 1.13e-01 3.49e-06 1.06e-04</span></span> +<span><span class="co">## N_parent 2.91e+00 1.45e-09 2.50e+00 3.32e+00</span></span> +<span><span class="co">## sigma 2.35e+00 5.31e-05 1.45e+00 3.26e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 9.85e+01 2.54e-20 97.390 99.672</span></span> +<span><span class="co">## k1 1.38e-01 3.52e-05 0.131 0.146</span></span> +<span><span class="co">## k2 9.02e-13 5.00e-01 0.000 Inf</span></span> +<span><span class="co">## g 6.52e-01 8.13e-06 0.642 0.661</span></span> +<span><span class="co">## sigma 7.88e-01 6.13e-02 0.481 1.095</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 16.9 5.63e+01 1.69e+01</span></span> +<span><span class="co">## IORE 11.6 3.37e+02 1.01e+02</span></span> +<span><span class="co">## DFOP 10.5 1.38e+12 7.69e+11</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 101.43</span></span></code></pre> <p>In this example, the residuals of the SFO indicate a lack of fit of this model, so even if it was an abiotic experiment, the data do not suggest a simple exponential decline.</p> </div> <div class="section level3"> <h3 id="example-on-page-9-lower-panel">Example on page 9, lower panel<a class="anchor" aria-label="anchor" href="#example-on-page-9-lower-panel"></a> </h3> <div class="sourceCode" id="cb37"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p9b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p9b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></code></pre> -<pre><code><span class="co">## The half-life obtained from the IORE model may be used</span></code></pre> -<div class="sourceCode" id="cb40"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p9b</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="va">p9b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p9b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in sqrt(diag(covar_notrans)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span></span> +<span><span class="co">## doubtful</span></span></code></pre> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> +<div class="sourceCode" id="cb44"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p9b</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p9b-1.png" width="700"></p> -<div class="sourceCode" id="cb41"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p9b</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Sums of squares:</span> -<span class="co">## SFO IORE DFOP </span> -<span class="co">## 35.64867 23.22334 35.64867 </span> -<span class="co">## </span> -<span class="co">## Critical sum of squares for checking the SFO model:</span> -<span class="co">## [1] 28.54188</span> -<span class="co">## </span> -<span class="co">## Parameters:</span> -<span class="co">## $SFO</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 94.7123 2.15e-19 93.178 96.2464</span> -<span class="co">## k_parent 0.0389 4.47e-14 0.037 0.0408</span> -<span class="co">## sigma 1.5957 1.28e-04 0.932 2.2595</span> -<span class="co">## </span> -<span class="co">## $IORE</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 93.863 2.32e-18 92.4565 95.269</span> -<span class="co">## k__iore_parent 0.127 1.85e-02 0.0504 0.321</span> -<span class="co">## N_parent 0.711 1.88e-05 0.4843 0.937</span> -<span class="co">## sigma 1.288 1.76e-04 0.7456 1.830</span> -<span class="co">## </span> -<span class="co">## $DFOP</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 94.7123 1.61e-16 93.1355 96.2891</span> -<span class="co">## k1 0.0389 1.08e-04 0.0266 0.0569</span> -<span class="co">## k2 0.0389 2.24e-04 0.0255 0.0592</span> -<span class="co">## g 0.5256 5.00e-01 0.0000 1.0000</span> -<span class="co">## sigma 1.5957 2.50e-04 0.9135 2.2779</span> -<span class="co">## </span> -<span class="co">## </span> -<span class="co">## DTx values:</span> -<span class="co">## DT50 DT90 DT50_rep</span> -<span class="co">## SFO 17.8 59.2 17.8</span> -<span class="co">## IORE 18.4 49.2 14.8</span> -<span class="co">## DFOP 17.8 59.2 17.8</span> -<span class="co">## </span> -<span class="co">## Representative half-life:</span> -<span class="co">## [1] 14.8</span></code></pre> +<div class="sourceCode" id="cb45"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p9b</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 35.64867 23.22334 35.64867 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 28.54188</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 94.7123 2.15e-19 93.178 96.2464</span></span> +<span><span class="co">## k_parent 0.0389 4.47e-14 0.037 0.0408</span></span> +<span><span class="co">## sigma 1.5957 1.28e-04 0.932 2.2595</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 93.863 2.32e-18 92.4565 95.269</span></span> +<span><span class="co">## k__iore_parent 0.127 1.85e-02 0.0504 0.321</span></span> +<span><span class="co">## N_parent 0.711 1.88e-05 0.4843 0.937</span></span> +<span><span class="co">## sigma 1.288 1.76e-04 0.7456 1.830</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 94.7123 1.61e-16 93.1355 96.2891</span></span> +<span><span class="co">## k1 0.0389 1.08e-04 0.0266 0.0569</span></span> +<span><span class="co">## k2 0.0389 2.23e-04 0.0255 0.0592</span></span> +<span><span class="co">## g 0.5256 NaN NA NA</span></span> +<span><span class="co">## sigma 1.5957 2.50e-04 0.9135 2.2779</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 17.8 59.2 17.8</span></span> +<span><span class="co">## IORE 18.4 49.2 14.8</span></span> +<span><span class="co">## DFOP 17.8 59.2 17.8</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 14.8</span></span></code></pre> <p>Here, mkin gives a longer slow DT50 for the DFOP model (17.8 days) than PestDF (13.5 days). Presumably, this is related to the fact that PestDF gives a negative value for the proportion of the fast degradation which should be between 0 and 1, inclusive. This parameter is called f in PestDF and g in mkin. In mkin, it is restricted to the interval from 0 to 1.</p> </div> <div class="section level3"> <h3 id="example-on-page-10">Example on page 10<a class="anchor" aria-label="anchor" href="#example-on-page-10"></a> </h3> -<div class="sourceCode" id="cb43"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p10</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p10"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></code></pre> -<pre><code><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></code></pre> -<pre><code><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span> -<span class="co">## doubtful</span></code></pre> -<pre><code><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></code></pre> -<pre><code><span class="co">## The half-life obtained from the IORE model may be used</span></code></pre> -<div class="sourceCode" id="cb49"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p10</span><span class="op">)</span></code></pre></div> +<div class="sourceCode" id="cb47"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">p10</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p10"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span></span> +<span><span class="co">## doubtful</span></span></code></pre> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> +<div class="sourceCode" id="cb53"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p10</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p10-1.png" width="700"></p> -<div class="sourceCode" id="cb50"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p10</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Sums of squares:</span> -<span class="co">## SFO IORE DFOP </span> -<span class="co">## 899.4089 336.4348 899.4089 </span> -<span class="co">## </span> -<span class="co">## Critical sum of squares for checking the SFO model:</span> -<span class="co">## [1] 413.4841</span> -<span class="co">## </span> -<span class="co">## Parameters:</span> -<span class="co">## $SFO</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 101.7315 6.42e-11 91.9259 111.5371</span> -<span class="co">## k_parent 0.0495 1.70e-07 0.0404 0.0607</span> -<span class="co">## sigma 8.0152 1.28e-04 4.6813 11.3491</span> -<span class="co">## </span> -<span class="co">## $IORE</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 96.86 3.32e-12 90.848 102.863</span> -<span class="co">## k__iore_parent 2.96 7.91e-02 0.687 12.761</span> -<span class="co">## N_parent 0.00 5.00e-01 -0.372 0.372</span> -<span class="co">## sigma 4.90 1.77e-04 2.837 6.968</span> -<span class="co">## </span> -<span class="co">## $DFOP</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 101.7315 1.41e-09 91.6534 111.8097</span> -<span class="co">## k1 0.0495 6.32e-03 0.0241 0.1018</span> -<span class="co">## k2 0.0495 2.41e-03 0.0272 0.0901</span> -<span class="co">## g 0.4487 5.00e-01 NA NA</span> -<span class="co">## sigma 8.0152 2.50e-04 4.5886 11.4418</span> -<span class="co">## </span> -<span class="co">## </span> -<span class="co">## DTx values:</span> -<span class="co">## DT50 DT90 DT50_rep</span> -<span class="co">## SFO 14.0 46.5 14.00</span> -<span class="co">## IORE 16.4 29.4 8.86</span> -<span class="co">## DFOP 14.0 46.5 14.00</span> -<span class="co">## </span> -<span class="co">## Representative half-life:</span> -<span class="co">## [1] 8.86</span></code></pre> +<div class="sourceCode" id="cb54"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p10</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 899.4089 336.4348 899.4089 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 413.4841</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 101.7315 6.42e-11 91.9259 111.5371</span></span> +<span><span class="co">## k_parent 0.0495 1.70e-07 0.0404 0.0607</span></span> +<span><span class="co">## sigma 8.0152 1.28e-04 4.6813 11.3491</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 96.86 3.32e-12 90.848 102.863</span></span> +<span><span class="co">## k__iore_parent 2.96 7.91e-02 0.687 12.761</span></span> +<span><span class="co">## N_parent 0.00 5.00e-01 -0.372 0.372</span></span> +<span><span class="co">## sigma 4.90 1.77e-04 2.837 6.968</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 101.7315 1.41e-09 91.6534 111.8097</span></span> +<span><span class="co">## k1 0.0495 6.58e-03 0.0303 0.0809</span></span> +<span><span class="co">## k2 0.0495 2.60e-03 0.0410 0.0598</span></span> +<span><span class="co">## g 0.4487 5.00e-01 NA NA</span></span> +<span><span class="co">## sigma 8.0152 2.50e-04 4.5886 11.4418</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 14.0 46.5 14.00</span></span> +<span><span class="co">## IORE 16.4 29.4 8.86</span></span> +<span><span class="co">## DFOP 14.0 46.5 14.00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 8.86</span></span></code></pre> <p>Here, a value below N is given for the IORE model, because the data suggests a faster decline towards the end of the experiment, which appears physically rather unlikely in the case of a photolysis study. It seems PestDF does not constrain N to values above zero, thus the slight difference in IORE model parameters between PestDF and mkin.</p> </div> </div> @@ -555,53 +566,53 @@ <div class="section level3"> <h3 id="example-on-page-11">Example on page 11<a class="anchor" aria-label="anchor" href="#example-on-page-11"></a> </h3> -<div class="sourceCode" id="cb52"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p11</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p11"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></code></pre> -<pre><code><span class="co">## The half-life obtained from the IORE model may be used</span></code></pre> -<div class="sourceCode" id="cb55"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p11</span><span class="op">)</span></code></pre></div> -<p><img src="NAFTA_examples_files/figure-html/p11-1.png" width="700"></p> <div class="sourceCode" id="cb56"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p11</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Sums of squares:</span> -<span class="co">## SFO IORE DFOP </span> -<span class="co">## 579.6805 204.7932 144.7783 </span> -<span class="co">## </span> -<span class="co">## Critical sum of squares for checking the SFO model:</span> -<span class="co">## [1] 251.6944</span> -<span class="co">## </span> -<span class="co">## Parameters:</span> -<span class="co">## $SFO</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 96.15820 4.83e-13 90.24934 1.02e+02</span> -<span class="co">## k_parent 0.00321 4.71e-05 0.00222 4.64e-03</span> -<span class="co">## sigma 6.43473 1.28e-04 3.75822 9.11e+00</span> -<span class="co">## </span> -<span class="co">## $IORE</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 1.05e+02 NA 9.90e+01 1.10e+02</span> -<span class="co">## k__iore_parent 3.11e-17 NA 1.35e-20 7.18e-14</span> -<span class="co">## N_parent 8.36e+00 NA 6.62e+00 1.01e+01</span> -<span class="co">## sigma 3.82e+00 NA 2.21e+00 5.44e+00</span> -<span class="co">## </span> -<span class="co">## $DFOP</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 1.05e+02 9.47e-13 99.9990 109.1224</span> -<span class="co">## k1 4.41e-02 5.95e-03 0.0296 0.0658</span> -<span class="co">## k2 9.93e-13 5.00e-01 0.0000 Inf</span> -<span class="co">## g 3.22e-01 1.45e-03 0.2814 0.3650</span> -<span class="co">## sigma 3.22e+00 3.52e-04 1.8410 4.5906</span> -<span class="co">## </span> -<span class="co">## </span> -<span class="co">## DTx values:</span> -<span class="co">## DT50 DT90 DT50_rep</span> -<span class="co">## SFO 2.16e+02 7.18e+02 2.16e+02</span> -<span class="co">## IORE 9.73e+02 1.37e+08 4.11e+07</span> -<span class="co">## DFOP 3.07e+11 1.93e+12 6.98e+11</span> -<span class="co">## </span> -<span class="co">## Representative half-life:</span> -<span class="co">## [1] 41148171</span></code></pre> +<code class="sourceCode R"><span><span class="va">p11</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p11"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> +<div class="sourceCode" id="cb59"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p11</span><span class="op">)</span></span></code></pre></div> +<p><img src="NAFTA_examples_files/figure-html/p11-1.png" width="700"></p> +<div class="sourceCode" id="cb60"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p11</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 579.6805 204.7932 144.7783 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 251.6944</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 96.15820 4.83e-13 90.24934 1.02e+02</span></span> +<span><span class="co">## k_parent 0.00321 4.71e-05 0.00222 4.64e-03</span></span> +<span><span class="co">## sigma 6.43473 1.28e-04 3.75822 9.11e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 1.05e+02 NA 9.90e+01 1.10e+02</span></span> +<span><span class="co">## k__iore_parent 3.11e-17 NA 1.35e-20 7.18e-14</span></span> +<span><span class="co">## N_parent 8.36e+00 NA 6.62e+00 1.01e+01</span></span> +<span><span class="co">## sigma 3.82e+00 NA 2.21e+00 5.44e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 1.05e+02 9.47e-13 99.9990 109.1224</span></span> +<span><span class="co">## k1 4.41e-02 5.95e-03 0.0296 0.0658</span></span> +<span><span class="co">## k2 9.94e-13 5.00e-01 0.0000 Inf</span></span> +<span><span class="co">## g 3.22e-01 1.45e-03 0.2814 0.3650</span></span> +<span><span class="co">## sigma 3.22e+00 3.52e-04 1.8410 4.5906</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 2.16e+02 7.18e+02 2.16e+02</span></span> +<span><span class="co">## IORE 9.73e+02 1.37e+08 4.11e+07</span></span> +<span><span class="co">## DFOP 3.07e+11 1.93e+12 6.98e+11</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 41148170</span></span></code></pre> <p>In this case, the DFOP fit reported for PestDF resulted in a negative value for the slower rate constant, which is not possible in mkin. The other results are in agreement.</p> </div> </div> @@ -612,380 +623,379 @@ <div class="section level3"> <h3 id="example-on-page-12-upper-panel">Example on page 12, upper panel<a class="anchor" aria-label="anchor" href="#example-on-page-12-upper-panel"></a> </h3> -<div class="sourceCode" id="cb58"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p12a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p12a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Warning in summary.mkinfit(x): Could not calculate correlation; no covariance</span> -<span class="co">## matrix</span> - -<span class="co">## Warning in summary.mkinfit(x): Could not calculate correlation; no covariance</span> -<span class="co">## matrix</span></code></pre> -<pre><code><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></code></pre> -<pre><code><span class="co">## The half-life obtained from the IORE model may be used</span></code></pre> <div class="sourceCode" id="cb62"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p12a</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="va">p12a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p12a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in summary.mkinfit(x): Could not calculate correlation; no covariance</span></span> +<span><span class="co">## matrix</span></span></code></pre> +<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in sqrt(diag(covar_notrans)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span></span> +<span><span class="co">## doubtful</span></span></code></pre> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> +<div class="sourceCode" id="cb70"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p12a</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p12a-1.png" width="700"></p> -<div class="sourceCode" id="cb63"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p12a</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Sums of squares:</span> -<span class="co">## SFO IORE DFOP </span> -<span class="co">## 695.4440 220.0685 695.4440 </span> -<span class="co">## </span> -<span class="co">## Critical sum of squares for checking the SFO model:</span> -<span class="co">## [1] 270.4679</span> -<span class="co">## </span> -<span class="co">## Parameters:</span> -<span class="co">## $SFO</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 100.521 8.75e-12 92.461 108.581</span> -<span class="co">## k_parent 0.124 3.61e-08 0.104 0.148</span> -<span class="co">## sigma 7.048 1.28e-04 4.116 9.980</span> -<span class="co">## </span> -<span class="co">## $IORE</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 96.823 NA NA NA</span> -<span class="co">## k__iore_parent 2.436 NA NA NA</span> -<span class="co">## N_parent 0.263 NA NA NA</span> -<span class="co">## sigma 3.965 NA NA NA</span> -<span class="co">## </span> -<span class="co">## $DFOP</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 100.521 NA NA NA</span> -<span class="co">## k1 0.124 NA NA NA</span> -<span class="co">## k2 0.124 NA NA NA</span> -<span class="co">## g 0.793 NA NA NA</span> -<span class="co">## sigma 7.048 NA NA NA</span> -<span class="co">## </span> -<span class="co">## </span> -<span class="co">## DTx values:</span> -<span class="co">## DT50 DT90 DT50_rep</span> -<span class="co">## SFO 5.58 18.5 5.58</span> -<span class="co">## IORE 6.49 13.2 3.99</span> -<span class="co">## DFOP 5.58 18.5 5.58</span> -<span class="co">## </span> -<span class="co">## Representative half-life:</span> -<span class="co">## [1] 3.99</span></code></pre> +<div class="sourceCode" id="cb71"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p12a</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 695.4440 220.0685 695.4440 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 270.4679</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 100.521 8.75e-12 92.461 108.581</span></span> +<span><span class="co">## k_parent 0.124 3.61e-08 0.104 0.148</span></span> +<span><span class="co">## sigma 7.048 1.28e-04 4.116 9.980</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 96.823 NA NA NA</span></span> +<span><span class="co">## k__iore_parent 2.436 NA NA NA</span></span> +<span><span class="co">## N_parent 0.263 NA NA NA</span></span> +<span><span class="co">## sigma 3.965 NA NA NA</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 100.521 2.74e-10 92.2366 108.805</span></span> +<span><span class="co">## k1 0.124 2.53e-05 0.0908 0.170</span></span> +<span><span class="co">## k2 0.124 2.52e-02 0.0456 0.339</span></span> +<span><span class="co">## g 0.793 NaN NA NA</span></span> +<span><span class="co">## sigma 7.048 2.50e-04 4.0349 10.061</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 5.58 18.5 5.58</span></span> +<span><span class="co">## IORE 6.49 13.2 3.99</span></span> +<span><span class="co">## DFOP 5.58 18.5 5.58</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 3.99</span></span></code></pre> </div> <div class="section level3"> <h3 id="example-on-page-12-lower-panel">Example on page 12, lower panel<a class="anchor" aria-label="anchor" href="#example-on-page-12-lower-panel"></a> </h3> -<div class="sourceCode" id="cb65"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p12b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p12b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Warning in qt(alpha/2, rdf): NaNs produced</span></code></pre> -<pre><code><span class="co">## Warning in qt(1 - alpha/2, rdf): NaNs produced</span></code></pre> -<pre><code><span class="co">## Warning in pt(abs(tval), rdf, lower.tail = FALSE): NaNs produced</span></code></pre> -<pre><code><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></code></pre> -<pre><code><span class="co">## The half-life obtained from the IORE model may be used</span></code></pre> -<div class="sourceCode" id="cb71"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p12b</span><span class="op">)</span></code></pre></div> +<div class="sourceCode" id="cb73"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">p12b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p12b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in qt(alpha/2, rdf): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in qt(1 - alpha/2, rdf): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in sqrt(diag(covar_notrans)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in pt(abs(tval), rdf, lower.tail = FALSE): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> +<div class="sourceCode" id="cb80"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p12b</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p12b-1.png" width="700"></p> -<div class="sourceCode" id="cb72"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p12b</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Sums of squares:</span> -<span class="co">## SFO IORE DFOP </span> -<span class="co">## 58.90242 19.06353 58.90242 </span> -<span class="co">## </span> -<span class="co">## Critical sum of squares for checking the SFO model:</span> -<span class="co">## [1] 51.51756</span> -<span class="co">## </span> -<span class="co">## Parameters:</span> -<span class="co">## $SFO</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 97.6840 0.00039 85.9388 109.4292</span> -<span class="co">## k_parent 0.0589 0.00261 0.0431 0.0805</span> -<span class="co">## sigma 3.4323 0.04356 -1.2377 8.1023</span> -<span class="co">## </span> -<span class="co">## $IORE</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 95.523 0.0055 74.539157 116.51</span> -<span class="co">## k__iore_parent 0.333 0.1433 0.000717 154.57</span> -<span class="co">## N_parent 0.568 0.0677 -0.989464 2.13</span> -<span class="co">## sigma 1.953 0.0975 -5.893100 9.80</span> -<span class="co">## </span> -<span class="co">## $DFOP</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 97.6840 NaN NaN NaN</span> -<span class="co">## k1 0.0589 NaN NA NA</span> -<span class="co">## k2 0.0589 NaN NA NA</span> -<span class="co">## g 0.6473 NaN NA NA</span> -<span class="co">## sigma 3.4323 NaN NaN NaN</span> -<span class="co">## </span> -<span class="co">## </span> -<span class="co">## DTx values:</span> -<span class="co">## DT50 DT90 DT50_rep</span> -<span class="co">## SFO 11.8 39.1 11.80</span> -<span class="co">## IORE 12.9 31.4 9.46</span> -<span class="co">## DFOP 11.8 39.1 11.80</span> -<span class="co">## </span> -<span class="co">## Representative half-life:</span> -<span class="co">## [1] 9.46</span></code></pre> +<div class="sourceCode" id="cb81"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p12b</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 58.90242 19.06353 58.90242 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 51.51756</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 97.6840 0.00039 85.9388 109.4292</span></span> +<span><span class="co">## k_parent 0.0589 0.00261 0.0431 0.0805</span></span> +<span><span class="co">## sigma 3.4323 0.04356 -1.2377 8.1023</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 95.523 0.0055 74.539157 116.51</span></span> +<span><span class="co">## k__iore_parent 0.333 0.1433 0.000717 154.57</span></span> +<span><span class="co">## N_parent 0.568 0.0677 -0.989464 2.13</span></span> +<span><span class="co">## sigma 1.953 0.0975 -5.893100 9.80</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 97.6840 NaN NaN NaN</span></span> +<span><span class="co">## k1 0.0589 NaN NA NA</span></span> +<span><span class="co">## k2 0.0589 NaN NA NA</span></span> +<span><span class="co">## g 0.6473 NaN NA NA</span></span> +<span><span class="co">## sigma 3.4323 NaN NaN NaN</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 11.8 39.1 11.80</span></span> +<span><span class="co">## IORE 12.9 31.4 9.46</span></span> +<span><span class="co">## DFOP 11.8 39.1 11.80</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 9.46</span></span></code></pre> </div> <div class="section level3"> <h3 id="example-on-page-13">Example on page 13<a class="anchor" aria-label="anchor" href="#example-on-page-13"></a> </h3> -<div class="sourceCode" id="cb74"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p13</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p13"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></code></pre> -<pre><code><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></code></pre> -<pre><code><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span> -<span class="co">## doubtful</span></code></pre> -<pre><code><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></code></pre> -<pre><code><span class="co">## The half-life obtained from the IORE model may be used</span></code></pre> -<div class="sourceCode" id="cb80"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p13</span><span class="op">)</span></code></pre></div> +<div class="sourceCode" id="cb83"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">p13</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p13"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> +<div class="sourceCode" id="cb86"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p13</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p13-1.png" width="700"></p> -<div class="sourceCode" id="cb81"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p13</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Sums of squares:</span> -<span class="co">## SFO IORE DFOP </span> -<span class="co">## 174.5971 142.3951 174.5971 </span> -<span class="co">## </span> -<span class="co">## Critical sum of squares for checking the SFO model:</span> -<span class="co">## [1] 172.131</span> -<span class="co">## </span> -<span class="co">## Parameters:</span> -<span class="co">## $SFO</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 92.73500 5.99e-17 89.61936 95.85065</span> -<span class="co">## k_parent 0.00258 2.42e-09 0.00223 0.00299</span> -<span class="co">## sigma 3.41172 7.07e-05 2.05455 4.76888</span> -<span class="co">## </span> -<span class="co">## $IORE</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 91.6016 6.34e-16 88.53086 94.672</span> -<span class="co">## k__iore_parent 0.0396 2.36e-01 0.00207 0.759</span> -<span class="co">## N_parent 0.3541 1.46e-01 -0.35153 1.060</span> -<span class="co">## sigma 3.0811 9.64e-05 1.84296 4.319</span> -<span class="co">## </span> -<span class="co">## $DFOP</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 92.73500 NA 8.95e+01 95.92118</span> -<span class="co">## k1 0.00258 NA 4.24e-04 0.01573</span> -<span class="co">## k2 0.00258 NA 1.76e-03 0.00379</span> -<span class="co">## g 0.16452 NA NA NA</span> -<span class="co">## sigma 3.41172 NA 2.02e+00 4.79960</span> -<span class="co">## </span> -<span class="co">## </span> -<span class="co">## DTx values:</span> -<span class="co">## DT50 DT90 DT50_rep</span> -<span class="co">## SFO 269 892 269</span> -<span class="co">## IORE 261 560 169</span> -<span class="co">## DFOP 269 892 269</span> -<span class="co">## </span> -<span class="co">## Representative half-life:</span> -<span class="co">## [1] 168.51</span></code></pre> +<div class="sourceCode" id="cb87"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p13</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 174.5971 142.3951 174.5971 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 172.131</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 92.73500 5.99e-17 89.61936 95.85065</span></span> +<span><span class="co">## k_parent 0.00258 2.42e-09 0.00223 0.00299</span></span> +<span><span class="co">## sigma 3.41172 7.07e-05 2.05455 4.76888</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 91.6016 6.34e-16 88.53086 94.672</span></span> +<span><span class="co">## k__iore_parent 0.0396 2.36e-01 0.00207 0.759</span></span> +<span><span class="co">## N_parent 0.3541 1.46e-01 -0.35153 1.060</span></span> +<span><span class="co">## sigma 3.0811 9.64e-05 1.84296 4.319</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 92.73500 NA 8.95e+01 95.92118</span></span> +<span><span class="co">## k1 0.00258 NA 4.14e-04 0.01611</span></span> +<span><span class="co">## k2 0.00258 NA 1.74e-03 0.00383</span></span> +<span><span class="co">## g 0.16452 NA 0.00e+00 1.00000</span></span> +<span><span class="co">## sigma 3.41172 NA 2.02e+00 4.79960</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 269 892 269</span></span> +<span><span class="co">## IORE 261 560 169</span></span> +<span><span class="co">## DFOP 269 892 269</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 168.51</span></span></code></pre> </div> </div> <div class="section level2"> <h2 id="dt50-not-observed-in-the-study-and-dfop-problems-in-pestdf">DT50 not observed in the study and DFOP problems in PestDF<a class="anchor" aria-label="anchor" href="#dt50-not-observed-in-the-study-and-dfop-problems-in-pestdf"></a> </h2> -<div class="sourceCode" id="cb83"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p14</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p14"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></code></pre> -<pre><code><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></code></pre> -<pre><code><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span> -<span class="co">## doubtful</span></code></pre> -<pre><code><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></code></pre> -<pre><code><span class="co">## The half-life obtained from the IORE model may be used</span></code></pre> <div class="sourceCode" id="cb89"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p14</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="va">p14</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p14"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span></span> +<span><span class="co">## doubtful</span></span></code></pre> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> +<div class="sourceCode" id="cb95"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p14</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p14-1.png" width="700"></p> -<div class="sourceCode" id="cb90"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p14</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Sums of squares:</span> -<span class="co">## SFO IORE DFOP </span> -<span class="co">## 48.43249 28.67746 27.26248 </span> -<span class="co">## </span> -<span class="co">## Critical sum of squares for checking the SFO model:</span> -<span class="co">## [1] 32.83337</span> -<span class="co">## </span> -<span class="co">## Parameters:</span> -<span class="co">## $SFO</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 99.47124 2.06e-30 98.42254 1.01e+02</span> -<span class="co">## k_parent 0.00279 3.75e-15 0.00256 3.04e-03</span> -<span class="co">## sigma 1.55616 3.81e-06 1.03704 2.08e+00</span> -<span class="co">## </span> -<span class="co">## $IORE</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 1.00e+02 NA NaN NaN</span> -<span class="co">## k__iore_parent 9.44e-08 NA NaN NaN</span> -<span class="co">## N_parent 3.31e+00 NA NaN NaN</span> -<span class="co">## sigma 1.20e+00 NA 0.796 1.6</span> -<span class="co">## </span> -<span class="co">## $DFOP</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 1.00e+02 2.96e-28 99.40280 101.2768</span> -<span class="co">## k1 9.53e-03 1.20e-01 0.00638 0.0143</span> -<span class="co">## k2 5.03e-12 5.00e-01 0.00000 Inf</span> -<span class="co">## g 3.98e-01 2.19e-01 0.30481 0.4998</span> -<span class="co">## sigma 1.17e+00 7.68e-06 0.77406 1.5610</span> -<span class="co">## </span> -<span class="co">## </span> -<span class="co">## DTx values:</span> -<span class="co">## DT50 DT90 DT50_rep</span> -<span class="co">## SFO 2.48e+02 8.25e+02 2.48e+02</span> -<span class="co">## IORE 4.34e+02 2.22e+04 6.70e+03</span> -<span class="co">## DFOP 3.69e+10 3.57e+11 1.38e+11</span> -<span class="co">## </span> -<span class="co">## Representative half-life:</span> -<span class="co">## [1] 6697.44</span></code></pre> +<div class="sourceCode" id="cb96"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p14</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 48.43249 28.67746 27.26248 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 32.83337</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 99.47124 2.06e-30 98.42254 1.01e+02</span></span> +<span><span class="co">## k_parent 0.00279 3.75e-15 0.00256 3.04e-03</span></span> +<span><span class="co">## sigma 1.55616 3.81e-06 1.03704 2.08e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 1.00e+02 NA NaN NaN</span></span> +<span><span class="co">## k__iore_parent 9.44e-08 NA NaN NaN</span></span> +<span><span class="co">## N_parent 3.31e+00 NA NaN NaN</span></span> +<span><span class="co">## sigma 1.20e+00 NA 0.796 1.6</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 1.00e+02 2.96e-28 99.40280 101.2768</span></span> +<span><span class="co">## k1 9.53e-03 1.20e-01 0.00638 0.0143</span></span> +<span><span class="co">## k2 6.08e-12 5.00e-01 0.00000 Inf</span></span> +<span><span class="co">## g 3.98e-01 2.19e-01 0.30481 0.4998</span></span> +<span><span class="co">## sigma 1.17e+00 7.68e-06 0.77406 1.5610</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 2.48e+02 8.25e+02 2.48e+02</span></span> +<span><span class="co">## IORE 4.34e+02 2.22e+04 6.70e+03</span></span> +<span><span class="co">## DFOP 3.05e+10 2.95e+11 1.14e+11</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 6697.44</span></span></code></pre> <p>The slower rate constant reported by PestDF is negative, which is not physically realistic, and not possible in mkin. The other fits give the same results in mkin and PestDF.</p> </div> <div class="section level2"> <h2 id="n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero">N is less than 1 and DFOP fraction parameter is below zero<a class="anchor" aria-label="anchor" href="#n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero"></a> </h2> -<div class="sourceCode" id="cb92"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p15a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p15a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></code></pre> -<pre><code><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></code></pre> -<pre><code><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span> -<span class="co">## doubtful</span></code></pre> -<pre><code><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></code></pre> -<pre><code><span class="co">## The half-life obtained from the IORE model may be used</span></code></pre> <div class="sourceCode" id="cb98"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p15a</span><span class="op">)</span></code></pre></div> -<p><img src="NAFTA_examples_files/figure-html/p15a-1.png" width="700"></p> -<div class="sourceCode" id="cb99"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p15a</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Sums of squares:</span> -<span class="co">## SFO IORE DFOP </span> -<span class="co">## 245.5248 135.0132 245.5248 </span> -<span class="co">## </span> -<span class="co">## Critical sum of squares for checking the SFO model:</span> -<span class="co">## [1] 165.9335</span> -<span class="co">## </span> -<span class="co">## Parameters:</span> -<span class="co">## $SFO</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 97.96751 2.00e-15 94.32049 101.615</span> -<span class="co">## k_parent 0.00952 4.93e-09 0.00824 0.011</span> -<span class="co">## sigma 4.18778 1.28e-04 2.44588 5.930</span> -<span class="co">## </span> -<span class="co">## $IORE</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 95.874 2.94e-15 92.937 98.811</span> -<span class="co">## k__iore_parent 0.629 2.11e-01 0.044 8.982</span> -<span class="co">## N_parent 0.000 5.00e-01 -0.642 0.642</span> -<span class="co">## sigma 3.105 1.78e-04 1.795 4.416</span> -<span class="co">## </span> -<span class="co">## $DFOP</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 97.96751 2.85e-13 94.21913 101.7159</span> -<span class="co">## k1 0.00952 6.28e-02 0.00260 0.0349</span> -<span class="co">## k2 0.00952 1.27e-04 0.00652 0.0139</span> -<span class="co">## g 0.21241 5.00e-01 NA NA</span> -<span class="co">## sigma 4.18778 2.50e-04 2.39747 5.9781</span> -<span class="co">## </span> -<span class="co">## </span> -<span class="co">## DTx values:</span> -<span class="co">## DT50 DT90 DT50_rep</span> -<span class="co">## SFO 72.8 242 72.8</span> -<span class="co">## IORE 76.3 137 41.3</span> -<span class="co">## DFOP 72.8 242 72.8</span> -<span class="co">## </span> -<span class="co">## Representative half-life:</span> -<span class="co">## [1] 41.33</span></code></pre> +<code class="sourceCode R"><span><span class="va">p15a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p15a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb101"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p15b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p15b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></code></pre> -<pre><code><span class="co">## The half-life obtained from the IORE model may be used</span></code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p15a</span><span class="op">)</span></span></code></pre></div> +<p><img src="NAFTA_examples_files/figure-html/p15a-1.png" width="700"></p> +<div class="sourceCode" id="cb102"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p15a</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 245.5248 135.0132 245.5248 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 165.9335</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 97.96751 2.00e-15 94.32049 101.615</span></span> +<span><span class="co">## k_parent 0.00952 4.93e-09 0.00824 0.011</span></span> +<span><span class="co">## sigma 4.18778 1.28e-04 2.44588 5.930</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 95.874 2.94e-15 92.937 98.811</span></span> +<span><span class="co">## k__iore_parent 0.629 2.11e-01 0.044 8.982</span></span> +<span><span class="co">## N_parent 0.000 5.00e-01 -0.642 0.642</span></span> +<span><span class="co">## sigma 3.105 1.78e-04 1.795 4.416</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 97.96751 2.85e-13 94.21913 101.7159</span></span> +<span><span class="co">## k1 0.00952 6.28e-02 0.00250 0.0363</span></span> +<span><span class="co">## k2 0.00952 1.27e-04 0.00646 0.0140</span></span> +<span><span class="co">## g 0.21241 5.00e-01 0.00000 1.0000</span></span> +<span><span class="co">## sigma 4.18778 2.50e-04 2.39747 5.9781</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 72.8 242 72.8</span></span> +<span><span class="co">## IORE 76.3 137 41.3</span></span> +<span><span class="co">## DFOP 72.8 242 72.8</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 41.33</span></span></code></pre> <div class="sourceCode" id="cb104"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p15b</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="va">p15b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p15b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span></span> +<span><span class="co">## doubtful</span></span></code></pre> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> +<div class="sourceCode" id="cb110"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p15b</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p15b-1.png" width="700"></p> -<div class="sourceCode" id="cb105"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p15b</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Sums of squares:</span> -<span class="co">## SFO IORE DFOP </span> -<span class="co">## 106.91629 68.55574 106.91629 </span> -<span class="co">## </span> -<span class="co">## Critical sum of squares for checking the SFO model:</span> -<span class="co">## [1] 84.25618</span> -<span class="co">## </span> -<span class="co">## Parameters:</span> -<span class="co">## $SFO</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 1.01e+02 3.06e-17 98.31594 1.03e+02</span> -<span class="co">## k_parent 4.86e-03 2.48e-10 0.00435 5.42e-03</span> -<span class="co">## sigma 2.76e+00 1.28e-04 1.61402 3.91e+00</span> -<span class="co">## </span> -<span class="co">## $IORE</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 99.83 1.81e-16 97.51349 102.14</span> -<span class="co">## k__iore_parent 0.38 3.22e-01 0.00352 41.05</span> -<span class="co">## N_parent 0.00 5.00e-01 -1.07695 1.08</span> -<span class="co">## sigma 2.21 2.57e-04 1.23245 3.19</span> -<span class="co">## </span> -<span class="co">## $DFOP</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 1.01e+02 NA 9.82e+01 1.04e+02</span> -<span class="co">## k1 4.86e-03 NA 8.62e-04 2.74e-02</span> -<span class="co">## k2 4.86e-03 NA 3.21e-03 7.35e-03</span> -<span class="co">## g 1.88e-01 NA 0.00e+00 1.00e+00</span> -<span class="co">## sigma 2.76e+00 NA 1.58e+00 3.94e+00</span> -<span class="co">## </span> -<span class="co">## </span> -<span class="co">## DTx values:</span> -<span class="co">## DT50 DT90 DT50_rep</span> -<span class="co">## SFO 143 474 143.0</span> -<span class="co">## IORE 131 236 71.2</span> -<span class="co">## DFOP 143 474 143.0</span> -<span class="co">## </span> -<span class="co">## Representative half-life:</span> -<span class="co">## [1] 71.18</span></code></pre> +<div class="sourceCode" id="cb111"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p15b</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 106.91629 68.55574 106.91629 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 84.25618</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 1.01e+02 3.06e-17 98.31594 1.03e+02</span></span> +<span><span class="co">## k_parent 4.86e-03 2.48e-10 0.00435 5.42e-03</span></span> +<span><span class="co">## sigma 2.76e+00 1.28e-04 1.61402 3.91e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 99.83 1.81e-16 97.51349 102.14</span></span> +<span><span class="co">## k__iore_parent 0.38 3.22e-01 0.00352 41.05</span></span> +<span><span class="co">## N_parent 0.00 5.00e-01 -1.07696 1.08</span></span> +<span><span class="co">## sigma 2.21 2.57e-04 1.23245 3.19</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 1.01e+02 NA 9.82e+01 1.04e+02</span></span> +<span><span class="co">## k1 4.86e-03 NA 8.63e-04 2.73e-02</span></span> +<span><span class="co">## k2 4.86e-03 NA 3.21e-03 7.35e-03</span></span> +<span><span class="co">## g 1.88e-01 NA NA NA</span></span> +<span><span class="co">## sigma 2.76e+00 NA 1.58e+00 3.94e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 143 474 143.0</span></span> +<span><span class="co">## IORE 131 236 71.2</span></span> +<span><span class="co">## DFOP 143 474 143.0</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 71.18</span></span></code></pre> <p>In mkin, only the IORE fit is affected (deemed unrealistic), as the fraction parameter of the DFOP model is restricted to the interval between 0 and 1 in mkin. The SFO fits give the same results for both mkin and PestDF.</p> </div> <div class="section level2"> <h2 id="the-dfop-fraction-parameter-is-greater-than-1">The DFOP fraction parameter is greater than 1<a class="anchor" aria-label="anchor" href="#the-dfop-fraction-parameter-is-greater-than-1"></a> </h2> -<div class="sourceCode" id="cb107"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p16</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p16"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></code></pre> -<pre><code><span class="co">## The representative half-life of the IORE model is longer than the one corresponding</span></code></pre> -<pre><code><span class="co">## to the terminal degradation rate found with the DFOP model.</span></code></pre> -<pre><code><span class="co">## The representative half-life obtained from the DFOP model may be used</span></code></pre> -<div class="sourceCode" id="cb112"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p16</span><span class="op">)</span></code></pre></div> -<p><img src="NAFTA_examples_files/figure-html/p16-1.png" width="700"></p> <div class="sourceCode" id="cb113"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p16</span><span class="op">)</span></code></pre></div> -<pre><code><span class="co">## Sums of squares:</span> -<span class="co">## SFO IORE DFOP </span> -<span class="co">## 3831.804 2062.008 1550.980 </span> -<span class="co">## </span> -<span class="co">## Critical sum of squares for checking the SFO model:</span> -<span class="co">## [1] 2247.348</span> -<span class="co">## </span> -<span class="co">## Parameters:</span> -<span class="co">## $SFO</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 71.953 2.33e-13 60.509 83.40</span> -<span class="co">## k_parent 0.159 4.86e-05 0.102 0.25</span> -<span class="co">## sigma 11.302 1.25e-08 8.308 14.30</span> -<span class="co">## </span> -<span class="co">## $IORE</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 8.74e+01 2.48e-16 7.72e+01 97.52972</span> -<span class="co">## k__iore_parent 4.55e-04 2.16e-01 3.48e-05 0.00595</span> -<span class="co">## N_parent 2.70e+00 1.21e-08 1.99e+00 3.40046</span> -<span class="co">## sigma 8.29e+00 1.61e-08 6.09e+00 10.49062</span> -<span class="co">## </span> -<span class="co">## $DFOP</span> -<span class="co">## Estimate Pr(>t) Lower Upper</span> -<span class="co">## parent_0 88.5333 7.40e-18 79.9836 97.083</span> -<span class="co">## k1 18.8461 5.00e-01 0.0000 Inf</span> -<span class="co">## k2 0.0776 1.41e-05 0.0518 0.116</span> -<span class="co">## g 0.4733 1.41e-09 0.3674 0.582</span> -<span class="co">## sigma 7.1902 2.11e-08 5.2785 9.102</span> -<span class="co">## </span> -<span class="co">## </span> -<span class="co">## DTx values:</span> -<span class="co">## DT50 DT90 DT50_rep</span> -<span class="co">## SFO 4.35 14.4 4.35</span> -<span class="co">## IORE 1.48 32.1 9.67</span> -<span class="co">## DFOP 0.67 21.4 8.93</span> -<span class="co">## </span> -<span class="co">## Representative half-life:</span> -<span class="co">## [1] 8.93</span></code></pre> +<code class="sourceCode R"><span><span class="va">p16</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p16"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The representative half-life of the IORE model is longer than the one corresponding</span></span></code></pre> +<pre><code><span><span class="co">## to the terminal degradation rate found with the DFOP model.</span></span></code></pre> +<pre><code><span><span class="co">## The representative half-life obtained from the DFOP model may be used</span></span></code></pre> +<div class="sourceCode" id="cb118"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p16</span><span class="op">)</span></span></code></pre></div> +<p><img src="NAFTA_examples_files/figure-html/p16-1.png" width="700"></p> +<div class="sourceCode" id="cb119"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p16</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 3831.804 2062.008 1550.980 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 2247.348</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 71.953 2.33e-13 60.509 83.40</span></span> +<span><span class="co">## k_parent 0.159 4.86e-05 0.102 0.25</span></span> +<span><span class="co">## sigma 11.302 1.25e-08 8.308 14.30</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 8.74e+01 2.48e-16 7.72e+01 97.52972</span></span> +<span><span class="co">## k__iore_parent 4.55e-04 2.16e-01 3.48e-05 0.00595</span></span> +<span><span class="co">## N_parent 2.70e+00 1.21e-08 1.99e+00 3.40046</span></span> +<span><span class="co">## sigma 8.29e+00 1.61e-08 6.09e+00 10.49062</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 88.5333 7.40e-18 79.9836 97.083</span></span> +<span><span class="co">## k1 18.8461 5.00e-01 0.0000 Inf</span></span> +<span><span class="co">## k2 0.0776 1.41e-05 0.0518 0.116</span></span> +<span><span class="co">## g 0.4733 1.41e-09 0.3674 0.582</span></span> +<span><span class="co">## sigma 7.1902 2.11e-08 5.2785 9.102</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 4.35 14.4 4.35</span></span> +<span><span class="co">## IORE 1.48 32.1 9.67</span></span> +<span><span class="co">## DFOP 0.67 21.4 8.93</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 8.93</span></span></code></pre> <p>In PestDF, the DFOP fit seems to have stuck in a local minimum, as mkin finds a solution with a much lower <span class="math inline">\(\chi^2\)</span> error level. As the half-life from the slower rate constant of the DFOP model is larger than the IORE derived half-life, the NAFTA recommendation obtained with mkin is to use the DFOP representative half-life of 8.9 days.</p> </div> <div class="section level2"> @@ -1022,7 +1032,7 @@ <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer> diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p10-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p10-1.png Binary files differindex a53c48b2..75611a70 100644 --- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p10-1.png +++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p10-1.png diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p15a-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p15a-1.png Binary files differindex fb211a8e..b6faeff9 100644 --- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p15a-1.png +++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p15a-1.png diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p15b-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p15b-1.png Binary files differindex 9aedbf16..6b9ba98c 100644 --- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p15b-1.png +++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p15b-1.png diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p5b-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p5b-1.png Binary files differindex 034eed46..db90244b 100644 --- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p5b-1.png +++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p5b-1.png diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p6-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p6-1.png Binary files differindex 86cd9755..a33372e8 100644 --- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p6-1.png +++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p6-1.png diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p7-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p7-1.png Binary files differindex 10225504..d64ea98d 100644 --- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p7-1.png +++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p7-1.png diff --git a/docs/articles/web_only/benchmarks.html b/docs/articles/web_only/benchmarks.html index 0b14fea2..64c68ea0 100644 --- a/docs/articles/web_only/benchmarks.html +++ b/docs/articles/web_only/benchmarks.html @@ -33,7 +33,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -62,19 +62,25 @@ <a href="../../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul> </li> @@ -105,7 +111,7 @@ <h1 data-toc-skip>Benchmark timings for mkin</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">Last change 14 July 2022 (rebuilt 2022-07-22)</h4> + <h4 data-toc-skip class="date">Last change 14 July 2022 (rebuilt 2022-11-17)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/benchmarks.rmd" class="external-link"><code>vignettes/web_only/benchmarks.rmd</code></a></small> <div class="hidden name"><code>benchmarks.rmd</code></div> @@ -149,7 +155,7 @@ <span> parent <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span> <span> m1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span> <span><span class="va">DFOP_SFO</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> -<span> parent <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span> +<span> parent <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="st">"m1"</span><span class="op">)</span>, <span class="co"># erroneously used FOMC twice, not fixed for consistency</span></span> <span> m1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span> <span><span class="va">t3</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="fu">mmkin_bench</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOMC_SFO</span>, <span class="va">DFOP_SFO</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">FOCUS_D</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span> <span><span class="va">t4</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="fu">mmkin_bench</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOMC_SFO</span>, <span class="va">DFOP_SFO</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">FOCUS_D</span><span class="op">)</span>,</span> @@ -336,8 +342,16 @@ <td align="left">Ryzen 7 1700</td> <td align="left">4.2.1</td> <td align="left">1.1.2</td> -<td align="right">1.962</td> -<td align="right">3.606</td> +<td align="right">1.957</td> +<td align="right">3.633</td> +</tr> +<tr class="even"> +<td align="left">Linux</td> +<td align="left">Ryzen 7 1700</td> +<td align="left">4.2.2</td> +<td align="left">1.2.0</td> +<td align="right">2.140</td> +<td align="right">3.774</td> </tr> </tbody> </table> @@ -506,9 +520,18 @@ <td align="left">Ryzen 7 1700</td> <td align="left">4.2.1</td> <td align="left">1.1.2</td> -<td align="right">1.465</td> -<td align="right">6.184</td> -<td align="right">2.752</td> +<td align="right">1.503</td> +<td align="right">6.147</td> +<td align="right">2.803</td> +</tr> +<tr class="even"> +<td align="left">Linux</td> +<td align="left">Ryzen 7 1700</td> +<td align="left">4.2.2</td> +<td align="left">1.2.0</td> +<td align="right">1.554</td> +<td align="right">6.193</td> +<td align="right">2.843</td> </tr> </tbody> </table> @@ -728,12 +751,24 @@ <td align="left">Ryzen 7 1700</td> <td align="left">4.2.1</td> <td align="left">1.1.2</td> -<td align="right">0.857</td> -<td align="right">1.298</td> -<td align="right">1.504</td> +<td align="right">0.861</td> +<td align="right">1.295</td> +<td align="right">1.507</td> +<td align="right">3.102</td> +<td align="right">1.961</td> +<td align="right">2.852</td> +</tr> +<tr class="even"> +<td align="left">Linux</td> +<td align="left">Ryzen 7 1700</td> +<td align="left">4.2.2</td> +<td align="left">1.2.0</td> +<td align="right">0.913</td> +<td align="right">1.345</td> +<td align="right">1.539</td> <td align="right">3.011</td> -<td align="right">1.888</td> -<td align="right">2.756</td> +<td align="right">1.987</td> +<td align="right">2.802</td> </tr> </tbody> </table> @@ -758,7 +793,7 @@ <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.5.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer> diff --git a/docs/articles/web_only/compiled_models.html b/docs/articles/web_only/compiled_models.html index 0b78bb2e..d17d7aeb 100644 --- a/docs/articles/web_only/compiled_models.html +++ b/docs/articles/web_only/compiled_models.html @@ -33,7 +33,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.1</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -62,19 +62,25 @@ <a href="../../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul> </li> @@ -105,7 +111,7 @@ <h1 data-toc-skip>Performance benefit by using compiled model definitions in mkin</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">2022-10-29</h4> + <h4 data-toc-skip class="date">2022-11-17</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/compiled_models.rmd" class="external-link"><code>vignettes/web_only/compiled_models.rmd</code></a></small> <div class="hidden name"><code>compiled_models.rmd</code></div> @@ -163,10 +169,10 @@ <span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="st">"R package rbenchmark is not available"</span><span class="op">)</span></span> <span><span class="op">}</span></span></code></pre></div> <pre><code><span><span class="co">## test replications relative elapsed</span></span> -<span><span class="co">## 4 analytical 1 1.000 0.188</span></span> -<span><span class="co">## 3 deSolve, compiled 1 1.628 0.306</span></span> -<span><span class="co">## 2 Eigenvalue based 1 2.064 0.388</span></span> -<span><span class="co">## 1 deSolve, not compiled 1 38.894 7.312</span></span></code></pre> +<span><span class="co">## 4 analytical 1 1.000 0.218</span></span> +<span><span class="co">## 3 deSolve, compiled 1 1.550 0.338</span></span> +<span><span class="co">## 2 Eigenvalue based 1 1.950 0.425</span></span> +<span><span class="co">## 1 deSolve, not compiled 1 33.041 7.203</span></span></code></pre> <p>We see that using the compiled model is by more than a factor of 10 faster than using deSolve without compiled code.</p> </div> <div class="section level2"> @@ -193,11 +199,11 @@ <span><span class="op">}</span></span></code></pre></div> <pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <pre><code><span><span class="co">## test replications relative elapsed</span></span> -<span><span class="co">## 2 deSolve, compiled 1 1.00 0.441</span></span> -<span><span class="co">## 1 deSolve, not compiled 1 30.68 13.530</span></span></code></pre> -<p>Here we get a performance benefit of a factor of 31 using the version of the differential equation model compiled from C code!</p> -<p>This vignette was built with mkin 1.1.2 on</p> -<pre><code><span><span class="co">## R version 4.2.1 (2022-06-23)</span></span> +<span><span class="co">## 2 deSolve, compiled 1 1.000 0.510</span></span> +<span><span class="co">## 1 deSolve, not compiled 1 26.247 13.386</span></span></code></pre> +<p>Here we get a performance benefit of a factor of 26 using the version of the differential equation model compiled from C code!</p> +<p>This vignette was built with mkin 1.2.0 on</p> +<pre><code><span><span class="co">## R version 4.2.2 (2022-10-31)</span></span> <span><span class="co">## Platform: x86_64-pc-linux-gnu (64-bit)</span></span> <span><span class="co">## Running under: Debian GNU/Linux 11 (bullseye)</span></span></code></pre> <pre><code><span><span class="co">## CPU model: AMD Ryzen 7 1700 Eight-Core Processor</span></span></code></pre> diff --git a/docs/articles/web_only/dimethenamid_2018.html b/docs/articles/web_only/dimethenamid_2018.html index b020a7b0..8c37edd6 100644 --- a/docs/articles/web_only/dimethenamid_2018.html +++ b/docs/articles/web_only/dimethenamid_2018.html @@ -33,7 +33,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.1</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -62,19 +62,25 @@ <a href="../../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul> </li> @@ -105,7 +111,7 @@ <h1 data-toc-skip>Example evaluations of the dimethenamid data from 2018</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">Last change 1 July 2022, built on 08 Jul 2022</h4> + <h4 data-toc-skip class="date">Last change 1 July 2022, built on 17 Nov 2022</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/dimethenamid_2018.rmd" class="external-link"><code>vignettes/web_only/dimethenamid_2018.rmd</code></a></small> <div class="hidden name"><code>dimethenamid_2018.rmd</code></div> @@ -155,20 +161,20 @@ <span> error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> <p>The plot of the individual SFO fits shown below suggests that at least in some datasets the degradation slows down towards later time points, and that the scatter of the residuals error is smaller for smaller values (panel to the right):</p> <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_sfo_const-1.png" width="700"></p> <p>Using biexponential decline (DFOP) results in a slightly more random scatter of the residuals:</p> <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png" width="700"></p> <p>The population curve (bold line) in the above plot results from taking the mean of the individual transformed parameters, i.e. of log k1 and log k2, as well as of the logit of the g parameter of the DFOP model). Here, this procedure does not result in parameters that represent the degradation well, because in some datasets the fitted value for k2 is extremely close to zero, leading to a log k2 value that dominates the average. This is alleviated if only rate constants that pass the t-test for significant difference from zero (on the untransformed scale) are considered in the averaging:</p> <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png" width="700"></p> <p>While this is visually much more satisfactory, such an average procedure could introduce a bias, as not all results from the individual fits enter the population curve with the same weight. This is where nonlinear mixed-effects models can help out by treating all datasets with equally by fitting a parameter distribution model together with the degradation model and the error model (see below).</p> <p>The remaining trend of the residuals to be higher for higher predicted residues is reduced by using the two-component error model:</p> <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png" width="700"></p> <p>However, note that in the case of using this error model, the fits to the Flaach and BBA 2.3 datasets appear to be ill-defined, indicated by the fact that they did not converge:</p> <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> @@ -178,7 +184,7 @@ Status of individual fits: dataset model Calke Borstel Flaach BBA 2.2 BBA 2.3 Elliot - DFOP OK OK OK OK C OK + DFOP OK OK C OK C OK OK: No warnings C: Optimisation did not converge: @@ -222,7 +228,7 @@ f_parent_nlme_dfop_tc 3 10 671.91 702.34 -325.96 2 vs 3 134.69 <.0001 <p>While the SFO variants converge fast, the additional parameters introduced by this lead to convergence warnings for the DFOP model. The model comparison clearly show that adding correlations between random effects does not improve the fits.</p> <p>The selected model (DFOP with two-component error) fitted to the data assuming no correlations between random effects is shown below.</p> <div class="sourceCode" id="cb13"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_nlme_dfop_tc</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_nlme_dfop_tc</span><span class="op">)</span></span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png" width="700"></p> </div> <div class="section level4"> @@ -231,41 +237,32 @@ f_parent_nlme_dfop_tc 3 10 671.91 702.34 -325.96 2 vs 3 134.69 <.0001 <p>The saemix package provided the first Open Source implementation of the Stochastic Approximation to the Expectation Maximisation (SAEM) algorithm. SAEM fits of degradation models can be conveniently performed using an interface to the saemix package available in current development versions of the mkin package.</p> <p>The corresponding SAEM fits of the four combinations of degradation and error models are fitted below. As there is no convergence criterion implemented in the saemix package, the convergence plots need to be manually checked for every fit. We define control settings that work well for all the parent data fits shown in this vignette.</p> <div class="sourceCode" id="cb14"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span></code></pre></div> -<pre><code>Loading required package: npde</code></pre> -<pre><code>Package saemix, version 3.0 - please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr</code></pre> -<pre><code> -Attaching package: 'saemix'</code></pre> -<pre><code>The following objects are masked from 'package:npde': - - kurtosis, skewness</code></pre> -<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="va">saemix_control</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/saemixControl.html" class="external-link">saemixControl</a></span><span class="op">(</span>nbiter.saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">800</span>, <span class="fl">300</span><span class="op">)</span>, nb.chains <span class="op">=</span> <span class="fl">15</span>,</span> +<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span> +<span><span class="va">saemix_control</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/saemixControl.html" class="external-link">saemixControl</a></span><span class="op">(</span>nbiter.saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">800</span>, <span class="fl">300</span><span class="op">)</span>, nb.chains <span class="op">=</span> <span class="fl">15</span>,</span> <span> print <span class="op">=</span> <span class="cn">FALSE</span>, save <span class="op">=</span> <span class="cn">FALSE</span>, save.graphs <span class="op">=</span> <span class="cn">FALSE</span>, displayProgress <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span> <span><span class="va">saemix_control_moreiter</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/saemixControl.html" class="external-link">saemixControl</a></span><span class="op">(</span>nbiter.saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1600</span>, <span class="fl">300</span><span class="op">)</span>, nb.chains <span class="op">=</span> <span class="fl">15</span>,</span> <span> print <span class="op">=</span> <span class="cn">FALSE</span>, save <span class="op">=</span> <span class="cn">FALSE</span>, save.graphs <span class="op">=</span> <span class="cn">FALSE</span>, displayProgress <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span> <span><span class="va">saemix_control_10k</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/saemixControl.html" class="external-link">saemixControl</a></span><span class="op">(</span>nbiter.saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10000</span>, <span class="fl">300</span><span class="op">)</span>, nb.chains <span class="op">=</span> <span class="fl">15</span>,</span> <span> print <span class="op">=</span> <span class="cn">FALSE</span>, save <span class="op">=</span> <span class="cn">FALSE</span>, save.graphs <span class="op">=</span> <span class="cn">FALSE</span>, displayProgress <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> <p>The convergence plot for the SFO model using constant variance is shown below.</p> -<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">f_parent_saemix_sfo_const</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> <span> control <span class="op">=</span> <span class="va">saemix_control</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span> <span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_sfo_const</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_const-1.png" width="700"></p> <p>Obviously the selected number of iterations is sufficient to reach convergence. This can also be said for the SFO fit using the two-component error model.</p> -<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">f_parent_saemix_sfo_tc</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> <span> control <span class="op">=</span> <span class="va">saemix_control</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span> <span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_sfo_tc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_tc-1.png" width="700"></p> <p>When fitting the DFOP model with constant variance (see below), parameter convergence is not as unambiguous.</p> -<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">f_parent_saemix_dfop_const</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> <span> control <span class="op">=</span> <span class="va">saemix_control</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span> <span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_const</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_const-1.png" width="700"></p> -<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_const</span><span class="op">)</span></span></code></pre></div> <pre><code>Kinetic nonlinear mixed-effects model fit by SAEM Structural model: @@ -286,23 +283,21 @@ DMTA_0 97.99583 96.50079 99.4909 k1 0.06377 0.03432 0.0932 k2 0.00848 0.00444 0.0125 g 0.95701 0.91313 1.0009 -a.1 1.82141 1.60516 2.0377 -SD.DMTA_0 1.64787 0.45729 2.8384 +a.1 1.82141 1.65974 1.9831 +SD.DMTA_0 1.64787 0.45779 2.8379 SD.k1 0.57439 0.24731 0.9015 -SD.k2 0.03296 -2.50524 2.5712 -SD.g 1.10266 0.32354 1.8818</code></pre> +SD.k2 0.03296 -2.50143 2.5673 +SD.g 1.10266 0.32371 1.8816</code></pre> <p>While the other parameters converge to credible values, the variance of k2 (<code>omega2.k2</code>) converges to a very small value. The printout of the <code>saem.mmkin</code> model shows that the estimated standard deviation of k2 across the population of soils (<code>SD.k2</code>) is ill-defined, indicating overparameterisation of this model.</p> <p>When the DFOP model is fitted with the two-component error model, we also observe that the estimated variance of k2 becomes very small, while being ill-defined, as illustrated by the excessive confidence interval of <code>SD.k2</code>.</p> -<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">f_parent_saemix_dfop_tc</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> <span> control <span class="op">=</span> <span class="va">saemix_control</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span> <span><span class="va">f_parent_saemix_dfop_tc_moreiter</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> -<span> control <span class="op">=</span> <span class="va">saemix_control_moreiter</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span></code></pre></div> -<pre><code>Likelihood cannot be computed by Importance Sampling.</code></pre> -<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></code></pre></div> +<span> control <span class="op">=</span> <span class="va">saemix_control_moreiter</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc-1.png" width="700"></p> -<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">)</span></span></code></pre></div> <pre><code>Kinetic nonlinear mixed-effects model fit by SAEM Structural model: @@ -318,21 +313,21 @@ Likelihood computed by importance sampling 666 664 -323 Fitted parameters: - estimate lower upper -DMTA_0 9.82e+01 96.27937 100.1783 -k1 6.41e-02 0.03333 0.0948 -k2 8.56e-03 0.00608 0.0110 -g 9.55e-01 0.91440 0.9947 -a.1 1.07e+00 0.86542 1.2647 -b.1 2.96e-02 0.02258 0.0367 -SD.DMTA_0 2.04e+00 0.40629 3.6678 -SD.k1 5.98e-01 0.25796 0.9373 -SD.k2 5.28e-04 -58.93251 58.9336 -SD.g 1.04e+00 0.36509 1.7083</code></pre> + estimate lower upper +DMTA_0 98.27617 96.3088 100.2436 +k1 0.06437 0.0337 0.0950 +k2 0.00880 0.0063 0.0113 +g 0.95249 0.9100 0.9949 +a.1 1.06161 0.8625 1.2607 +b.1 0.02967 0.0226 0.0367 +SD.DMTA_0 2.06075 0.4187 3.7028 +SD.k1 0.59357 0.2561 0.9310 +SD.k2 0.00292 -10.2960 10.3019 +SD.g 1.05725 0.3808 1.7337</code></pre> <p>Doubling the number of iterations in the first phase of the algorithm leads to a slightly lower likelihood, and therefore to slightly higher AIC and BIC values. With even more iterations, the algorithm stops with an error message. This is related to the variance of k2 approximating zero and has been submitted as a <a href="https://github.com/saemixdevelopment/saemixextension/issues/29" class="external-link">bug to the saemix package</a>, as the algorithm does not converge in this case.</p> <p>An alternative way to fit DFOP in combination with the two-component error model is to use the model formulation with transformed parameters as used per default in mkin. When using this option, convergence is slower, but eventually the algorithm stops as well with the same error message.</p> <p>The four combinations (SFO/const, SFO/tc, DFOP/const and DFOP/tc) and the version with increased iterations can be compared using the model comparison function of the saemix package:</p> -<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">AIC_parent_saemix</span> <span class="op"><-</span> <span class="fu">saemix</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/compare.saemix.html" class="external-link">compare.saemix</a></span><span class="op">(</span></span> <span> <span class="va">f_parent_saemix_sfo_const</span><span class="op">$</span><span class="va">so</span>,</span> <span> <span class="va">f_parent_saemix_sfo_tc</span><span class="op">$</span><span class="va">so</span>,</span> @@ -340,7 +335,7 @@ SD.g 1.04e+00 0.36509 1.7083</code></pre> <span> <span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span>,</span> <span> <span class="va">f_parent_saemix_dfop_tc_moreiter</span><span class="op">$</span><span class="va">so</span><span class="op">)</span></span></code></pre></div> <pre><code>Likelihoods calculated by importance sampling</code></pre> -<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="va">AIC_parent_saemix</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span> <span> <span class="st">"SFO const"</span>, <span class="st">"SFO tc"</span>, <span class="st">"DFOP const"</span>, <span class="st">"DFOP tc"</span>, <span class="st">"DFOP tc more iterations"</span><span class="op">)</span></span> <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">AIC_parent_saemix</span><span class="op">)</span></span></code></pre></div> @@ -348,10 +343,10 @@ SD.g 1.04e+00 0.36509 1.7083</code></pre> SFO const 796.38 795.34 SFO tc 798.38 797.13 DFOP const 705.75 703.88 -DFOP tc 665.72 663.63 -DFOP tc more iterations NaN NaN</code></pre> +DFOP tc 665.65 663.57 +DFOP tc more iterations 665.88 663.80</code></pre> <p>In order to check the influence of the likelihood calculation algorithms implemented in saemix, the likelihood from Gaussian quadrature is added to the best fit, and the AIC values obtained from the three methods are compared.</p> -<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span> <span class="op"><-</span></span> <span> <span class="fu">saemix</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/llgq.saemix.html" class="external-link">llgq.saemix</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span><span class="op">)</span></span> <span><span class="va">AIC_parent_saemix_methods</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span> @@ -361,11 +356,11 @@ DFOP tc more iterations NaN NaN</code></pre> <span><span class="op">)</span></span> <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">AIC_parent_saemix_methods</span><span class="op">)</span></span></code></pre></div> <pre><code> is gq lin -665.72 665.88 665.15 </code></pre> +665.65 665.68 665.11 </code></pre> <p>The AIC values based on importance sampling and Gaussian quadrature are very similar. Using linearisation is known to be less accurate, but still gives a similar value.</p> <p>In order to illustrate that the comparison of the three method depends on the degree of convergence obtained in the fit, the same comparison is shown below for the fit using the defaults for the number of iterations and the number of MCMC chains.</p> <p>When using OpenBlas for linear algebra, there is a large difference in the values obtained with Gaussian quadrature, so the larger number of iterations makes a lot of difference. When using the LAPACK version coming with Debian Bullseye, the AIC based on Gaussian quadrature is almost the same as the one obtained with the other methods, also when using defaults for the fit.</p> -<div class="sourceCode" id="cb36"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">f_parent_saemix_dfop_tc_defaults</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span></span> <span><span class="va">f_parent_saemix_dfop_tc_defaults</span><span class="op">$</span><span class="va">so</span> <span class="op"><-</span></span> <span> <span class="fu">saemix</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/llgq.saemix.html" class="external-link">llgq.saemix</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc_defaults</span><span class="op">$</span><span class="va">so</span><span class="op">)</span></span> @@ -376,14 +371,14 @@ DFOP tc more iterations NaN NaN</code></pre> <span><span class="op">)</span></span> <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">AIC_parent_saemix_methods_defaults</span><span class="op">)</span></span></code></pre></div> <pre><code> is gq lin -668.91 663.61 667.40 </code></pre> +669.77 669.36 670.95 </code></pre> </div> </div> <div class="section level3"> <h3 id="comparison">Comparison<a class="anchor" aria-label="anchor" href="#comparison"></a> </h3> <p>The following table gives the AIC values obtained with both backend packages using the same control parameters (800 iterations burn-in, 300 iterations second phase, 15 chains).</p> -<div class="sourceCode" id="cb38"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">AIC_all</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span> <span> check.names <span class="op">=</span> <span class="cn">FALSE</span>,</span> <span> <span class="st">"Degradation model"</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"SFO"</span>, <span class="st">"DFOP"</span>, <span class="st">"DFOP"</span><span class="op">)</span>,</span> @@ -408,7 +403,7 @@ DFOP tc more iterations NaN NaN</code></pre> <td align="left">SFO</td> <td align="left">const</td> <td align="right">796.60</td> -<td align="right">794.17</td> +<td align="right">796.60</td> <td align="right">796.38</td> </tr> <tr class="even"> @@ -422,15 +417,15 @@ DFOP tc more iterations NaN NaN</code></pre> <td align="left">DFOP</td> <td align="left">const</td> <td align="right">NA</td> -<td align="right">704.95</td> +<td align="right">671.98</td> <td align="right">705.75</td> </tr> <tr class="even"> <td align="left">DFOP</td> <td align="left">tc</td> <td align="right">671.91</td> -<td align="right">665.15</td> -<td align="right">665.72</td> +<td align="right">665.11</td> +<td align="right">665.65</td> </tr> </tbody> </table> @@ -445,15 +440,15 @@ DFOP tc more iterations NaN NaN</code></pre> <div class="section level2"> <h2 id="session-info">Session Info<a class="anchor" aria-label="anchor" href="#session-info"></a> </h2> -<div class="sourceCode" id="cb39"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/sessionInfo.html" class="external-link">sessionInfo</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> -<pre><code>R version 4.2.1 (2022-06-23) +<pre><code>R version 4.2.2 (2022-10-31) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Debian GNU/Linux 11 (bullseye) Matrix products: default -BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0 -LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0 +BLAS: /usr/lib/x86_64-linux-gnu/openblas-serial/libblas.so.3 +LAPACK: /usr/lib/x86_64-linux-gnu/openblas-serial/libopenblas-r0.3.13.so locale: [1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C @@ -464,28 +459,27 @@ locale: [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C attached base packages: -[1] parallel stats graphics grDevices utils datasets methods -[8] base +[1] stats graphics grDevices utils datasets methods base other attached packages: -[1] nlme_3.1-158 mkin_1.1.1 knitr_1.39 +[1] nlme_3.1-160 mkin_1.2.0 knitr_1.40 loaded via a namespace (and not attached): - [1] deSolve_1.32 zoo_1.8-10 tidyselect_1.1.2 xfun_0.31 - [5] bslib_0.3.1 purrr_0.3.4 lattice_0.20-45 colorspace_2.0-3 - [9] vctrs_0.4.1 generics_0.1.3 htmltools_0.5.2 yaml_2.3.5 -[13] utf8_1.2.2 rlang_1.0.3 pkgdown_2.0.5 saemix_3.0 -[17] jquerylib_0.1.4 pillar_1.7.0 glue_1.6.2 lifecycle_1.0.1 -[21] stringr_1.4.0 munsell_0.5.0 gtable_0.3.0 ragg_1.2.2 -[25] memoise_2.0.1 evaluate_0.15 npde_3.2 fastmap_1.1.0 -[29] lmtest_0.9-40 fansi_1.0.3 highr_0.9 scales_1.2.0 -[33] cachem_1.0.6 desc_1.4.1 jsonlite_1.8.0 systemfonts_1.0.4 -[37] fs_1.5.2 textshaping_0.3.6 gridExtra_2.3 ggplot2_3.3.6 -[41] digest_0.6.29 stringi_1.7.6 dplyr_1.0.9 grid_4.2.1 -[45] rprojroot_2.0.3 cli_3.3.0 tools_4.2.1 magrittr_2.0.3 -[49] sass_0.4.1 tibble_3.1.7 crayon_1.5.1 pkgconfig_2.0.3 -[53] ellipsis_0.3.2 rmarkdown_2.14 R6_2.5.1 mclust_5.4.10 -[57] compiler_4.2.1 </code></pre> + [1] deSolve_1.34 zoo_1.8-11 tidyselect_1.2.0 xfun_0.33 + [5] bslib_0.4.0 purrr_0.3.5 lattice_0.20-45 colorspace_2.0-3 + [9] vctrs_0.5.0 generics_0.1.3 htmltools_0.5.3 yaml_2.3.6 +[13] utf8_1.2.2 rlang_1.0.6 pkgdown_2.0.6 saemix_3.2 +[17] jquerylib_0.1.4 pillar_1.8.1 glue_1.6.2 DBI_1.1.3 +[21] lifecycle_1.0.3 stringr_1.4.1 munsell_0.5.0 gtable_0.3.1 +[25] ragg_1.2.2 memoise_2.0.1 evaluate_0.18 npde_3.2 +[29] fastmap_1.1.0 lmtest_0.9-40 parallel_4.2.2 fansi_1.0.3 +[33] highr_0.9 scales_1.2.1 cachem_1.0.6 desc_1.4.2 +[37] jsonlite_1.8.3 systemfonts_1.0.4 fs_1.5.2 textshaping_0.3.6 +[41] gridExtra_2.3 ggplot2_3.4.0 digest_0.6.30 stringi_1.7.8 +[45] dplyr_1.0.10 grid_4.2.2 rprojroot_2.0.3 cli_3.4.1 +[49] tools_4.2.2 magrittr_2.0.3 sass_0.4.2 tibble_3.1.8 +[53] pkgconfig_2.0.3 assertthat_0.2.1 rmarkdown_2.16 R6_2.5.1 +[57] mclust_6.0.0 compiler_4.2.2 </code></pre> </div> <div class="section level2"> <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a> @@ -522,7 +516,7 @@ loaded via a namespace (and not attached): <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.5.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer> diff --git a/docs/articles/web_only/multistart.html b/docs/articles/web_only/multistart.html new file mode 100644 index 00000000..720c6742 --- /dev/null +++ b/docs/articles/web_only/multistart.html @@ -0,0 +1,200 @@ +<!DOCTYPE html> +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"> +<head> +<meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> +<meta charset="utf-8"> +<meta http-equiv="X-UA-Compatible" content="IE=edge"> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<title>Short demo of the multistart method • mkin</title> +<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" 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href="../../index.html">mkin</a> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + </span> + </div> + + <div id="navbar" class="navbar-collapse collapse"> + <ul class="nav navbar-nav"> +<li> + <a href="../../reference/index.html">Functions and data</a> +</li> +<li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> + Articles + + <span class="caret"></span> + </a> + <ul class="dropdown-menu" role="menu"> +<li> + <a href="../../articles/mkin.html">Introduction to mkin</a> + </li> + <li> + <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> + </li> + <li> + <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> + </li> + <li> + <a href="../../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with 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href="https://github.com/jranke/mkin/" class="external-link"> + <span class="fab fa-github fa-lg"></span> + + </a> +</li> + </ul> +</div> +<!--/.nav-collapse --> + </div> +<!--/.container --> +</div> +<!--/.navbar --> + + + + </header><script src="multistart_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + <div class="col-md-9 contents"> + <div class="page-header toc-ignore"> + <h1 data-toc-skip>Short demo of the multistart method</h1> + <h4 data-toc-skip class="author">Johannes Ranke</h4> + + <h4 data-toc-skip class="date">Last change 26 September 2022 (rebuilt 2022-11-17)</h4> + + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/multistart.rmd" class="external-link"><code>vignettes/web_only/multistart.rmd</code></a></small> + <div class="hidden name"><code>multistart.rmd</code></div> + + </div> + + + +<p>The dimethenamid data from 2018 from seven soils is used as example data in this vignette.</p> +<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span> +<span><span class="va">dmta_ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span> +<span> <span class="va">ds_i</span> <span class="op"><-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span> +<span> <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op"><-</span> <span class="st">"DMTA"</span></span> +<span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op"><-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span></span> +<span> <span class="va">ds_i</span></span> +<span><span class="op">}</span><span class="op">)</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span> +<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span> +<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span></span></code></pre></div> +<p>First, we check the DFOP model with the two-component error model and random effects for all degradation parameters.</p> +<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_mmkin</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">dmta_ds</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, cores <span class="op">=</span> <span class="fl">7</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span><span class="va">f_saem_full</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">)</span></span> +<span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_full</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## [1] "sd(log_k2)"</span></span></code></pre> +<p>We see that not all variability parameters are identifiable. The <code>illparms</code> function tells us that the confidence interval for the standard deviation of ‘log_k2’ includes zero. We check this assessment using multiple runs with different starting values.</p> +<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_saem_full_multi</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem_full</span>, n <span class="op">=</span> <span class="fl">16</span>, cores <span class="op">=</span> <span class="fl">16</span><span class="op">)</span></span> +<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_full_multi</span><span class="op">)</span></span></code></pre></div> +<p><img src="multistart_files/figure-html/unnamed-chunk-3-1.png" width="700"></p> +<p>This confirms that the variance of k2 is the most problematic parameter, so we reduce the parameter distribution model by removing the intersoil variability for k2.</p> +<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_saem_reduced</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_full</span>, no_random_effect <span class="op">=</span> <span class="st">"log_k2"</span><span class="op">)</span></span> +<span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_reduced</span><span class="op">)</span></span> +<span><span class="va">f_saem_reduced_multi</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem_reduced</span>, n <span class="op">=</span> <span class="fl">16</span>, cores <span class="op">=</span> <span class="fl">16</span><span class="op">)</span></span> +<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span>, lpos <span class="op">=</span> <span class="st">"topright"</span><span class="op">)</span></span></code></pre></div> +<p><img src="multistart_files/figure-html/unnamed-chunk-4-1.png" width="700"></p> +<p>The results confirm that all remaining parameters can be determined with sufficient certainty.</p> +<p>We can also analyse the log-likelihoods obtained in the multiple runs:</p> +<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/llhist.html">llhist</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span><span class="op">)</span></span></code></pre></div> +<p><img src="multistart_files/figure-html/unnamed-chunk-5-1.png" width="700"></p> +<p>The parameter histograms can be further improved by excluding the result with the low likelihood.</p> +<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span>, lpos <span class="op">=</span> <span class="st">"topright"</span>, llmin <span class="op">=</span> <span class="op">-</span><span class="fl">326</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<p><img src="multistart_files/figure-html/unnamed-chunk-6-1.png" width="700"></p> +<p>We can use the <code>anova</code> method to compare the models, including a likelihood ratio test if the models are nested.</p> +<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_full</span>, <span class="fu"><a href="../../reference/multistart.html">best</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span><span class="op">)</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Data: 155 observations of 1 variable(s) grouped in 6 datasets</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## npar AIC BIC Lik Chisq Df Pr(>Chisq)</span></span> +<span><span class="co">## best(f_saem_reduced_multi) 9 663.69 661.82 -322.85 </span></span> +<span><span class="co">## f_saem_full 10 669.77 667.69 -324.89 0 1 1</span></span></code></pre> +<p>While AIC and BIC are lower for the reduced model, the likelihood ratio test does not indicate a significant difference between the fits.</p> + </div> + + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + + </div> + +</div> + + + + <footer><div class="copyright"> + <p></p> +<p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +</div> + + </footer> +</div> + + + + + + + </body> +</html> diff --git a/docs/articles/web_only/multistart_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/web_only/multistart_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 00000000..ca349fd6 --- /dev/null +++ b/docs/articles/web_only/multistart_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo 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data-toc-skip>Benchmark timings for saem.mmkin</h1> + <h4 data-toc-skip class="author">Johannes Ranke</h4> + + <h4 data-toc-skip class="date">Last change 14 November 2022 (rebuilt 2022-11-17)</h4> + + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/saem_benchmarks.rmd" class="external-link"><code>vignettes/web_only/saem_benchmarks.rmd</code></a></small> + <div class="hidden name"><code>saem_benchmarks.rmd</code></div> + + </div> + + + +<p>Each system is characterized by operating system type, CPU type, mkin version, saemix version and R version. A compiler was available, so if no analytical solution was available, compiled ODE models are used.</p> +<p>Every fit is only performed once, so the accuracy of the benchmarks is limited.</p> +<p>For the initial mmkin fits, we use all available cores.</p> +<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">n_cores</span> <span class="op"><-</span> <span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<div class="section level2"> +<h2 id="test-data">Test data<a class="anchor" aria-label="anchor" href="#test-data"></a> +</h2> +<p>Please refer to the vignette <code>dimethenamid_2018</code> for an explanation of the following preprocessing.</p> +<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">dmta_ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span> +<span> <span class="va">ds_i</span> <span class="op"><-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span> +<span> <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op"><-</span> <span class="st">"DMTA"</span></span> +<span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op"><-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span></span> +<span> <span class="va">ds_i</span></span> +<span><span class="op">}</span><span class="op">)</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span> +<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span> +<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span></span> +<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span></span></code></pre></div> +</div> +<div class="section level2"> +<h2 id="test-cases">Test cases<a class="anchor" aria-label="anchor" href="#test-cases"></a> +</h2> +<div class="section level3"> +<h3 id="parent-only">Parent only<a class="anchor" aria-label="anchor" href="#parent-only"></a> +</h3> +<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">parent_mods</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"DFOP"</span>, <span class="st">"SFORB"</span>, <span class="st">"HS"</span><span class="op">)</span></span> +<span><span class="va">parent_sep_const</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="va">parent_mods</span>, <span class="va">dmta_ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, cores <span class="op">=</span> <span class="va">n_cores</span><span class="op">)</span></span> +<span><span class="va">parent_sep_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">parent_sep_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span> +<span></span> +<span><span class="va">t1</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">sfo_const</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">parent_sep_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span> +<span><span class="va">t2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">dfop_const</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">parent_sep_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span> +<span><span class="va">t3</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">sforb_const</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">parent_sep_const</span><span class="op">[</span><span class="st">"SFORB"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span> +<span><span class="va">t4</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">hs_const</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">parent_sep_const</span><span class="op">[</span><span class="st">"HS"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span> +<span><span class="va">t5</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">sfo_tc</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">parent_sep_tc</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span> +<span><span class="va">t6</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">dfop_tc</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">parent_sep_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span> +<span><span class="va">t7</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">sforb_tc</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">parent_sep_tc</span><span class="op">[</span><span class="st">"SFORB"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span> +<span><span class="va">t8</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">hs_tc</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">parent_sep_tc</span><span class="op">[</span><span class="st">"HS"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span></code></pre></div> +<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span></span> +<span> <span class="va">sfo_const</span>, <span class="va">dfop_const</span>, <span class="va">sforb_const</span>, <span class="va">hs_const</span>,</span> +<span> <span class="va">sfo_tc</span>, <span class="va">dfop_tc</span>, <span class="va">sforb_tc</span>, <span class="va">hs_tc</span><span class="op">)</span> <span class="op">|></span> <span class="fu">kable</span><span class="op">(</span>, digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="right">npar</th> +<th align="right">AIC</th> +<th align="right">BIC</th> +<th align="right">Lik</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">sfo_const</td> +<td align="right">5</td> +<td align="right">796.3</td> +<td align="right">795.3</td> +<td align="right">-393.2</td> +</tr> +<tr class="even"> +<td align="left">sfo_tc</td> +<td align="right">6</td> +<td align="right">798.3</td> +<td align="right">797.1</td> +<td align="right">-393.2</td> +</tr> +<tr class="odd"> +<td align="left">dfop_const</td> +<td align="right">9</td> +<td align="right">709.4</td> +<td align="right">707.5</td> +<td align="right">-345.7</td> +</tr> +<tr class="even"> +<td align="left">sforb_const</td> +<td align="right">9</td> +<td align="right">710.0</td> +<td align="right">708.1</td> +<td align="right">-346.0</td> +</tr> +<tr class="odd"> +<td align="left">hs_const</td> +<td align="right">9</td> +<td align="right">713.7</td> +<td align="right">711.8</td> +<td align="right">-347.8</td> +</tr> +<tr class="even"> +<td align="left">dfop_tc</td> +<td align="right">10</td> +<td align="right">669.8</td> +<td align="right">667.7</td> +<td align="right">-324.9</td> +</tr> +<tr class="odd"> +<td align="left">sforb_tc</td> +<td align="right">10</td> +<td align="right">662.8</td> +<td align="right">660.7</td> +<td align="right">-321.4</td> +</tr> +<tr class="even"> +<td align="left">hs_tc</td> +<td align="right">10</td> +<td align="right">667.3</td> +<td align="right">665.2</td> +<td align="right">-323.6</td> +</tr> +</tbody> +</table> +<p>The above model comparison suggests to use the SFORB model with two-component error. For comparison, we keep the DFOP model with two-component error, as it competes with SFORB for biphasic curves.</p> +<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">dfop_tc</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## [1] "sd(log_k2)"</span></span></code></pre> +<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">sforb_tc</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## [1] "sd(log_k_DMTA_bound_free)"</span></span></code></pre> +<p>For these two models, random effects for the transformed parameters <code>k2</code> and <code>k_DMTA_bound_free</code> could not be quantified.</p> +</div> +<div class="section level3"> +<h3 id="one-metabolite">One metabolite<a class="anchor" aria-label="anchor" href="#one-metabolite"></a> +</h3> +<p>We remove parameters that were found to be ill-defined in the parent only fits.</p> +<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">one_met_mods</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span> +<span> DFOP_SFO <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"M23"</span><span class="op">)</span>,</span> +<span> M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> SFORB_SFO <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="st">"M23"</span><span class="op">)</span>,</span> +<span> M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span> +<span></span> +<span><span class="va">one_met_sep_const</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="va">one_met_mods</span>, <span class="va">dmta_ds</span>, error_model <span class="op">=</span> <span class="st">"const"</span>,</span> +<span> cores <span class="op">=</span> <span class="va">n_cores</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span><span class="va">one_met_sep_tc</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="va">one_met_mods</span>, <span class="va">dmta_ds</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>,</span> +<span> cores <span class="op">=</span> <span class="va">n_cores</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span></span> +<span><span class="va">t9</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">dfop_sfo_tc</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">one_met_sep_tc</span><span class="op">[</span><span class="st">"DFOP_SFO"</span>, <span class="op">]</span>,</span> +<span> no_random_effect <span class="op">=</span> <span class="st">"log_k2"</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span> +<span><span class="va">t10</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">sforb_sfo_tc</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">one_met_sep_tc</span><span class="op">[</span><span class="st">"SFORB_SFO"</span>, <span class="op">]</span>,</span> +<span> no_random_effect <span class="op">=</span> <span class="st">"log_k_DMTA_bound_free"</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span></code></pre></div> +</div> +<div class="section level3"> +<h3 id="three-metabolites">Three metabolites<a class="anchor" aria-label="anchor" href="#three-metabolites"></a> +</h3> +<p>For the case of three metabolites, we only keep the SFORB model in order to limit the time for compiling this vignette, and as fitting in parallel may disturb the benchmark. Again, we do not include random effects that were ill-defined in previous fits of subsets of the degradation model.</p> +<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">sforb_sfo_tc</span><span class="op">)</span></span></code></pre></div> +<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">three_met_mods</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span> +<span> SFORB_SFO3_plus <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> M27 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> M31 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span> +<span></span> +<span><span class="va">three_met_sep_tc</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="va">three_met_mods</span>, <span class="va">dmta_ds</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>,</span> +<span> cores <span class="op">=</span> <span class="va">n_cores</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span></span> +<span><span class="va">t11</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">sforb_sfo3_plus_const</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">three_met_sep_tc</span><span class="op">[</span><span class="st">"SFORB_SFO3_plus"</span>, <span class="op">]</span>,</span> +<span> no_random_effect <span class="op">=</span> <span class="st">"log_k_DMTA_bound_free"</span><span class="op">)</span><span class="op">)</span><span class="op">[[</span><span class="st">"elapsed"</span><span class="op">]</span><span class="op">]</span></span></code></pre></div> +</div> +</div> +<div class="section level2"> +<h2 id="results">Results<a class="anchor" aria-label="anchor" href="#results"></a> +</h2> +<p>Benchmarks for all available error models are shown. They are intended for improving mkin, not for comparing CPUs or operating systems. All trademarks belong to their respective owners.</p> +<div class="section level3"> +<h3 id="parent-only-1">Parent only<a class="anchor" aria-label="anchor" href="#parent-only-1"></a> +</h3> +<p>Constant variance for SFO, DFOP, SFORB and HS.</p> +<table class="table"> +<thead><tr class="header"> +<th align="left">CPU</th> +<th align="left">OS</th> +<th align="left">mkin</th> +<th align="left">saemix</th> +<th align="right">t1</th> +<th align="right">t2</th> +<th align="right">t3</th> +<th align="right">t4</th> +</tr></thead> +<tbody><tr class="odd"> +<td align="left">Ryzen 7 1700</td> +<td align="left">Linux</td> +<td align="left">1.2.0</td> +<td align="left">3.2</td> +<td align="right">2.14</td> +<td align="right">4.626</td> +<td align="right">4.328</td> +<td align="right">4.998</td> +</tr></tbody> +</table> +<p>Two-component error fits for SFO, DFOP, SFORB and HS.</p> +<table class="table"> +<thead><tr class="header"> +<th align="left">CPU</th> +<th align="left">OS</th> +<th align="left">mkin</th> +<th align="left">saemix</th> +<th align="right">t5</th> +<th align="right">t6</th> +<th align="right">t7</th> +<th align="right">t8</th> +</tr></thead> +<tbody><tr class="odd"> +<td align="left">Ryzen 7 1700</td> +<td align="left">Linux</td> +<td align="left">1.2.0</td> +<td align="left">3.2</td> +<td align="right">5.678</td> +<td align="right">7.441</td> +<td align="right">8</td> +<td align="right">7.98</td> +</tr></tbody> +</table> +</div> +<div class="section level3"> +<h3 id="one-metabolite-1">One metabolite<a class="anchor" aria-label="anchor" href="#one-metabolite-1"></a> +</h3> +<p>Two-component error for DFOP-SFO and SFORB-SFO.</p> +<table class="table"> +<thead><tr class="header"> +<th align="left">CPU</th> +<th align="left">OS</th> +<th align="left">mkin</th> +<th align="left">saemix</th> +<th align="right">t9</th> +<th align="right">t10</th> +</tr></thead> +<tbody><tr class="odd"> +<td align="left">Ryzen 7 1700</td> +<td align="left">Linux</td> +<td align="left">1.2.0</td> +<td align="left">3.2</td> +<td align="right">24.465</td> +<td align="right">800.266</td> +</tr></tbody> +</table> +</div> +<div class="section level3"> +<h3 id="three-metabolites-1">Three metabolites<a class="anchor" aria-label="anchor" href="#three-metabolites-1"></a> +</h3> +<p>Two-component error for SFORB-SFO3-plus</p> +<table class="table"> +<thead><tr class="header"> +<th align="left">CPU</th> +<th align="left">OS</th> +<th align="left">mkin</th> +<th align="left">saemix</th> +<th align="right">t11</th> +</tr></thead> +<tbody><tr class="odd"> +<td align="left">Ryzen 7 1700</td> +<td align="left">Linux</td> +<td align="left">1.2.0</td> +<td align="left">3.2</td> +<td align="right">1289.198</td> +</tr></tbody> +</table> +</div> +</div> + </div> + + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + + <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2> + </nav> +</div> + +</div> + + + + <footer><div class="copyright"> + <p></p> +<p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +</div> + + </footer> +</div> + + + + + + + </body> +</html> diff --git a/docs/articles/web_only/saem_benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/web_only/saem_benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 00000000..ca349fd6 --- /dev/null +++ b/docs/articles/web_only/saem_benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js @@ -0,0 +1,15 @@ +// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> +// v0.0.1 +// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. + +document.addEventListener('DOMContentLoaded', function() { + const codeList = document.getElementsByClassName("sourceCode"); + for (var i = 0; i < codeList.length; i++) { + var linkList = codeList[i].getElementsByTagName('a'); + for (var j = 0; j < linkList.length; j++) { + if (linkList[j].innerHTML === "") { + linkList[j].setAttribute('aria-hidden', 'true'); + } + } + } +}); diff --git a/docs/authors.html b/docs/authors.html index ab436c86..7afe3000 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.1</span> </span> </div> @@ -44,19 +44,25 @@ <a href="articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -109,13 +115,13 @@ <p>Ranke J (2022). <em>mkin: Kinetic Evaluation of Chemical Degradation Data</em>. -R package version 1.1.2, <a href="https://pkgdown.jrwb.de/mkin/">https://pkgdown.jrwb.de/mkin/</a>. +R package version 1.2.1, <a href="https://pkgdown.jrwb.de/mkin/">https://pkgdown.jrwb.de/mkin/</a>. </p> <pre>@Manual{, title = {mkin: Kinetic Evaluation of Chemical Degradation Data}, author = {Johannes Ranke}, year = {2022}, - note = {R package version 1.1.2}, + note = {R package version 1.2.1}, url = {https://pkgdown.jrwb.de/mkin/}, }</pre> diff --git a/docs/dev/articles/2022_wp_1.1_dmta_parent.html b/docs/dev/articles/2022_wp_1.1_dmta_parent.html new file mode 100644 index 00000000..61bb81d3 --- /dev/null +++ b/docs/dev/articles/2022_wp_1.1_dmta_parent.html @@ -0,0 +1,2177 @@ +<!DOCTYPE html> +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"> +<head> +<meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> +<meta charset="utf-8"> +<meta http-equiv="X-UA-Compatible" content="IE=edge"> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<title>Work package 1.1: Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P • mkin</title> +<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"> +<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"> +<script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" 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Attachment examples</a> + </li> + <li> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul> +</li> +<li> + <a href="../news/index.html">News</a> +</li> + </ul> +<ul class="nav navbar-nav navbar-right"> +<li> + <a href="https://github.com/jranke/mkin/" class="external-link"> + <span class="fab fa-github fa-lg"></span> + + </a> +</li> + </ul> +</div> +<!--/.nav-collapse --> + </div> +<!--/.container --> +</div> +<!--/.navbar --> + + + + </header><div class="row"> + <div class="col-md-9 contents"> + <div class="page-header toc-ignore"> + <h1 data-toc-skip>Work package 1.1: Testing hierarchical parent +degradation kinetics with residue data on dimethenamid and +dimethenamid-P</h1> + <h4 data-toc-skip class="author">Johannes +Ranke</h4> + + <h4 data-toc-skip class="date">Last change on 5 January +2022, last compiled on 5 Januar 2023</h4> + + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/2022_wp_1.1_dmta_parent.rmd" class="external-link"><code>vignettes/2022_wp_1.1_dmta_parent.rmd</code></a></small> + <div class="hidden name"><code>2022_wp_1.1_dmta_parent.rmd</code></div> + + </div> + + + +<div class="section level2"> +<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a> +</h2> +<p>The purpose of this document is to demonstrate how nonlinear +hierarchical models (NLHM) based on the parent degradation models SFO, +FOMC, DFOP and HS can be fitted with the mkin package.</p> +<p>The mkin package is used in version 1.2.2. It contains the test data +and the functions used in the evaluations. The <code>saemix</code> +package is used as a backend for fitting the NLHM, but is also loaded to +make the convergence plot function available.</p> +<p>This document is processed with the <code>knitr</code> package, which +also provides the <code>kable</code> function that is used to improve +the display of tabular data in R markdown documents. For parallel +processing, the <code>parallel</code> package is used.</p> +<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span> +<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://yihui.org/knitr/" class="external-link">knitr</a></span><span class="op">)</span></span> +<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span> +<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">parallel</span><span class="op">)</span></span> +<span><span class="va">n_cores</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span></span> +<span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="op">{</span></span> +<span> <span class="va">cl</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span> +<span><span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span> +<span> <span class="va">cl</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makeForkCluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span> +<span><span class="op">}</span></span></code></pre></div> +<div class="section level3"> +<h3 id="preprocessing-of-test-data">Preprocessing of test data<a class="anchor" aria-label="anchor" href="#preprocessing-of-test-data"></a> +</h3> +<p>The test data are available in the mkin package as an object of class +<code>mkindsg</code> (mkin dataset group) under the identifier +<code>dimethenamid_2018</code>. The following preprocessing steps are +still necessary:</p> +<ul> +<li>The data available for the enantiomer dimethenamid-P (DMTAP) are +renamed to have the same substance name as the data for the racemic +mixture dimethenamid (DMTA). The reason for this is that no difference +between their degradation behaviour was identified in the EU risk +assessment.</li> +<li>The data for transformation products and unnecessary columns are +discarded</li> +<li>The observation times of each dataset are multiplied with the +corresponding normalisation factor also available in the dataset, in +order to make it possible to describe all datasets with a single set of +parameters that are independent of temperature</li> +<li>Finally, datasets observed in the same soil (<code>Elliot 1</code> +and <code>Elliot 2</code>) are combined, resulting in dimethenamid +(DMTA) data from six soils.</li> +</ul> +<p>The following commented R code performs this preprocessing.</p> +<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="co"># Apply a function to each of the seven datasets in the mkindsg object to create a list</span></span> +<span><span class="va">dmta_ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span> +<span> <span class="va">ds_i</span> <span class="op"><-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span> <span class="co"># Get a dataset</span></span> +<span> <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op"><-</span> <span class="st">"DMTA"</span> <span class="co"># Rename DMTAP to DMTA</span></span> +<span> <span class="va">ds_i</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">ds_i</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"DMTA"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">)</span> <span class="co"># Select data</span></span> +<span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op"><-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span> <span class="co"># Normalise time</span></span> +<span> <span class="va">ds_i</span> <span class="co"># Return the dataset</span></span> +<span><span class="op">}</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># Use dataset titles as names for the list elements</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># Combine data for Elliot soil to obtain a named list with six elements</span></span> +<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span> <span class="co">#</span></span> +<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span></span> +<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span></span></code></pre></div> +<p>The following tables show the 6 datasets.</p> +<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="kw">for</span> <span class="op">(</span><span class="va">ds_name</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span> +<span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="fu"><a href="../reference/mkin_long_to_wide.html">mkin_long_to_wide</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="va">ds_name</span><span class="op">]</span><span class="op">]</span><span class="op">)</span>,</span> +<span> caption <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="va">ds_name</span><span class="op">)</span>,</span> +<span> label <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="st">"tab:"</span>, <span class="va">ds_name</span><span class="op">)</span>, booktabs <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span> +<span> <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"\n\\clearpage\n"</span><span class="op">)</span></span> +<span><span class="op">}</span></span></code></pre></div> +<table class="table"> +<caption>Dataset Calke</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0</td> +<td align="right">95.8</td> +</tr> +<tr class="even"> +<td align="right">0</td> +<td align="right">98.7</td> +</tr> +<tr class="odd"> +<td align="right">14</td> +<td align="right">60.5</td> +</tr> +<tr class="even"> +<td align="right">30</td> +<td align="right">39.1</td> +</tr> +<tr class="odd"> +<td align="right">59</td> +<td align="right">15.2</td> +</tr> +<tr class="even"> +<td align="right">120</td> +<td align="right">4.8</td> +</tr> +<tr class="odd"> +<td align="right">120</td> +<td align="right">4.6</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Borstel</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">100.5</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">99.6</td> +</tr> +<tr class="odd"> +<td align="right">1.941295</td> +<td align="right">91.9</td> +</tr> +<tr class="even"> +<td align="right">1.941295</td> +<td align="right">91.3</td> +</tr> +<tr class="odd"> +<td align="right">6.794534</td> +<td align="right">81.8</td> +</tr> +<tr class="even"> +<td align="right">6.794534</td> +<td align="right">82.1</td> +</tr> +<tr class="odd"> +<td align="right">13.589067</td> +<td align="right">69.1</td> +</tr> +<tr class="even"> +<td align="right">13.589067</td> +<td align="right">68.0</td> +</tr> +<tr class="odd"> +<td align="right">27.178135</td> +<td align="right">51.4</td> +</tr> +<tr class="even"> +<td align="right">27.178135</td> +<td align="right">51.4</td> +</tr> +<tr class="odd"> +<td align="right">56.297565</td> +<td align="right">27.6</td> +</tr> +<tr class="even"> +<td align="right">56.297565</td> +<td align="right">26.8</td> +</tr> +<tr class="odd"> +<td align="right">86.387643</td> +<td align="right">15.7</td> +</tr> +<tr class="even"> +<td align="right">86.387643</td> +<td align="right">15.3</td> +</tr> +<tr class="odd"> +<td align="right">115.507073</td> +<td align="right">7.9</td> +</tr> +<tr class="even"> +<td align="right">115.507073</td> +<td align="right">8.1</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Flaach</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">96.5</td> +</tr> +<tr class="even"> +<td align="right">0.0000000</td> +<td align="right">96.8</td> +</tr> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">97.0</td> +</tr> +<tr class="even"> +<td align="right">0.6233856</td> +<td align="right">82.9</td> +</tr> +<tr class="odd"> +<td align="right">0.6233856</td> +<td align="right">86.7</td> +</tr> +<tr class="even"> +<td align="right">0.6233856</td> +<td align="right">87.4</td> +</tr> +<tr class="odd"> +<td align="right">1.8701567</td> +<td align="right">72.8</td> +</tr> +<tr class="even"> +<td align="right">1.8701567</td> +<td align="right">69.9</td> +</tr> +<tr class="odd"> +<td align="right">1.8701567</td> +<td align="right">71.9</td> +</tr> +<tr class="even"> +<td align="right">4.3636989</td> +<td align="right">51.4</td> +</tr> +<tr class="odd"> +<td align="right">4.3636989</td> +<td align="right">52.9</td> +</tr> +<tr class="even"> +<td align="right">4.3636989</td> +<td align="right">48.6</td> +</tr> +<tr class="odd"> +<td align="right">8.7273979</td> +<td align="right">28.5</td> +</tr> +<tr class="even"> +<td align="right">8.7273979</td> +<td align="right">27.3</td> +</tr> +<tr class="odd"> +<td align="right">8.7273979</td> +<td align="right">27.5</td> +</tr> +<tr class="even"> +<td align="right">13.0910968</td> +<td align="right">14.8</td> +</tr> +<tr class="odd"> +<td align="right">13.0910968</td> +<td align="right">13.4</td> +</tr> +<tr class="even"> +<td align="right">13.0910968</td> +<td align="right">14.4</td> +</tr> +<tr class="odd"> +<td align="right">17.4547957</td> +<td align="right">7.7</td> +</tr> +<tr class="even"> +<td align="right">17.4547957</td> +<td align="right">7.3</td> +</tr> +<tr class="odd"> +<td align="right">17.4547957</td> +<td align="right">8.1</td> +</tr> +<tr class="even"> +<td align="right">26.1821936</td> +<td align="right">2.0</td> +</tr> +<tr class="odd"> +<td align="right">26.1821936</td> +<td align="right">1.5</td> +</tr> +<tr class="even"> +<td align="right">26.1821936</td> +<td align="right">1.9</td> +</tr> +<tr class="odd"> +<td align="right">34.9095915</td> +<td align="right">1.3</td> +</tr> +<tr class="even"> +<td align="right">34.9095915</td> +<td align="right">1.0</td> +</tr> +<tr class="odd"> +<td align="right">34.9095915</td> +<td align="right">1.1</td> +</tr> +<tr class="even"> +<td align="right">43.6369893</td> +<td align="right">0.9</td> +</tr> +<tr class="odd"> +<td align="right">43.6369893</td> +<td align="right">0.7</td> +</tr> +<tr class="even"> +<td align="right">43.6369893</td> +<td align="right">0.7</td> +</tr> +<tr class="odd"> +<td align="right">52.3643872</td> +<td align="right">0.6</td> +</tr> +<tr class="even"> +<td align="right">52.3643872</td> +<td align="right">0.4</td> +</tr> +<tr class="odd"> +<td align="right">52.3643872</td> +<td align="right">0.5</td> +</tr> +<tr class="even"> +<td align="right">74.8062674</td> +<td align="right">0.4</td> +</tr> +<tr class="odd"> +<td align="right">74.8062674</td> +<td align="right">0.3</td> +</tr> +<tr class="even"> +<td align="right">74.8062674</td> +<td align="right">0.3</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset BBA 2.2</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">98.09</td> +</tr> +<tr class="even"> +<td align="right">0.0000000</td> +<td align="right">98.77</td> +</tr> +<tr class="odd"> +<td align="right">0.7678922</td> +<td align="right">93.52</td> +</tr> +<tr class="even"> +<td align="right">0.7678922</td> +<td align="right">92.03</td> +</tr> +<tr class="odd"> +<td align="right">2.3036765</td> +<td align="right">88.39</td> +</tr> +<tr class="even"> +<td align="right">2.3036765</td> +<td align="right">87.18</td> +</tr> +<tr class="odd"> +<td align="right">5.3752452</td> +<td align="right">69.38</td> +</tr> +<tr class="even"> +<td align="right">5.3752452</td> +<td align="right">71.06</td> +</tr> +<tr class="odd"> +<td align="right">10.7504904</td> +<td align="right">45.21</td> +</tr> +<tr class="even"> +<td align="right">10.7504904</td> +<td align="right">46.81</td> +</tr> +<tr class="odd"> +<td align="right">16.1257355</td> +<td align="right">30.54</td> +</tr> +<tr class="even"> +<td align="right">16.1257355</td> +<td align="right">30.07</td> +</tr> +<tr class="odd"> +<td align="right">21.5009807</td> +<td align="right">21.60</td> +</tr> +<tr class="even"> +<td align="right">21.5009807</td> +<td align="right">20.41</td> +</tr> +<tr class="odd"> +<td align="right">32.2514711</td> +<td align="right">9.10</td> +</tr> +<tr class="even"> +<td align="right">32.2514711</td> +<td align="right">9.70</td> +</tr> +<tr class="odd"> +<td align="right">43.0019614</td> +<td align="right">6.58</td> +</tr> +<tr class="even"> +<td align="right">43.0019614</td> +<td align="right">6.31</td> +</tr> +<tr class="odd"> +<td align="right">53.7524518</td> +<td align="right">3.47</td> +</tr> +<tr class="even"> +<td align="right">53.7524518</td> +<td align="right">3.52</td> +</tr> +<tr class="odd"> +<td align="right">64.5029421</td> +<td align="right">3.40</td> +</tr> +<tr class="even"> +<td align="right">64.5029421</td> +<td align="right">3.67</td> +</tr> +<tr class="odd"> +<td align="right">91.3791680</td> +<td align="right">1.62</td> +</tr> +<tr class="even"> +<td align="right">91.3791680</td> +<td align="right">1.62</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset BBA 2.3</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">99.33</td> +</tr> +<tr class="even"> +<td align="right">0.0000000</td> +<td align="right">97.44</td> +</tr> +<tr class="odd"> +<td align="right">0.6733938</td> +<td align="right">93.73</td> +</tr> +<tr class="even"> +<td align="right">0.6733938</td> +<td align="right">93.77</td> +</tr> +<tr class="odd"> +<td align="right">2.0201814</td> +<td align="right">87.84</td> +</tr> +<tr class="even"> +<td align="right">2.0201814</td> +<td align="right">89.82</td> +</tr> +<tr class="odd"> +<td align="right">4.7137565</td> +<td align="right">71.61</td> +</tr> +<tr class="even"> +<td align="right">4.7137565</td> +<td align="right">71.42</td> +</tr> +<tr class="odd"> +<td align="right">9.4275131</td> +<td align="right">45.60</td> +</tr> +<tr class="even"> +<td align="right">9.4275131</td> +<td align="right">45.42</td> +</tr> +<tr class="odd"> +<td align="right">14.1412696</td> +<td align="right">31.12</td> +</tr> +<tr class="even"> +<td align="right">14.1412696</td> +<td align="right">31.68</td> +</tr> +<tr class="odd"> +<td align="right">18.8550262</td> +<td align="right">23.20</td> +</tr> +<tr class="even"> +<td align="right">18.8550262</td> +<td align="right">24.13</td> +</tr> +<tr class="odd"> +<td align="right">28.2825393</td> +<td align="right">9.43</td> +</tr> +<tr class="even"> +<td align="right">28.2825393</td> +<td align="right">9.82</td> +</tr> +<tr class="odd"> +<td align="right">37.7100523</td> +<td align="right">7.08</td> +</tr> +<tr class="even"> +<td align="right">37.7100523</td> +<td align="right">8.64</td> +</tr> +<tr class="odd"> +<td align="right">47.1375654</td> +<td align="right">4.41</td> +</tr> +<tr class="even"> +<td align="right">47.1375654</td> +<td align="right">4.78</td> +</tr> +<tr class="odd"> +<td align="right">56.5650785</td> +<td align="right">4.92</td> +</tr> +<tr class="even"> +<td align="right">56.5650785</td> +<td align="right">5.08</td> +</tr> +<tr class="odd"> +<td align="right">80.1338612</td> +<td align="right">2.13</td> +</tr> +<tr class="even"> +<td align="right">80.1338612</td> +<td align="right">2.23</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Elliot</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">97.5</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">100.7</td> +</tr> +<tr class="odd"> +<td align="right">1.228478</td> +<td align="right">86.4</td> +</tr> +<tr class="even"> +<td align="right">1.228478</td> +<td align="right">88.5</td> +</tr> +<tr class="odd"> +<td align="right">3.685435</td> +<td align="right">69.8</td> +</tr> +<tr class="even"> +<td align="right">3.685435</td> +<td align="right">77.1</td> +</tr> +<tr class="odd"> +<td align="right">8.599349</td> +<td align="right">59.0</td> +</tr> +<tr class="even"> +<td align="right">8.599349</td> +<td align="right">54.2</td> +</tr> +<tr class="odd"> +<td align="right">17.198697</td> +<td align="right">31.3</td> +</tr> +<tr class="even"> +<td align="right">17.198697</td> +<td align="right">33.5</td> +</tr> +<tr class="odd"> +<td align="right">25.798046</td> +<td align="right">19.6</td> +</tr> +<tr class="even"> +<td align="right">25.798046</td> +<td align="right">20.9</td> +</tr> +<tr class="odd"> +<td align="right">34.397395</td> +<td align="right">13.3</td> +</tr> +<tr class="even"> +<td align="right">34.397395</td> +<td align="right">15.8</td> +</tr> +<tr class="odd"> +<td align="right">51.596092</td> +<td align="right">6.7</td> +</tr> +<tr class="even"> +<td align="right">51.596092</td> +<td align="right">8.7</td> +</tr> +<tr class="odd"> +<td align="right">68.794789</td> +<td align="right">8.8</td> +</tr> +<tr class="even"> +<td align="right">68.794789</td> +<td align="right">8.7</td> +</tr> +<tr class="odd"> +<td align="right">103.192184</td> +<td align="right">6.0</td> +</tr> +<tr class="even"> +<td align="right">103.192184</td> +<td align="right">4.4</td> +</tr> +<tr class="odd"> +<td align="right">146.188928</td> +<td align="right">3.3</td> +</tr> +<tr class="even"> +<td align="right">146.188928</td> +<td align="right">2.8</td> +</tr> +<tr class="odd"> +<td align="right">223.583066</td> +<td align="right">1.4</td> +</tr> +<tr class="even"> +<td align="right">223.583066</td> +<td align="right">1.8</td> +</tr> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">93.4</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">103.2</td> +</tr> +<tr class="odd"> +<td align="right">1.228478</td> +<td align="right">89.2</td> +</tr> +<tr class="even"> +<td align="right">1.228478</td> +<td align="right">86.6</td> +</tr> +<tr class="odd"> +<td align="right">3.685435</td> +<td align="right">78.2</td> +</tr> +<tr class="even"> +<td align="right">3.685435</td> +<td align="right">78.1</td> +</tr> +<tr class="odd"> +<td align="right">8.599349</td> +<td align="right">55.6</td> +</tr> +<tr class="even"> +<td align="right">8.599349</td> +<td align="right">53.0</td> +</tr> +<tr class="odd"> +<td align="right">17.198697</td> +<td align="right">33.7</td> +</tr> +<tr class="even"> +<td align="right">17.198697</td> +<td align="right">33.2</td> +</tr> +<tr class="odd"> +<td align="right">25.798046</td> +<td align="right">20.9</td> +</tr> +<tr class="even"> +<td align="right">25.798046</td> +<td align="right">19.9</td> +</tr> +<tr class="odd"> +<td align="right">34.397395</td> +<td align="right">18.2</td> +</tr> +<tr class="even"> +<td align="right">34.397395</td> +<td align="right">12.7</td> +</tr> +<tr class="odd"> +<td align="right">51.596092</td> +<td align="right">7.8</td> +</tr> +<tr class="even"> +<td align="right">51.596092</td> +<td align="right">9.0</td> +</tr> +<tr class="odd"> +<td align="right">68.794789</td> +<td align="right">11.4</td> +</tr> +<tr class="even"> +<td align="right">68.794789</td> +<td align="right">9.0</td> +</tr> +<tr class="odd"> +<td align="right">103.192184</td> +<td align="right">3.9</td> +</tr> +<tr class="even"> +<td align="right">103.192184</td> +<td align="right">4.4</td> +</tr> +<tr class="odd"> +<td align="right">146.188928</td> +<td align="right">2.6</td> +</tr> +<tr class="even"> +<td align="right">146.188928</td> +<td align="right">3.4</td> +</tr> +<tr class="odd"> +<td align="right">223.583066</td> +<td align="right">2.0</td> +</tr> +<tr class="even"> +<td align="right">223.583066</td> +<td align="right">1.7</td> +</tr> +</tbody> +</table> +</div> +</div> +<div class="section level2"> +<h2 id="separate-evaluations">Separate evaluations<a class="anchor" aria-label="anchor" href="#separate-evaluations"></a> +</h2> +<p>In order to obtain suitable starting parameters for the NLHM fits, +separate fits of the four models to the data for each soil are generated +using the <code>mmkin</code> function from the <code>mkin</code> +package. In a first step, constant variance is assumed. Convergence is +checked with the <code>status</code> function.</p> +<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">deg_mods</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>, <span class="st">"HS"</span><span class="op">)</span></span> +<span><span class="va">f_sep_const</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mmkin.html">mmkin</a></span><span class="op">(</span></span> +<span> <span class="va">deg_mods</span>,</span> +<span> <span class="va">dmta_ds</span>,</span> +<span> error_model <span class="op">=</span> <span class="st">"const"</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span></span> +<span><span class="fu"><a href="../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_const</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">Calke</th> +<th align="left">Borstel</th> +<th align="left">Flaach</th> +<th align="left">BBA 2.2</th> +<th align="left">BBA 2.3</th> +<th align="left">Elliot</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">HS</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>In the table above, OK indicates convergence, and C indicates failure +to converge. All separate fits with constant variance converged, with +the sole exception of the HS fit to the BBA 2.2 data. To prepare for +fitting NLHM using the two-component error model, the separate fits are +updated assuming two-component error.</p> +<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_sep_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_sep_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span> +<span><span class="fu"><a href="../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_tc</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">Calke</th> +<th align="left">Borstel</th> +<th align="left">Flaach</th> +<th align="left">BBA 2.2</th> +<th align="left">BBA 2.3</th> +<th align="left">Elliot</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">HS</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>Using the two-component error model, the one fit that did not +converge with constant variance did converge, but other non-SFO fits +failed to converge.</p> +</div> +<div class="section level2"> +<h2 id="hierarchichal-model-fits">Hierarchichal model fits<a class="anchor" aria-label="anchor" href="#hierarchichal-model-fits"></a> +</h2> +<p>The following code fits eight versions of hierarchical models to the +data, using SFO, FOMC, DFOP and HS for the parent compound, and using +either constant variance or two-component error for the error model. The +default parameter distribution model in mkin allows for variation of all +degradation parameters across the assumed population of soils. In other +words, each degradation parameter is associated with a random effect as +a first step. The <code>mhmkin</code> function makes it possible to fit +all eight versions in parallel (given a sufficient number of computing +cores being available) to save execution time.</p> +<p>Convergence plots and summaries for these fits are shown in the +appendix.</p> +<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_saem</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_sep_const</span>, <span class="va">f_sep_tc</span><span class="op">)</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span></code></pre></div> +<p>The output of the <code>status</code> function shows that all fits +terminated successfully.</p> +<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">HS</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>The AIC and BIC values show that the biphasic models DFOP and HS give +the best fits.</p> +<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="right">npar</th> +<th align="right">AIC</th> +<th align="right">BIC</th> +<th align="right">Lik</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">SFO const</td> +<td align="right">5</td> +<td align="right">796.3</td> +<td align="right">795.3</td> +<td align="right">-393.2</td> +</tr> +<tr class="even"> +<td align="left">SFO tc</td> +<td align="right">6</td> +<td align="right">798.3</td> +<td align="right">797.1</td> +<td align="right">-393.2</td> +</tr> +<tr class="odd"> +<td align="left">FOMC const</td> +<td align="right">7</td> +<td align="right">734.2</td> +<td align="right">732.7</td> +<td align="right">-360.1</td> +</tr> +<tr class="even"> +<td align="left">FOMC tc</td> +<td align="right">8</td> +<td align="right">720.4</td> +<td align="right">718.8</td> +<td align="right">-352.2</td> +</tr> +<tr class="odd"> +<td align="left">DFOP const</td> +<td align="right">9</td> +<td align="right">711.8</td> +<td align="right">710.0</td> +<td align="right">-346.9</td> +</tr> +<tr class="even"> +<td align="left">HS const</td> +<td align="right">9</td> +<td align="right">714.0</td> +<td align="right">712.1</td> +<td align="right">-348.0</td> +</tr> +<tr class="odd"> +<td align="left">DFOP tc</td> +<td align="right">10</td> +<td align="right">665.5</td> +<td align="right">663.4</td> +<td align="right">-322.8</td> +</tr> +<tr class="even"> +<td align="left">HS tc</td> +<td align="right">10</td> +<td align="right">667.1</td> +<td align="right">665.0</td> +<td align="right">-323.6</td> +</tr> +</tbody> +</table> +<p>The DFOP model is preferred here, as it has a better mechanistic +basis for batch experiments with constant incubation conditions. Also, +it shows the lowest AIC and BIC values in the first set of fits when +combined with the two-component error model. Therefore, the DFOP model +was selected for further refinements of the fits with the aim to make +the model fully identifiable.</p> +<div class="section level3"> +<h3 id="parameter-identifiability-based-on-the-fisher-information-matrix">Parameter identifiability based on the Fisher Information +Matrix<a class="anchor" aria-label="anchor" href="#parameter-identifiability-based-on-the-fisher-information-matrix"></a> +</h3> +<p>Using the <code>illparms</code> function, ill-defined statistical +model parameters such as standard deviations of the degradation +parameters in the population and error model parameters can be +found.</p> +<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left"></td> +<td align="left">b.1</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left"></td> +<td align="left">sd(DMTA_0)</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">sd(k2)</td> +<td align="left">sd(k2)</td> +</tr> +<tr class="even"> +<td align="left">HS</td> +<td align="left"></td> +<td align="left">sd(tb)</td> +</tr> +</tbody> +</table> +<p>According to the <code>illparms</code> function, the fitted standard +deviation of the second kinetic rate constant <code>k2</code> is +ill-defined in both DFOP fits. This suggests that different values would +be obtained for this standard deviation when using different starting +values.</p> +<p>The thus identified overparameterisation is addressed by removing the +random effect for <code>k2</code> from the parameter model.</p> +<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_saem_dfop_tc_no_ranef_k2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>,</span> +<span> no_random_effect <span class="op">=</span> <span class="st">"k2"</span><span class="op">)</span></span></code></pre></div> +<p>For the resulting fit, it is checked whether there are still +ill-defined parameters,</p> +<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span><span class="op">)</span></span></code></pre></div> +<p>which is not the case. Below, the refined model is compared with the +previous best model. The model without random effect for <code>k2</code> +is a reduced version of the previous model. Therefore, the models are +nested and can be compared using the likelihood ratio test. This is +achieved with the argument <code>test = TRUE</code> to the +<code>anova</code> function.</p> +<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>, <span class="va">f_saem_dfop_tc_no_ranef_k2</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">|></span></span> +<span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>format.args <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<colgroup> +<col width="37%"> +<col width="6%"> +<col width="8%"> +<col width="8%"> +<col width="9%"> +<col width="9%"> +<col width="4%"> +<col width="15%"> +</colgroup> +<thead><tr class="header"> +<th align="left"></th> +<th align="right">npar</th> +<th align="right">AIC</th> +<th align="right">BIC</th> +<th align="right">Lik</th> +<th align="right">Chisq</th> +<th align="right">Df</th> +<th align="right">Pr(>Chisq)</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">f_saem_dfop_tc_no_ranef_k2</td> +<td align="right">9</td> +<td align="right">663.8</td> +<td align="right">661.9</td> +<td align="right">-322.9</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="left">f_saem[[“DFOP”, “tc”]]</td> +<td align="right">10</td> +<td align="right">665.5</td> +<td align="right">663.4</td> +<td align="right">-322.8</td> +<td align="right">0.2809</td> +<td align="right">1</td> +<td align="right">0.5961</td> +</tr> +</tbody> +</table> +<p>The AIC and BIC criteria are lower after removal of the ill-defined +random effect for <code>k2</code>. The p value of the likelihood ratio +test is much greater than 0.05, indicating that the model with the +higher likelihood (here the model with random effects for all +degradation parameters <code>f_saem[["DFOP", "tc"]]</code>) does not fit +significantly better than the model with the lower likelihood (the +reduced model <code>f_saem_dfop_tc_no_ranef_k2</code>).</p> +<p>Therefore, AIC, BIC and likelihood ratio test suggest the use of the +reduced model.</p> +<p>The convergence of the fit is checked visually.</p> +<div class="figure" style="text-align: center"> +<img src="2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-dfop-tc-no-ranef-k2-1.png" alt="Convergence plot for the NLHM DFOP fit with two-component error and without a random effect on 'k2'" width="864"><p class="caption"> +Convergence plot for the NLHM DFOP fit with two-component error and +without a random effect on ‘k2’ +</p> +</div> +<p>All parameters appear to have converged to a satisfactory degree. The +final fit is plotted using the plot method from the mkin package.</p> +<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_wp_1.1_dmta_parent_files/figure-html/plot-saem-dfop-tc-no-ranef-k2-1.png" alt="Plot of the final NLHM DFOP fit" width="864"><p class="caption"> +Plot of the final NLHM DFOP fit +</p> +</div> +<p>Finally, a summary report of the fit is produced.</p> +<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span><span class="op">)</span></span></code></pre></div> +<pre><code>saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Thu Jan 5 08:19:13 2023 +Date of summary: Thu Jan 5 08:19:13 2023 + +Equations: +d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 4.075 s +Using 300, 100 iterations and 9 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + DMTA_0 k1 k2 g +98.759266 0.087034 0.009933 0.930827 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 k1 k2 g +DMTA_0 98.76 0 0 0 +k1 0.00 1 0 0 +k2 0.00 0 1 0 +g 0.00 0 0 1 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 663.8 661.9 -322.9 + +Optimised parameters: + est. lower upper +DMTA_0 98.228939 96.285869 100.17201 +k1 0.064063 0.033477 0.09465 +k2 0.008297 0.005824 0.01077 +g 0.953821 0.914328 0.99331 +a.1 1.068479 0.869538 1.26742 +b.1 0.029424 0.022406 0.03644 +SD.DMTA_0 2.030437 0.404824 3.65605 +SD.k1 0.594692 0.256660 0.93272 +SD.g 1.006754 0.361327 1.65218 + +Correlation: + DMTA_0 k1 k2 +k1 0.0218 +k2 0.0556 0.0355 +g -0.0516 -0.0284 -0.2800 + +Random effects: + est. lower upper +SD.DMTA_0 2.0304 0.4048 3.6560 +SD.k1 0.5947 0.2567 0.9327 +SD.g 1.0068 0.3613 1.6522 + +Variance model: + est. lower upper +a.1 1.06848 0.86954 1.26742 +b.1 0.02942 0.02241 0.03644 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +DMTA 11.45 41.4 12.46 10.82 83.54</code></pre> +</div> +<div class="section level3"> +<h3 id="alternative-check-of-parameter-identifiability">Alternative check of parameter identifiability<a class="anchor" aria-label="anchor" href="#alternative-check-of-parameter-identifiability"></a> +</h3> +<p>The parameter check used in the <code>illparms</code> function is +based on a quadratic approximation of the likelihood surface near its +optimum, which is calculated using the Fisher Information Matrix (FIM). +An alternative way to check parameter identifiability based on a +multistart approach has recently been implemented in mkin.</p> +<p>The graph below shows boxplots of the parameters obtained in 50 runs +of the saem algorithm with different parameter combinations, sampled +from the range of the parameters obtained for the individual datasets +fitted separately using nonlinear regression.</p> +<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_saem_dfop_tc_multi</span> <span class="op"><-</span> <span class="fu"><a href="../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>, n <span class="op">=</span> <span class="fl">50</span>, cores <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></span></code></pre></div> +<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span> +<span><span class="fu"><a href="../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_multi</span>, lpos <span class="op">=</span> <span class="st">"bottomright"</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.3</span>, <span class="fl">10</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_wp_1.1_dmta_parent_files/figure-html/multistart-full-par-1.png" alt="Scaled parameters from the multistart runs, full model" width="960"><p class="caption"> +Scaled parameters from the multistart runs, full model +</p> +</div> +<p>The graph clearly confirms the lack of identifiability of the +variance of <code>k2</code> in the full model. The overparameterisation +of the model also indicates a lack of identifiability of the variance of +parameter <code>g</code>.</p> +<p>The parameter boxplots of the multistart runs with the reduced model +shown below indicate that all runs give similar results, regardless of +the starting parameters.</p> +<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_saem_dfop_tc_no_ranef_k2_multi</span> <span class="op"><-</span> <span class="fu"><a href="../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span>,</span> +<span> n <span class="op">=</span> <span class="fl">50</span>, cores <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></span></code></pre></div> +<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span> +<span><span class="fu"><a href="../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2_multi</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span>,</span> +<span> lpos <span class="op">=</span> <span class="st">"bottomright"</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_wp_1.1_dmta_parent_files/figure-html/multistart-reduced-par-1.png" alt="Scaled parameters from the multistart runs, reduced model" width="960"><p class="caption"> +Scaled parameters from the multistart runs, reduced model +</p> +</div> +<p>When only the parameters of the top 25% of the fits are shown (based +on a feature introduced in mkin 1.2.2 currently under development), the +scatter is even less as shown below.</p> +<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span> +<span><span class="fu"><a href="../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2_multi</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span>, llquant <span class="op">=</span> <span class="fl">0.25</span>,</span> +<span> lpos <span class="op">=</span> <span class="st">"bottomright"</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_wp_1.1_dmta_parent_files/figure-html/multistart-reduced-par-llquant-1.png" alt="Scaled parameters from the multistart runs, reduced model, fits with the top 25\% likelihood values" width="960"><p class="caption"> +Scaled parameters from the multistart runs, reduced model, fits with the +top 25% likelihood values +</p> +</div> +</div> +</div> +<div class="section level2"> +<h2 id="conclusions">Conclusions<a class="anchor" aria-label="anchor" href="#conclusions"></a> +</h2> +<p>Fitting the four parent degradation models SFO, FOMC, DFOP and HS as +part of hierarchical model fits with two different error models and +normal distributions of the transformed degradation parameters works +without technical problems. The biphasic models DFOP and HS gave the +best fit to the data, but the default parameter distribution model was +not fully identifiable. Removing the random effect for the second +kinetic rate constant of the DFOP model resulted in a reduced model that +was fully identifiable and showed the lowest values for the model +selection criteria AIC and BIC. The reliability of the identification of +all model parameters was confirmed using multiple starting values.</p> +</div> +<div class="section level2"> +<h2 id="appendix">Appendix<a class="anchor" aria-label="anchor" href="#appendix"></a> +</h2> +<div class="section level3"> +<h3 id="hierarchical-model-fit-listings">Hierarchical model fit listings<a class="anchor" aria-label="anchor" href="#hierarchical-model-fit-listings"></a> +</h3> +<caption> +Hierarchical mkin fit of the SFO model with error model const +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Thu Jan 5 08:19:06 2023 +Date of summary: Thu Jan 5 08:20:11 2023 + +Equations: +d_DMTA/dt = - k_DMTA * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 1.09 s +Using 300, 100 iterations and 9 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + DMTA_0 k_DMTA +97.2953 0.0566 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 k_DMTA +DMTA_0 97.3 0 +k_DMTA 0.0 1 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 796.3 795.3 -393.2 + +Optimised parameters: + est. lower upper +DMTA_0 97.28130 95.71113 98.8515 +k_DMTA 0.05665 0.02909 0.0842 +a.1 2.66442 2.35579 2.9731 +SD.DMTA_0 1.54776 0.15447 2.9411 +SD.k_DMTA 0.60690 0.26248 0.9513 + +Correlation: + DMTA_0 +k_DMTA 0.0168 + +Random effects: + est. lower upper +SD.DMTA_0 1.5478 0.1545 2.9411 +SD.k_DMTA 0.6069 0.2625 0.9513 + +Variance model: + est. lower upper +a.1 2.664 2.356 2.973 + +Estimated disappearance times: + DT50 DT90 +DMTA 12.24 40.65 + +</code></pre> +<p></p> +<caption> +Hierarchical mkin fit of the SFO model with error model tc +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Thu Jan 5 08:19:07 2023 +Date of summary: Thu Jan 5 08:20:11 2023 + +Equations: +d_DMTA/dt = - k_DMTA * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 2.441 s +Using 300, 100 iterations and 9 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + DMTA_0 k_DMTA +96.99175 0.05603 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 k_DMTA +DMTA_0 96.99 0 +k_DMTA 0.00 1 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 798.3 797.1 -393.2 + +Optimised parameters: + est. lower upper +DMTA_0 97.271822 95.703157 98.84049 +k_DMTA 0.056638 0.029110 0.08417 +a.1 2.660081 2.230398 3.08976 +b.1 0.001665 -0.006911 0.01024 +SD.DMTA_0 1.545520 0.145035 2.94601 +SD.k_DMTA 0.606422 0.262274 0.95057 + +Correlation: + DMTA_0 +k_DMTA 0.0169 + +Random effects: + est. lower upper +SD.DMTA_0 1.5455 0.1450 2.9460 +SD.k_DMTA 0.6064 0.2623 0.9506 + +Variance model: + est. lower upper +a.1 2.660081 2.230398 3.08976 +b.1 0.001665 -0.006911 0.01024 + +Estimated disappearance times: + DT50 DT90 +DMTA 12.24 40.65 + +</code></pre> +<p></p> +<caption> +Hierarchical mkin fit of the FOMC model with error model const +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Thu Jan 5 08:19:06 2023 +Date of summary: Thu Jan 5 08:20:11 2023 + +Equations: +d_DMTA/dt = - (alpha/beta) * 1/((time/beta) + 1) * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 1.156 s +Using 300, 100 iterations and 9 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + DMTA_0 alpha beta + 98.292 9.909 156.341 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 alpha beta +DMTA_0 98.29 0 0 +alpha 0.00 1 0 +beta 0.00 0 1 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 734.2 732.7 -360.1 + +Optimised parameters: + est. lower upper +DMTA_0 98.3435 96.9033 99.784 +alpha 7.2007 2.5889 11.812 +beta 112.8746 34.8816 190.868 +a.1 2.0459 1.8054 2.286 +SD.DMTA_0 1.4795 0.2717 2.687 +SD.alpha 0.6396 0.1509 1.128 +SD.beta 0.6874 0.1587 1.216 + +Correlation: + DMTA_0 alpha +alpha -0.1125 +beta -0.1227 0.3632 + +Random effects: + est. lower upper +SD.DMTA_0 1.4795 0.2717 2.687 +SD.alpha 0.6396 0.1509 1.128 +SD.beta 0.6874 0.1587 1.216 + +Variance model: + est. lower upper +a.1 2.046 1.805 2.286 + +Estimated disappearance times: + DT50 DT90 DT50back +DMTA 11.41 42.53 12.8 + +</code></pre> +<p></p> +<caption> +Hierarchical mkin fit of the FOMC model with error model tc +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Thu Jan 5 08:19:07 2023 +Date of summary: Thu Jan 5 08:20:11 2023 + +Equations: +d_DMTA/dt = - (alpha/beta) * 1/((time/beta) + 1) * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 2.729 s +Using 300, 100 iterations and 9 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: +DMTA_0 alpha beta +98.772 4.663 92.597 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 alpha beta +DMTA_0 98.77 0 0 +alpha 0.00 1 0 +beta 0.00 0 1 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 720.4 718.8 -352.2 + +Optimised parameters: + est. lower upper +DMTA_0 98.99136 97.26011 100.72261 +alpha 5.86312 2.57485 9.15138 +beta 88.55571 29.20889 147.90254 +a.1 1.51063 1.24384 1.77741 +b.1 0.02824 0.02040 0.03609 +SD.DMTA_0 1.57436 -0.04867 3.19739 +SD.alpha 0.59871 0.17132 1.02611 +SD.beta 0.72994 0.22849 1.23139 + +Correlation: + DMTA_0 alpha +alpha -0.1363 +beta -0.1414 0.2542 + +Random effects: + est. lower upper +SD.DMTA_0 1.5744 -0.04867 3.197 +SD.alpha 0.5987 0.17132 1.026 +SD.beta 0.7299 0.22849 1.231 + +Variance model: + est. lower upper +a.1 1.51063 1.2438 1.77741 +b.1 0.02824 0.0204 0.03609 + +Estimated disappearance times: + DT50 DT90 DT50back +DMTA 11.11 42.6 12.82 + +</code></pre> +<p></p> +<caption> +Hierarchical mkin fit of the DFOP model with error model const +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Thu Jan 5 08:19:07 2023 +Date of summary: Thu Jan 5 08:20:11 2023 + +Equations: +d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 2.007 s +Using 300, 100 iterations and 9 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + DMTA_0 k1 k2 g +98.64383 0.09211 0.02999 0.76814 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 k1 k2 g +DMTA_0 98.64 0 0 0 +k1 0.00 1 0 0 +k2 0.00 0 1 0 +g 0.00 0 0 1 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 711.8 710 -346.9 + +Optimised parameters: + est. lower upper +DMTA_0 98.092481 96.573898 99.61106 +k1 0.062499 0.030336 0.09466 +k2 0.009065 -0.005133 0.02326 +g 0.948967 0.862079 1.03586 +a.1 1.821671 1.604774 2.03857 +SD.DMTA_0 1.677785 0.472066 2.88350 +SD.k1 0.634962 0.270788 0.99914 +SD.k2 1.033498 -0.205994 2.27299 +SD.g 1.710046 0.428642 2.99145 + +Correlation: + DMTA_0 k1 k2 +k1 0.0246 +k2 0.0491 0.0953 +g -0.0552 -0.0889 -0.4795 + +Random effects: + est. lower upper +SD.DMTA_0 1.678 0.4721 2.8835 +SD.k1 0.635 0.2708 0.9991 +SD.k2 1.033 -0.2060 2.2730 +SD.g 1.710 0.4286 2.9914 + +Variance model: + est. lower upper +a.1 1.822 1.605 2.039 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +DMTA 11.79 42.8 12.88 11.09 76.46 + +</code></pre> +<p></p> +<caption> +Hierarchical mkin fit of the DFOP model with error model tc +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Thu Jan 5 08:19:08 2023 +Date of summary: Thu Jan 5 08:20:11 2023 + +Equations: +d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 3.033 s +Using 300, 100 iterations and 9 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + DMTA_0 k1 k2 g +98.759266 0.087034 0.009933 0.930827 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 k1 k2 g +DMTA_0 98.76 0 0 0 +k1 0.00 1 0 0 +k2 0.00 0 1 0 +g 0.00 0 0 1 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 665.5 663.4 -322.8 + +Optimised parameters: + est. lower upper +DMTA_0 98.377019 96.447952 100.30609 +k1 0.064843 0.034607 0.09508 +k2 0.008895 0.006368 0.01142 +g 0.949696 0.903815 0.99558 +a.1 1.065241 0.865754 1.26473 +b.1 0.029340 0.022336 0.03634 +SD.DMTA_0 2.007754 0.387982 3.62753 +SD.k1 0.580473 0.250286 0.91066 +SD.k2 0.006105 -4.920337 4.93255 +SD.g 1.097149 0.412779 1.78152 + +Correlation: + DMTA_0 k1 k2 +k1 0.0235 +k2 0.0595 0.0424 +g -0.0470 -0.0278 -0.2731 + +Random effects: + est. lower upper +SD.DMTA_0 2.007754 0.3880 3.6275 +SD.k1 0.580473 0.2503 0.9107 +SD.k2 0.006105 -4.9203 4.9325 +SD.g 1.097149 0.4128 1.7815 + +Variance model: + est. lower upper +a.1 1.06524 0.86575 1.26473 +b.1 0.02934 0.02234 0.03634 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +DMTA 11.36 41.32 12.44 10.69 77.92 + +</code></pre> +<p></p> +<caption> +Hierarchical mkin fit of the HS model with error model const +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Thu Jan 5 08:19:07 2023 +Date of summary: Thu Jan 5 08:20:11 2023 + +Equations: +d_DMTA/dt = - ifelse(time <= tb, k1, k2) * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 2.004 s +Using 300, 100 iterations and 9 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + DMTA_0 k1 k2 tb +97.82176 0.06931 0.02997 11.13945 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 k1 k2 tb +DMTA_0 97.82 0 0 0 +k1 0.00 1 0 0 +k2 0.00 0 1 0 +tb 0.00 0 0 1 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 714 712.1 -348 + +Optimised parameters: + est. lower upper +DMTA_0 98.16102 96.47747 99.84456 +k1 0.07876 0.05261 0.10491 +k2 0.02227 0.01706 0.02747 +tb 13.99089 -7.40049 35.38228 +a.1 1.82305 1.60700 2.03910 +SD.DMTA_0 1.88413 0.56204 3.20622 +SD.k1 0.34292 0.10482 0.58102 +SD.k2 0.19851 0.01718 0.37985 +SD.tb 1.68168 0.58064 2.78272 + +Correlation: + DMTA_0 k1 k2 +k1 0.0142 +k2 0.0001 -0.0025 +tb 0.0165 -0.1256 -0.0301 + +Random effects: + est. lower upper +SD.DMTA_0 1.8841 0.56204 3.2062 +SD.k1 0.3429 0.10482 0.5810 +SD.k2 0.1985 0.01718 0.3798 +SD.tb 1.6817 0.58064 2.7827 + +Variance model: + est. lower upper +a.1 1.823 1.607 2.039 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +DMTA 8.801 67.91 20.44 8.801 31.13 + +</code></pre> +<p></p> +<caption> +Hierarchical mkin fit of the HS model with error model tc +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Thu Jan 5 08:19:08 2023 +Date of summary: Thu Jan 5 08:20:11 2023 + +Equations: +d_DMTA/dt = - ifelse(time <= tb, k1, k2) * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 3.287 s +Using 300, 100 iterations and 9 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + DMTA_0 k1 k2 tb +98.45190 0.07525 0.02576 19.19375 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 k1 k2 tb +DMTA_0 98.45 0 0 0 +k1 0.00 1 0 0 +k2 0.00 0 1 0 +tb 0.00 0 0 1 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 667.1 665 -323.6 + +Optimised parameters: + est. lower upper +DMTA_0 97.76570 95.81350 99.71791 +k1 0.05855 0.03080 0.08630 +k2 0.02337 0.01664 0.03010 +tb 31.09638 29.38289 32.80987 +a.1 1.08835 0.88590 1.29080 +b.1 0.02964 0.02257 0.03671 +SD.DMTA_0 2.04877 0.42607 3.67147 +SD.k1 0.59166 0.25621 0.92711 +SD.k2 0.30698 0.09561 0.51835 +SD.tb 0.01274 -0.10914 0.13462 + +Correlation: + DMTA_0 k1 k2 +k1 0.0160 +k2 -0.0070 -0.0024 +tb -0.0668 -0.0103 -0.2013 + +Random effects: + est. lower upper +SD.DMTA_0 2.04877 0.42607 3.6715 +SD.k1 0.59166 0.25621 0.9271 +SD.k2 0.30698 0.09561 0.5183 +SD.tb 0.01274 -0.10914 0.1346 + +Variance model: + est. lower upper +a.1 1.08835 0.88590 1.29080 +b.1 0.02964 0.02257 0.03671 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +DMTA 11.84 51.71 15.57 11.84 29.66 + +</code></pre> +<p></p> +</div> +<div class="section level3"> +<h3 id="hierarchical-model-convergence-plots">Hierarchical model convergence plots<a class="anchor" aria-label="anchor" href="#hierarchical-model-convergence-plots"></a> +</h3> +<div class="figure" style="text-align: center"> +<img src="2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-sfo-const-1.png" alt="Convergence plot for the NLHM SFO fit with constant variance" width="864"><p class="caption"> +Convergence plot for the NLHM SFO fit with constant variance +</p> +</div> +<div class="figure" style="text-align: center"> +<img src="2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-sfo-tc-1.png" alt="Convergence plot for the NLHM SFO fit with two-component error" width="864"><p class="caption"> +Convergence plot for the NLHM SFO fit with two-component error +</p> +</div> +<div class="figure" style="text-align: center"> +<img src="2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-fomc-const-1.png" alt="Convergence plot for the NLHM FOMC fit with constant variance" width="864"><p class="caption"> +Convergence plot for the NLHM FOMC fit with constant variance +</p> +</div> +<div class="figure" style="text-align: center"> +<img src="2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-fomc-tc-1.png" alt="Convergence plot for the NLHM FOMC fit with two-component error" width="864"><p class="caption"> +Convergence plot for the NLHM FOMC fit with two-component error +</p> +</div> +<div class="figure" style="text-align: center"> +<img src="2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png" alt="Convergence plot for the NLHM DFOP fit with constant variance" width="864"><p class="caption"> +Convergence plot for the NLHM DFOP fit with constant variance +</p> +</div> +<div class="figure" style="text-align: center"> +<img src="2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-dfop-tc-1.png" alt="Convergence plot for the NLHM DFOP fit with two-component error" width="864"><p class="caption"> +Convergence plot for the NLHM DFOP fit with two-component error +</p> +</div> +<div class="figure" style="text-align: center"> +<img src="2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-hs-const-1.png" alt="Convergence plot for the NLHM HS fit with constant variance" width="864"><p class="caption"> +Convergence plot for the NLHM HS fit with constant variance +</p> +</div> +<div class="figure" style="text-align: center"> +<img src="2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-hs-tc-1.png" alt="Convergence plot for the NLHM HS fit with two-component error" width="864"><p class="caption"> +Convergence plot for the NLHM HS fit with two-component error +</p> +</div> +</div> +<div class="section level3"> +<h3 id="session-info">Session info<a class="anchor" aria-label="anchor" href="#session-info"></a> +</h3> +<pre><code>R version 4.2.2 Patched (2022-11-10 r83330) +Platform: x86_64-pc-linux-gnu (64-bit) +Running under: Debian GNU/Linux bookworm/sid + +Matrix products: default +BLAS: /usr/lib/x86_64-linux-gnu/openblas-serial/libblas.so.3 +LAPACK: /usr/lib/x86_64-linux-gnu/openblas-serial/libopenblas-r0.3.21.so + +locale: + [1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C + [3] LC_TIME=de_DE.UTF-8 LC_COLLATE=de_DE.UTF-8 + [5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=de_DE.UTF-8 + [7] LC_PAPER=de_DE.UTF-8 LC_NAME=C + [9] LC_ADDRESS=C LC_TELEPHONE=C +[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C + +attached base packages: +[1] parallel stats graphics grDevices utils datasets methods +[8] base + +other attached packages: +[1] saemix_3.2 npde_3.3 knitr_1.41 mkin_1.2.2 + +loaded via a namespace (and not attached): + [1] deSolve_1.34 zoo_1.8-11 tidyselect_1.2.0 xfun_0.35 + [5] bslib_0.4.2 purrr_1.0.0 lattice_0.20-45 colorspace_2.0-3 + [9] vctrs_0.5.1 generics_0.1.3 htmltools_0.5.4 yaml_2.3.6 +[13] utf8_1.2.2 rlang_1.0.6 pkgdown_2.0.7 jquerylib_0.1.4 +[17] pillar_1.8.1 glue_1.6.2 DBI_1.1.3 lifecycle_1.0.3 +[21] stringr_1.5.0 munsell_0.5.0 gtable_0.3.1 ragg_1.2.4 +[25] codetools_0.2-18 memoise_2.0.1 evaluate_0.19 fastmap_1.1.0 +[29] lmtest_0.9-40 fansi_1.0.3 highr_0.9 scales_1.2.1 +[33] cachem_1.0.6 desc_1.4.2 jsonlite_1.8.4 systemfonts_1.0.4 +[37] fs_1.5.2 textshaping_0.3.6 gridExtra_2.3 ggplot2_3.4.0 +[41] digest_0.6.31 stringi_1.7.8 dplyr_1.0.10 grid_4.2.2 +[45] rprojroot_2.0.3 cli_3.5.0 tools_4.2.2 magrittr_2.0.3 +[49] sass_0.4.4 tibble_3.1.8 pkgconfig_2.0.3 assertthat_0.2.1 +[53] rmarkdown_2.19 R6_2.5.1 mclust_6.0.0 nlme_3.1-161 +[57] compiler_4.2.2 </code></pre> +</div> +</div> + </div> + + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + + <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2> + </nav> +</div> + +</div> + + + + <footer><div class="copyright"> + <p></p> +<p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> +</div> + + </footer> +</div> + + + + + + + </body> +</html> diff --git a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png b/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png Binary files differnew file mode 100644 index 00000000..4f87b956 --- /dev/null +++ b/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png diff --git 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class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> @@ -32,7 +34,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -42,7 +44,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -58,19 +60,28 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul> </li> @@ -80,7 +91,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> <li> - <a href="https://github.com/jranke/mkin/"> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> @@ -95,15 +106,15 @@ - </header><script src="FOCUS_D_files/header-attrs-2.6/header-attrs.js"></script><script src="FOCUS_D_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><script src="FOCUS_D_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Example evaluation of FOCUS Example Dataset D</h1> - <h4 class="author">Johannes Ranke</h4> + <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 class="date">Last change 31 January 2019 (rebuilt 2021-02-15)</h4> + <h4 data-toc-skip class="date">Last change 31 January 2019 (rebuilt 2022-11-24)</h4> - <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/FOCUS_D.rmd"><code>vignettes/FOCUS_D.rmd</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/FOCUS_D.rmd" class="external-link"><code>vignettes/FOCUS_D.rmd</code></a></small> <div class="hidden name"><code>FOCUS_D.rmd</code></div> </div> @@ -112,207 +123,207 @@ <p>This is just a very simple vignette showing how to fit a degradation model for a parent compound with one transformation product using <code>mkin</code>. After loading the library we look at the data. We have observed concentrations in the column named <code>value</code> at the times specified in column <code>time</code> for the two observed variables named <code>parent</code> and <code>m1</code>.</p> <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> -<span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span><span class="op">)</span></code></pre></div> -<pre><code>## name time value -## 1 parent 0 99.46 -## 2 parent 0 102.04 -## 3 parent 1 93.50 -## 4 parent 1 92.50 -## 5 parent 3 63.23 -## 6 parent 3 68.99 -## 7 parent 7 52.32 -## 8 parent 7 55.13 -## 9 parent 14 27.27 -## 10 parent 14 26.64 -## 11 parent 21 11.50 -## 12 parent 21 11.64 -## 13 parent 35 2.85 -## 14 parent 35 2.91 -## 15 parent 50 0.69 -## 16 parent 50 0.63 -## 17 parent 75 0.05 -## 18 parent 75 0.06 -## 19 parent 100 NA -## 20 parent 100 NA -## 21 parent 120 NA -## 22 parent 120 NA -## 23 m1 0 0.00 -## 24 m1 0 0.00 -## 25 m1 1 4.84 -## 26 m1 1 5.64 -## 27 m1 3 12.91 -## 28 m1 3 12.96 -## 29 m1 7 22.97 -## 30 m1 7 24.47 -## 31 m1 14 41.69 -## 32 m1 14 33.21 -## 33 m1 21 44.37 -## 34 m1 21 46.44 -## 35 m1 35 41.22 -## 36 m1 35 37.95 -## 37 m1 50 41.19 -## 38 m1 50 40.01 -## 39 m1 75 40.09 -## 40 m1 75 33.85 -## 41 m1 100 31.04 -## 42 m1 100 33.13 -## 43 m1 120 25.15 -## 44 m1 120 33.31</code></pre> +<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## name time value</span></span> +<span><span class="co">## 1 parent 0 99.46</span></span> +<span><span class="co">## 2 parent 0 102.04</span></span> +<span><span class="co">## 3 parent 1 93.50</span></span> +<span><span class="co">## 4 parent 1 92.50</span></span> +<span><span class="co">## 5 parent 3 63.23</span></span> +<span><span class="co">## 6 parent 3 68.99</span></span> +<span><span class="co">## 7 parent 7 52.32</span></span> +<span><span class="co">## 8 parent 7 55.13</span></span> +<span><span class="co">## 9 parent 14 27.27</span></span> +<span><span class="co">## 10 parent 14 26.64</span></span> +<span><span class="co">## 11 parent 21 11.50</span></span> +<span><span class="co">## 12 parent 21 11.64</span></span> +<span><span class="co">## 13 parent 35 2.85</span></span> +<span><span class="co">## 14 parent 35 2.91</span></span> +<span><span class="co">## 15 parent 50 0.69</span></span> +<span><span class="co">## 16 parent 50 0.63</span></span> +<span><span class="co">## 17 parent 75 0.05</span></span> +<span><span class="co">## 18 parent 75 0.06</span></span> +<span><span class="co">## 19 parent 100 NA</span></span> +<span><span class="co">## 20 parent 100 NA</span></span> +<span><span class="co">## 21 parent 120 NA</span></span> +<span><span class="co">## 22 parent 120 NA</span></span> +<span><span class="co">## 23 m1 0 0.00</span></span> +<span><span class="co">## 24 m1 0 0.00</span></span> +<span><span class="co">## 25 m1 1 4.84</span></span> +<span><span class="co">## 26 m1 1 5.64</span></span> +<span><span class="co">## 27 m1 3 12.91</span></span> +<span><span class="co">## 28 m1 3 12.96</span></span> +<span><span class="co">## 29 m1 7 22.97</span></span> +<span><span class="co">## 30 m1 7 24.47</span></span> +<span><span class="co">## 31 m1 14 41.69</span></span> +<span><span class="co">## 32 m1 14 33.21</span></span> +<span><span class="co">## 33 m1 21 44.37</span></span> +<span><span class="co">## 34 m1 21 46.44</span></span> +<span><span class="co">## 35 m1 35 41.22</span></span> +<span><span class="co">## 36 m1 35 37.95</span></span> +<span><span class="co">## 37 m1 50 41.19</span></span> +<span><span class="co">## 38 m1 50 40.01</span></span> +<span><span class="co">## 39 m1 75 40.09</span></span> +<span><span class="co">## 40 m1 75 33.85</span></span> +<span><span class="co">## 41 m1 100 31.04</span></span> +<span><span class="co">## 42 m1 100 33.13</span></span> +<span><span class="co">## 43 m1 120 25.15</span></span> +<span><span class="co">## 44 m1 120 33.31</span></span></code></pre> <p>Next we specify the degradation model: The parent compound degrades with simple first-order kinetics (SFO) to one metabolite named m1, which also degrades with SFO kinetics.</p> <p>The call to mkinmod returns a degradation model. The differential equations represented in R code can be found in the character vector <code>$diffs</code> of the <code>mkinmod</code> object. If a C compiler (gcc) is installed and functional, the differential equation model will be compiled from auto-generated C code.</p> <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></code></pre></div> -<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> +<code class="sourceCode R"><span><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">SFO_SFO</span><span class="op">$</span><span class="va">diffs</span><span class="op">)</span></code></pre></div> -<pre><code>## parent -## "d_parent = - k_parent * parent" -## m1 -## "d_m1 = + f_parent_to_m1 * k_parent * parent - k_m1 * m1"</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">SFO_SFO</span><span class="op">$</span><span class="va">diffs</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## parent </span></span> +<span><span class="co">## "d_parent = - k_parent * parent" </span></span> +<span><span class="co">## m1 </span></span> +<span><span class="co">## "d_m1 = + f_parent_to_m1 * k_parent * parent - k_m1 * m1"</span></span></code></pre> <p>We do the fitting without progress report (<code>quiet = TRUE</code>).</p> <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE): Observations with value -## of zero were removed from the data</code></pre> +<code class="sourceCode R"><span><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE): Observations with value</span></span> +<span><span class="co">## of zero were removed from the data</span></span></code></pre> <p>A plot of the fit including a residual plot for both observed variables is obtained using the <code>plot_sep</code> method for <code>mkinfit</code> objects, which shows separate graphs for all compounds and their residuals.</p> <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">fit</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"bottomright"</span><span class="op">)</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">fit</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"bottomright"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_D_files/figure-html/plot-1.png" width="768"></p> <p>Confidence intervals for the parameter estimates are obtained using the <code>mkinparplot</code> function.</p> <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../reference/mkinparplot.html">mkinparplot</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../reference/mkinparplot.html">mkinparplot</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_D_files/figure-html/plot_2-1.png" width="768"></p> <p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p> <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.0.3.9000 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 17:13:36 2021 -## Date of summary: Mon Feb 15 17:13:37 2021 -## -## Equations: -## d_parent/dt = - k_parent * parent -## d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1 -## -## Model predictions using solution type analytical -## -## Fitted using 401 model solutions performed in 0.161 s -## -## Error model: Constant variance -## -## Error model algorithm: OLS -## -## Starting values for parameters to be optimised: -## value type -## parent_0 100.7500 state -## k_parent 0.1000 deparm -## k_m1 0.1001 deparm -## f_parent_to_m1 0.5000 deparm -## -## Starting values for the transformed parameters actually optimised: -## value lower upper -## parent_0 100.750000 -Inf Inf -## log_k_parent -2.302585 -Inf Inf -## log_k_m1 -2.301586 -Inf Inf -## f_parent_qlogis 0.000000 -Inf Inf -## -## Fixed parameter values: -## value type -## m1_0 0 state -## -## -## Warning(s): -## Observations with value of zero were removed from the data -## -## Results: -## -## AIC BIC logLik -## 204.4486 212.6365 -97.22429 -## -## Optimised, transformed parameters with symmetric confidence intervals: -## Estimate Std. Error Lower Upper -## parent_0 99.60000 1.57000 96.4000 102.8000 -## log_k_parent -2.31600 0.04087 -2.3990 -2.2330 -## log_k_m1 -5.24700 0.13320 -5.5180 -4.9770 -## f_parent_qlogis 0.05792 0.08926 -0.1237 0.2395 -## sigma 3.12600 0.35850 2.3960 3.8550 -## -## Parameter correlation: -## parent_0 log_k_parent log_k_m1 f_parent_qlogis sigma -## parent_0 1.000e+00 5.174e-01 -1.688e-01 -5.471e-01 -1.172e-06 -## log_k_parent 5.174e-01 1.000e+00 -3.263e-01 -5.426e-01 -8.483e-07 -## log_k_m1 -1.688e-01 -3.263e-01 1.000e+00 7.478e-01 8.205e-07 -## f_parent_qlogis -5.471e-01 -5.426e-01 7.478e-01 1.000e+00 1.305e-06 -## sigma -1.172e-06 -8.483e-07 8.205e-07 1.305e-06 1.000e+00 -## -## Backtransformed parameters: -## Confidence intervals for internally transformed parameters are asymmetric. -## t-test (unrealistically) based on the assumption of normal distribution -## for estimators of untransformed parameters. -## Estimate t value Pr(>t) Lower Upper -## parent_0 99.600000 63.430 2.298e-36 96.400000 1.028e+02 -## k_parent 0.098700 24.470 4.955e-23 0.090820 1.073e-01 -## k_m1 0.005261 7.510 6.165e-09 0.004012 6.898e-03 -## f_parent_to_m1 0.514500 23.070 3.104e-22 0.469100 5.596e-01 -## sigma 3.126000 8.718 2.235e-10 2.396000 3.855e+00 -## -## FOCUS Chi2 error levels in percent: -## err.min n.optim df -## All data 6.398 4 15 -## parent 6.459 2 7 -## m1 4.690 2 8 -## -## Resulting formation fractions: -## ff -## parent_m1 0.5145 -## parent_sink 0.4855 -## -## Estimated disappearance times: -## DT50 DT90 -## parent 7.023 23.33 -## m1 131.761 437.70 -## -## Data: -## time variable observed predicted residual -## 0 parent 99.46 99.59848 -1.385e-01 -## 0 parent 102.04 99.59848 2.442e+00 -## 1 parent 93.50 90.23787 3.262e+00 -## 1 parent 92.50 90.23787 2.262e+00 -## 3 parent 63.23 74.07319 -1.084e+01 -## 3 parent 68.99 74.07319 -5.083e+00 -## 7 parent 52.32 49.91207 2.408e+00 -## 7 parent 55.13 49.91207 5.218e+00 -## 14 parent 27.27 25.01258 2.257e+00 -## 14 parent 26.64 25.01258 1.627e+00 -## 21 parent 11.50 12.53462 -1.035e+00 -## 21 parent 11.64 12.53462 -8.946e-01 -## 35 parent 2.85 3.14787 -2.979e-01 -## 35 parent 2.91 3.14787 -2.379e-01 -## 50 parent 0.69 0.71624 -2.624e-02 -## 50 parent 0.63 0.71624 -8.624e-02 -## 75 parent 0.05 0.06074 -1.074e-02 -## 75 parent 0.06 0.06074 -7.382e-04 -## 1 m1 4.84 4.80296 3.704e-02 -## 1 m1 5.64 4.80296 8.370e-01 -## 3 m1 12.91 13.02400 -1.140e-01 -## 3 m1 12.96 13.02400 -6.400e-02 -## 7 m1 22.97 25.04476 -2.075e+00 -## 7 m1 24.47 25.04476 -5.748e-01 -## 14 m1 41.69 36.69003 5.000e+00 -## 14 m1 33.21 36.69003 -3.480e+00 -## 21 m1 44.37 41.65310 2.717e+00 -## 21 m1 46.44 41.65310 4.787e+00 -## 35 m1 41.22 43.31313 -2.093e+00 -## 35 m1 37.95 43.31313 -5.363e+00 -## 50 m1 41.19 41.21832 -2.832e-02 -## 50 m1 40.01 41.21832 -1.208e+00 -## 75 m1 40.09 36.44704 3.643e+00 -## 75 m1 33.85 36.44704 -2.597e+00 -## 100 m1 31.04 31.98162 -9.416e-01 -## 100 m1 33.13 31.98162 1.148e+00 -## 120 m1 25.15 28.78984 -3.640e+00 -## 120 m1 33.31 28.78984 4.520e+00</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.2 </span></span> +<span><span class="co">## R version used for fitting: 4.2.2 </span></span> +<span><span class="co">## Date of fit: Thu Nov 24 08:12:04 2022 </span></span> +<span><span class="co">## Date of summary: Thu Nov 24 08:12:05 2022 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Equations:</span></span> +<span><span class="co">## d_parent/dt = - k_parent * parent</span></span> +<span><span class="co">## d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Model predictions using solution type analytical </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Fitted using 401 model solutions performed in 0.152 s</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Error model: Constant variance </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Error model algorithm: OLS </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Starting values for parameters to be optimised:</span></span> +<span><span class="co">## value type</span></span> +<span><span class="co">## parent_0 100.7500 state</span></span> +<span><span class="co">## k_parent 0.1000 deparm</span></span> +<span><span class="co">## k_m1 0.1001 deparm</span></span> +<span><span class="co">## f_parent_to_m1 0.5000 deparm</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Starting values for the transformed parameters actually optimised:</span></span> +<span><span class="co">## value lower upper</span></span> +<span><span class="co">## parent_0 100.750000 -Inf Inf</span></span> +<span><span class="co">## log_k_parent -2.302585 -Inf Inf</span></span> +<span><span class="co">## log_k_m1 -2.301586 -Inf Inf</span></span> +<span><span class="co">## f_parent_qlogis 0.000000 -Inf Inf</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Fixed parameter values:</span></span> +<span><span class="co">## value type</span></span> +<span><span class="co">## m1_0 0 state</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Warning(s): </span></span> +<span><span class="co">## Observations with value of zero were removed from the data</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Results:</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## AIC BIC logLik</span></span> +<span><span class="co">## 204.4486 212.6365 -97.22429</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span></span> +<span><span class="co">## Estimate Std. Error Lower Upper</span></span> +<span><span class="co">## parent_0 99.60000 1.57000 96.4000 102.8000</span></span> +<span><span class="co">## log_k_parent -2.31600 0.04087 -2.3990 -2.2330</span></span> +<span><span class="co">## log_k_m1 -5.24700 0.13320 -5.5180 -4.9770</span></span> +<span><span class="co">## f_parent_qlogis 0.05792 0.08926 -0.1237 0.2395</span></span> +<span><span class="co">## sigma 3.12600 0.35850 2.3960 3.8550</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameter correlation:</span></span> +<span><span class="co">## parent_0 log_k_parent log_k_m1 f_parent_qlogis sigma</span></span> +<span><span class="co">## parent_0 1.000e+00 5.174e-01 -1.688e-01 -5.471e-01 -1.172e-06</span></span> +<span><span class="co">## log_k_parent 5.174e-01 1.000e+00 -3.263e-01 -5.426e-01 -8.483e-07</span></span> +<span><span class="co">## log_k_m1 -1.688e-01 -3.263e-01 1.000e+00 7.478e-01 8.205e-07</span></span> +<span><span class="co">## f_parent_qlogis -5.471e-01 -5.426e-01 7.478e-01 1.000e+00 1.305e-06</span></span> +<span><span class="co">## sigma -1.172e-06 -8.483e-07 8.205e-07 1.305e-06 1.000e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Backtransformed parameters:</span></span> +<span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span> +<span><span class="co">## t-test (unrealistically) based on the assumption of normal distribution</span></span> +<span><span class="co">## for estimators of untransformed parameters.</span></span> +<span><span class="co">## Estimate t value Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 99.600000 63.430 2.298e-36 96.400000 1.028e+02</span></span> +<span><span class="co">## k_parent 0.098700 24.470 4.955e-23 0.090820 1.073e-01</span></span> +<span><span class="co">## k_m1 0.005261 7.510 6.165e-09 0.004012 6.898e-03</span></span> +<span><span class="co">## f_parent_to_m1 0.514500 23.070 3.104e-22 0.469100 5.596e-01</span></span> +<span><span class="co">## sigma 3.126000 8.718 2.235e-10 2.396000 3.855e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## FOCUS Chi2 error levels in percent:</span></span> +<span><span class="co">## err.min n.optim df</span></span> +<span><span class="co">## All data 6.398 4 15</span></span> +<span><span class="co">## parent 6.459 2 7</span></span> +<span><span class="co">## m1 4.690 2 8</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Resulting formation fractions:</span></span> +<span><span class="co">## ff</span></span> +<span><span class="co">## parent_m1 0.5145</span></span> +<span><span class="co">## parent_sink 0.4855</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Estimated disappearance times:</span></span> +<span><span class="co">## DT50 DT90</span></span> +<span><span class="co">## parent 7.023 23.33</span></span> +<span><span class="co">## m1 131.761 437.70</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Data:</span></span> +<span><span class="co">## time variable observed predicted residual</span></span> +<span><span class="co">## 0 parent 99.46 99.59848 -1.385e-01</span></span> +<span><span class="co">## 0 parent 102.04 99.59848 2.442e+00</span></span> +<span><span class="co">## 1 parent 93.50 90.23787 3.262e+00</span></span> +<span><span class="co">## 1 parent 92.50 90.23787 2.262e+00</span></span> +<span><span class="co">## 3 parent 63.23 74.07319 -1.084e+01</span></span> +<span><span class="co">## 3 parent 68.99 74.07319 -5.083e+00</span></span> +<span><span class="co">## 7 parent 52.32 49.91207 2.408e+00</span></span> +<span><span class="co">## 7 parent 55.13 49.91207 5.218e+00</span></span> +<span><span class="co">## 14 parent 27.27 25.01258 2.257e+00</span></span> +<span><span class="co">## 14 parent 26.64 25.01258 1.627e+00</span></span> +<span><span class="co">## 21 parent 11.50 12.53462 -1.035e+00</span></span> +<span><span class="co">## 21 parent 11.64 12.53462 -8.946e-01</span></span> +<span><span class="co">## 35 parent 2.85 3.14787 -2.979e-01</span></span> +<span><span class="co">## 35 parent 2.91 3.14787 -2.379e-01</span></span> +<span><span class="co">## 50 parent 0.69 0.71624 -2.624e-02</span></span> +<span><span class="co">## 50 parent 0.63 0.71624 -8.624e-02</span></span> +<span><span class="co">## 75 parent 0.05 0.06074 -1.074e-02</span></span> +<span><span class="co">## 75 parent 0.06 0.06074 -7.382e-04</span></span> +<span><span class="co">## 1 m1 4.84 4.80296 3.704e-02</span></span> +<span><span class="co">## 1 m1 5.64 4.80296 8.370e-01</span></span> +<span><span class="co">## 3 m1 12.91 13.02400 -1.140e-01</span></span> +<span><span class="co">## 3 m1 12.96 13.02400 -6.400e-02</span></span> +<span><span class="co">## 7 m1 22.97 25.04476 -2.075e+00</span></span> +<span><span class="co">## 7 m1 24.47 25.04476 -5.748e-01</span></span> +<span><span class="co">## 14 m1 41.69 36.69003 5.000e+00</span></span> +<span><span class="co">## 14 m1 33.21 36.69003 -3.480e+00</span></span> +<span><span class="co">## 21 m1 44.37 41.65310 2.717e+00</span></span> +<span><span class="co">## 21 m1 46.44 41.65310 4.787e+00</span></span> +<span><span class="co">## 35 m1 41.22 43.31313 -2.093e+00</span></span> +<span><span class="co">## 35 m1 37.95 43.31313 -5.363e+00</span></span> +<span><span class="co">## 50 m1 41.19 41.21832 -2.832e-02</span></span> +<span><span class="co">## 50 m1 40.01 41.21832 -1.208e+00</span></span> +<span><span class="co">## 75 m1 40.09 36.44704 3.643e+00</span></span> +<span><span class="co">## 75 m1 33.85 36.44704 -2.597e+00</span></span> +<span><span class="co">## 100 m1 31.04 31.98162 -9.416e-01</span></span> +<span><span class="co">## 100 m1 33.13 31.98162 1.148e+00</span></span> +<span><span class="co">## 120 m1 25.15 28.78984 -3.640e+00</span></span> +<span><span class="co">## 120 m1 33.31 28.78984 4.520e+00</span></span></code></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> @@ -324,11 +335,13 @@ <footer><div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <p></p> +<p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer> @@ -337,5 +350,7 @@ + + </body> </html> diff --git a/docs/dev/articles/FOCUS_D_files/figure-html/plot-1.png b/docs/dev/articles/FOCUS_D_files/figure-html/plot-1.png Binary files differindex abf26715..f0b51c1f 100644 --- a/docs/dev/articles/FOCUS_D_files/figure-html/plot-1.png +++ b/docs/dev/articles/FOCUS_D_files/figure-html/plot-1.png diff --git a/docs/dev/articles/FOCUS_D_files/figure-html/plot_2-1.png b/docs/dev/articles/FOCUS_D_files/figure-html/plot_2-1.png Binary files differindex f4937894..f6180470 100644 --- a/docs/dev/articles/FOCUS_D_files/figure-html/plot_2-1.png +++ b/docs/dev/articles/FOCUS_D_files/figure-html/plot_2-1.png diff --git a/docs/dev/articles/FOCUS_L.html b/docs/dev/articles/FOCUS_L.html index 43ed0f69..586a6a00 100644 --- a/docs/dev/articles/FOCUS_L.html +++ b/docs/dev/articles/FOCUS_L.html @@ -34,7 +34,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.2</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -63,19 +63,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul> </li> @@ -106,7 +112,7 @@ <h1 data-toc-skip>Example evaluation of FOCUS Laboratory Data L1 to L3</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">Last change 18 May 2022 (rebuilt 2022-09-16)</h4> + <h4 data-toc-skip class="date">Last change 18 May 2022 (rebuilt 2022-11-24)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/FOCUS_L.rmd" class="external-link"><code>vignettes/FOCUS_L.rmd</code></a></small> <div class="hidden name"><code>FOCUS_L.rmd</code></div> @@ -132,17 +138,17 @@ <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">m.L1.SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_L1_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> <span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L1.SFO</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.1.2 </span></span> -<span><span class="co">## R version used for fitting: 4.2.1 </span></span> -<span><span class="co">## Date of fit: Fri Sep 16 10:31:35 2022 </span></span> -<span><span class="co">## Date of summary: Fri Sep 16 10:31:35 2022 </span></span> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.2 </span></span> +<span><span class="co">## R version used for fitting: 4.2.2 </span></span> +<span><span class="co">## Date of fit: Thu Nov 24 08:12:09 2022 </span></span> +<span><span class="co">## Date of summary: Thu Nov 24 08:12:09 2022 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - k_parent * parent</span></span> <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 133 model solutions performed in 0.032 s</span></span> +<span><span class="co">## Fitted using 133 model solutions performed in 0.033 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -238,17 +244,17 @@ <pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> <pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span></span> <span><span class="co">## doubtful</span></span></code></pre> -<pre><code><span><span class="co">## mkin version used for fitting: 1.1.2 </span></span> -<span><span class="co">## R version used for fitting: 4.2.1 </span></span> -<span><span class="co">## Date of fit: Fri Sep 16 10:31:36 2022 </span></span> -<span><span class="co">## Date of summary: Fri Sep 16 10:31:36 2022 </span></span> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.2 </span></span> +<span><span class="co">## R version used for fitting: 4.2.2 </span></span> +<span><span class="co">## Date of fit: Thu Nov 24 08:12:09 2022 </span></span> +<span><span class="co">## Date of summary: Thu Nov 24 08:12:09 2022 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span></span> <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 369 model solutions performed in 0.081 s</span></span> +<span><span class="co">## Fitted using 369 model solutions performed in 0.091 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -350,17 +356,17 @@ <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-9-1.png" width="672"></p> <div class="sourceCode" id="cb17"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L2.FOMC</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.1.2 </span></span> -<span><span class="co">## R version used for fitting: 4.2.1 </span></span> -<span><span class="co">## Date of fit: Fri Sep 16 10:31:36 2022 </span></span> -<span><span class="co">## Date of summary: Fri Sep 16 10:31:36 2022 </span></span> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.2 </span></span> +<span><span class="co">## R version used for fitting: 4.2.2 </span></span> +<span><span class="co">## Date of fit: Thu Nov 24 08:12:10 2022 </span></span> +<span><span class="co">## Date of summary: Thu Nov 24 08:12:10 2022 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span></span> <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 239 model solutions performed in 0.049 s</span></span> +<span><span class="co">## Fitted using 239 model solutions performed in 0.048 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -431,10 +437,10 @@ <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-10-1.png" width="672"></p> <div class="sourceCode" id="cb20"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L2.DFOP</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.1.2 </span></span> -<span><span class="co">## R version used for fitting: 4.2.1 </span></span> -<span><span class="co">## Date of fit: Fri Sep 16 10:31:37 2022 </span></span> -<span><span class="co">## Date of summary: Fri Sep 16 10:31:37 2022 </span></span> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.2 </span></span> +<span><span class="co">## R version used for fitting: 4.2.2 </span></span> +<span><span class="co">## Date of fit: Thu Nov 24 08:12:10 2022 </span></span> +<span><span class="co">## Date of summary: Thu Nov 24 08:12:10 2022 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span></span> @@ -443,7 +449,7 @@ <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 581 model solutions performed in 0.132 s</span></span> +<span><span class="co">## Fitted using 581 model solutions performed in 0.13 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -537,10 +543,10 @@ <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.</p> <div class="sourceCode" id="cb24"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.1.2 </span></span> -<span><span class="co">## R version used for fitting: 4.2.1 </span></span> -<span><span class="co">## Date of fit: Fri Sep 16 10:31:37 2022 </span></span> -<span><span class="co">## Date of summary: Fri Sep 16 10:31:38 2022 </span></span> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.2 </span></span> +<span><span class="co">## R version used for fitting: 4.2.2 </span></span> +<span><span class="co">## Date of fit: Thu Nov 24 08:12:11 2022 </span></span> +<span><span class="co">## Date of summary: Thu Nov 24 08:12:11 2022 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span></span> @@ -549,7 +555,7 @@ <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 376 model solutions performed in 0.079 s</span></span> +<span><span class="co">## Fitted using 376 model solutions performed in 0.078 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -650,17 +656,17 @@ <p>The <span class="math inline">\(\chi^2\)</span> error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the <span class="math inline">\(\chi^2\)</span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p> <div class="sourceCode" id="cb29"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">[[</span><span class="st">"SFO"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.1.2 </span></span> -<span><span class="co">## R version used for fitting: 4.2.1 </span></span> -<span><span class="co">## Date of fit: Fri Sep 16 10:31:38 2022 </span></span> -<span><span class="co">## Date of summary: Fri Sep 16 10:31:38 2022 </span></span> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.2 </span></span> +<span><span class="co">## R version used for fitting: 4.2.2 </span></span> +<span><span class="co">## Date of fit: Thu Nov 24 08:12:12 2022 </span></span> +<span><span class="co">## Date of summary: Thu Nov 24 08:12:12 2022 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - k_parent * parent</span></span> <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 142 model solutions performed in 0.03 s</span></span> +<span><span class="co">## Fitted using 142 model solutions performed in 0.029 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -715,17 +721,17 @@ <span><span class="co">## parent 106 352</span></span></code></pre> <div class="sourceCode" id="cb31"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.1.2 </span></span> -<span><span class="co">## R version used for fitting: 4.2.1 </span></span> -<span><span class="co">## Date of fit: Fri Sep 16 10:31:38 2022 </span></span> -<span><span class="co">## Date of summary: Fri Sep 16 10:31:38 2022 </span></span> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.2 </span></span> +<span><span class="co">## R version used for fitting: 4.2.2 </span></span> +<span><span class="co">## Date of fit: Thu Nov 24 08:12:12 2022 </span></span> +<span><span class="co">## Date of summary: Thu Nov 24 08:12:12 2022 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span></span> <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 224 model solutions performed in 0.045 s</span></span> +<span><span class="co">## Fitted using 224 model solutions performed in 0.044 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> diff --git a/docs/dev/articles/index.html b/docs/dev/articles/index.html index 395b5f7c..b9571a60 100644 --- a/docs/dev/articles/index.html +++ b/docs/dev/articles/index.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -96,6 +113,12 @@ <dd> </dd><dt><a href="mkin.html">Introduction to mkin</a></dt> <dd> + </dd><dt><a href="prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></dt> + <dd> + </dd><dt><a href="prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></dt> + <dd> + </dd><dt><a href="prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></dt> + <dd> </dd><dt><a href="twa.html">Calculation of time weighted average concentrations with mkin</a></dt> <dd> </dd><dt><a href="web_only/FOCUS_Z.html">Example evaluation of FOCUS dataset Z</a></dt> @@ -122,7 +145,7 @@ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/dev/articles/mkin.html b/docs/dev/articles/mkin.html index 6bfb63bc..df4df718 100644 --- a/docs/dev/articles/mkin.html +++ b/docs/dev/articles/mkin.html @@ -34,7 +34,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -44,7 +44,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -60,19 +60,28 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul> </li> @@ -103,7 +112,7 @@ <h1 data-toc-skip>Introduction to mkin</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">Last change 15 February 2021 (rebuilt 2022-02-28)</h4> + <h4 data-toc-skip class="date">Last change 15 February 2021 (rebuilt 2022-11-24)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/mkin.rmd" class="external-link"><code>vignettes/mkin.rmd</code></a></small> <div class="hidden name"><code>mkin.rmd</code></div> @@ -118,34 +127,34 @@ </h2> <p>In the regulatory evaluation of chemical substances like plant protection products (pesticides), biocides and other chemicals, degradation data play an important role. For the evaluation of pesticide degradation experiments, detailed guidance has been developed, based on nonlinear optimisation. The <code>R</code> add-on package <code>mkin</code> implements fitting some of the models recommended in this guidance from within R and calculates some statistical measures for data series within one or more compartments, for parent and metabolites.</p> <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="st"><a href="https://pkgdown.jrwb.de/mkin/">"mkin"</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> -<span class="co"># Define the kinetic model</span> -<span class="va">m_SFO_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M1"</span><span class="op">)</span>, - M1 <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M2"</span><span class="op">)</span>, - M2 <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, - use_of_ff <span class="op">=</span> <span class="st">"max"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> - - -<span class="co"># Produce model predictions using some arbitrary parameters</span> -<span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span> -<span class="va">d_SFO_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO_SFO_SFO</span>, - <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.03</span>, - f_parent_to_M1 <span class="op">=</span> <span class="fl">0.5</span>, k_M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">/</span><span class="fl">100</span>, - f_M1_to_M2 <span class="op">=</span> <span class="fl">0.9</span>, k_M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">/</span><span class="fl">50</span><span class="op">)</span>, - <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span>, M2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, - <span class="va">sampling_times</span><span class="op">)</span> - -<span class="co"># Generate a dataset by adding normally distributed errors with</span> -<span class="co"># standard deviation 3, for two replicates at each sampling time</span> -<span class="va">d_SFO_SFO_SFO_err</span> <span class="op"><-</span> <span class="fu"><a href="../reference/add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_SFO_SFO_SFO</span>, reps <span class="op">=</span> <span class="fl">2</span>, - sdfunc <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fl">3</span>, - n <span class="op">=</span> <span class="fl">1</span>, seed <span class="op">=</span> <span class="fl">123456789</span> <span class="op">)</span> - -<span class="co"># Fit the model to the dataset</span> -<span class="va">f_SFO_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_SFO_SFO_SFO</span>, <span class="va">d_SFO_SFO_SFO_err</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> - -<span class="co"># Plot the results separately for parent and metabolites</span> -<span class="fu"><a href="../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">f_SFO_SFO_SFO</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"bottomright"</span>, <span class="st">"bottomright"</span><span class="op">)</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="st"><a href="https://pkgdown.jrwb.de/mkin/">"mkin"</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span><span class="co"># Define the kinetic model</span></span> +<span><span class="va">m_SFO_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M1"</span><span class="op">)</span>,</span> +<span> M1 <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span> +<span> M2 <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> use_of_ff <span class="op">=</span> <span class="st">"max"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span></span> +<span></span> +<span><span class="co"># Produce model predictions using some arbitrary parameters</span></span> +<span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span> +<span><span class="va">d_SFO_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO_SFO_SFO</span>,</span> +<span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.03</span>,</span> +<span> f_parent_to_M1 <span class="op">=</span> <span class="fl">0.5</span>, k_M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">/</span><span class="fl">100</span>,</span> +<span> f_M1_to_M2 <span class="op">=</span> <span class="fl">0.9</span>, k_M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">/</span><span class="fl">50</span><span class="op">)</span>,</span> +<span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span>, M2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span> +<span> <span class="va">sampling_times</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># Generate a dataset by adding normally distributed errors with</span></span> +<span><span class="co"># standard deviation 3, for two replicates at each sampling time</span></span> +<span><span class="va">d_SFO_SFO_SFO_err</span> <span class="op"><-</span> <span class="fu"><a href="../reference/add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_SFO_SFO_SFO</span>, reps <span class="op">=</span> <span class="fl">2</span>,</span> +<span> sdfunc <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fl">3</span>,</span> +<span> n <span class="op">=</span> <span class="fl">1</span>, seed <span class="op">=</span> <span class="fl">123456789</span> <span class="op">)</span></span> +<span></span> +<span><span class="co"># Fit the model to the dataset</span></span> +<span><span class="va">f_SFO_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_SFO_SFO_SFO</span>, <span class="va">d_SFO_SFO_SFO_err</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># Plot the results separately for parent and metabolites</span></span> +<span><span class="fu"><a href="../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">f_SFO_SFO_SFO</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"bottomright"</span>, <span class="st">"bottomright"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> <p><img src="mkin_files/figure-html/unnamed-chunk-2-1.png" width="768"></p> </div> <div class="section level2"> @@ -224,10 +233,10 @@ <p>Ranke, J. 2021. <em>‘mkin‘: Kinetic Evaluation of Chemical Degradation Data</em>. <a href="https://CRAN.R-project.org/package=mkin" class="external-link">https://CRAN.R-project.org/package=mkin</a>.</p> </div> <div id="ref-ranke2012"> -<p>Ranke, J., and R. Lehmann. 2012. “Parameter Reliability in Kinetic Evaluation of Environmental Metabolism Data - Assessment and the Influence of Model Specification.” In <em>SETAC World 20-24 May</em>. Berlin.</p> +<p>Ranke, J., and R. Lehmann. 2012. “Parameter Reliability in Kinetic Evaluation of Environmental Metabolism Data - Assessment and the Influence of Model Specification.” In <em>SETAC World 20-24 May</em>. Berlin. <a href="https://jrwb.de/posters/Poster_SETAC_2012_Kinetic_parameter_uncertainty_model_parameterization_Lehmann_Ranke.pdf" class="external-link">https://jrwb.de/posters/Poster_SETAC_2012_Kinetic_parameter_uncertainty_model_parameterization_Lehmann_Ranke.pdf</a>.</p> </div> <div id="ref-ranke2015"> -<p>———. 2015. “To T-Test or Not to T-Test, That Is the Question.” In <em>XV Symposium on Pesticide Chemistry 2-4 September 2015</em>. Piacenza. <a href="http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf" class="external-link">http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf</a>.</p> +<p>———. 2015. “To T-Test or Not to T-Test, That Is the Question.” In <em>XV Symposium on Pesticide Chemistry 2-4 September 2015</em>. Piacenza. <a href="https://jrwb.de/posters/piacenza_2015.pdf" class="external-link">https://jrwb.de/posters/piacenza_2015.pdf</a>.</p> </div> <div id="ref-ranke2019"> <p>Ranke, Johannes, and Stefan Meinecke. 2019. “Error Models for the Kinetic Evaluation of Chemical Degradation Data.” <em>Environments</em> 6 (12). <a href="https://doi.org/10.3390/environments6120124" class="external-link">https://doi.org/10.3390/environments6120124</a>.</p> @@ -262,7 +271,7 @@ <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer> diff --git a/docs/dev/articles/prebuilt/2022_cyan_pathway.html b/docs/dev/articles/prebuilt/2022_cyan_pathway.html new file mode 100644 index 00000000..87d4ca4a --- /dev/null +++ b/docs/dev/articles/prebuilt/2022_cyan_pathway.html @@ -0,0 +1,5623 @@ +<!DOCTYPE html> +<!-- Generated 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toc-ignore"> + <h1 data-toc-skip>Testing hierarchical pathway kinetics with +residue data on cyantraniliprole</h1> + <h4 data-toc-skip class="author">Johannes +Ranke</h4> + + <h4 data-toc-skip class="date">Last change on 6 January +2023, last compiled on 28 Januar 2023</h4> + + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/prebuilt/2022_cyan_pathway.rmd" class="external-link"><code>vignettes/prebuilt/2022_cyan_pathway.rmd</code></a></small> + <div class="hidden name"><code>2022_cyan_pathway.rmd</code></div> + + </div> + + + +<div class="section level2"> +<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a> +</h2> +<p>The purpose of this document is to test demonstrate how nonlinear +hierarchical models (NLHM) based on the parent degradation models SFO, +FOMC, DFOP and HS, with serial formation of two or more metabolites can +be fitted with the mkin package.</p> +<p>It was assembled in the course of work package 1.2 of Project Number +173340 (Application of nonlinear hierarchical models to the kinetic +evaluation of chemical degradation data) of the German Environment +Agency carried out in 2022 and 2023.</p> +<p>The mkin package is used in version 1.2.2 which is currently under +development. The newly introduced functionality that is used here is a +simplification of excluding random effects for a set of fits based on a +related set of fits with a reduced model, and the documentation of the +starting parameters of the fit, so that all starting parameters of +<code>saem</code> fits are now listed in the summary. The +<code>saemix</code> package is used as a backend for fitting the NLHM, +but is also loaded to make the convergence plot function available.</p> +<p>This document is processed with the <code>knitr</code> package, which +also provides the <code>kable</code> function that is used to improve +the display of tabular data in R markdown documents. For parallel +processing, the <code>parallel</code> package is used.</p> +<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span> +<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://yihui.org/knitr/" class="external-link">knitr</a></span><span class="op">)</span></span> +<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span> +<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">parallel</span><span class="op">)</span></span> +<span><span class="va">n_cores</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span></span> +<span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="op">{</span></span> +<span> <span class="va">cl</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span> +<span><span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span> +<span> <span class="va">cl</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makeForkCluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span> +<span><span class="op">}</span></span></code></pre></div> +<div class="section level3"> +<h3 id="test-data">Test data<a class="anchor" aria-label="anchor" href="#test-data"></a> +</h3> +<p>The example data are taken from the final addendum to the DAR from +2014 and are distributed with the mkin package. Residue data and time +step normalisation factors are read in using the function +<code>read_spreadsheet</code> from the mkin package. This function also +performs the time step normalisation.</p> +<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">data_file</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.file.html" class="external-link">system.file</a></span><span class="op">(</span></span> +<span> <span class="st">"testdata"</span>, <span class="st">"cyantraniliprole_soil_efsa_2014.xlsx"</span>,</span> +<span> package <span class="op">=</span> <span class="st">"mkin"</span><span class="op">)</span></span> +<span><span class="va">cyan_ds</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/read_spreadsheet.html">read_spreadsheet</a></span><span class="op">(</span><span class="va">data_file</span>, parent_only <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> +<p>The following tables show the covariate data and the 5 datasets that +were read in from the spreadsheet file.</p> +<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">pH</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/attr.html" class="external-link">attr</a></span><span class="op">(</span><span class="va">cyan_ds</span>, <span class="st">"covariates"</span><span class="op">)</span></span> +<span><span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="va">pH</span>, caption <span class="op">=</span> <span class="st">"Covariate data"</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<caption>Covariate data</caption> +<thead><tr class="header"> +<th align="left"></th> +<th align="right">pH</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">Nambsheim</td> +<td align="right">7.90</td> +</tr> +<tr class="even"> +<td align="left">Tama</td> +<td align="right">6.20</td> +</tr> +<tr class="odd"> +<td align="left">Gross-Umstadt</td> +<td align="right">7.04</td> +</tr> +<tr class="even"> +<td align="left">Sassafras</td> +<td align="right">4.62</td> +</tr> +<tr class="odd"> +<td align="left">Lleida</td> +<td align="right">8.05</td> +</tr> +</tbody> +</table> +<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="kw">for</span> <span class="op">(</span><span class="va">ds_name</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">cyan_ds</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span> +<span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span></span> +<span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mkin_long_to_wide.html">mkin_long_to_wide</a></span><span class="op">(</span><span class="va">cyan_ds</span><span class="op">[[</span><span class="va">ds_name</span><span class="op">]</span><span class="op">]</span><span class="op">)</span>,</span> +<span> caption <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="va">ds_name</span><span class="op">)</span>,</span> +<span> booktabs <span class="op">=</span> <span class="cn">TRUE</span>, row.names <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">)</span></span> +<span> <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"\n\\clearpage\n"</span><span class="op">)</span></span> +<span><span class="op">}</span></span></code></pre></div> +<table class="table"> +<caption>Dataset Nambsheim</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">cyan</th> +<th align="right">JCZ38</th> +<th align="right">J9C38</th> +<th align="right">JSE76</th> +<th align="right">J9Z38</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">105.79</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">3.210424</td> +<td align="right">77.26</td> +<td align="right">7.92</td> +<td align="right">11.94</td> +<td align="right">5.58</td> +<td align="right">9.12</td> +</tr> +<tr class="odd"> +<td align="right">7.490988</td> +<td align="right">57.13</td> +<td align="right">15.46</td> +<td align="right">16.58</td> +<td align="right">12.59</td> +<td align="right">11.74</td> +</tr> +<tr class="even"> +<td align="right">17.122259</td> +<td align="right">37.74</td> +<td align="right">15.98</td> +<td align="right">13.36</td> +<td align="right">26.05</td> +<td align="right">10.77</td> +</tr> +<tr class="odd"> +<td align="right">23.543105</td> +<td align="right">31.47</td> +<td align="right">6.05</td> +<td align="right">14.49</td> +<td align="right">34.71</td> +<td align="right">4.96</td> +</tr> +<tr class="even"> +<td align="right">43.875788</td> +<td align="right">16.74</td> +<td align="right">6.07</td> +<td align="right">7.57</td> +<td align="right">40.38</td> +<td align="right">6.52</td> +</tr> +<tr class="odd"> +<td align="right">67.418893</td> +<td align="right">8.85</td> +<td align="right">10.34</td> +<td align="right">6.39</td> +<td align="right">30.71</td> +<td align="right">8.90</td> +</tr> +<tr class="even"> +<td align="right">107.014116</td> +<td align="right">5.19</td> +<td align="right">9.61</td> +<td align="right">1.95</td> +<td align="right">20.41</td> +<td align="right">12.93</td> +</tr> +<tr class="odd"> +<td align="right">129.487080</td> +<td align="right">3.45</td> +<td align="right">6.18</td> +<td align="right">1.36</td> +<td align="right">21.78</td> +<td align="right">6.99</td> +</tr> +<tr class="even"> +<td align="right">195.835832</td> +<td align="right">2.15</td> +<td align="right">9.13</td> +<td align="right">0.95</td> +<td align="right">16.29</td> +<td align="right">7.69</td> +</tr> +<tr class="odd"> +<td align="right">254.693596</td> +<td align="right">1.92</td> +<td align="right">6.92</td> +<td align="right">0.20</td> +<td align="right">13.57</td> +<td align="right">7.16</td> +</tr> +<tr class="even"> +<td align="right">321.042348</td> +<td align="right">2.26</td> +<td align="right">7.02</td> +<td align="right">NA</td> +<td align="right">11.12</td> +<td align="right">8.66</td> +</tr> +<tr class="odd"> +<td align="right">383.110535</td> +<td align="right">NA</td> +<td align="right">5.05</td> +<td align="right">NA</td> +<td align="right">10.64</td> +<td align="right">5.56</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">105.57</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">3.210424</td> +<td align="right">78.88</td> +<td align="right">12.77</td> +<td align="right">11.94</td> +<td align="right">5.47</td> +<td align="right">9.12</td> +</tr> +<tr class="even"> +<td align="right">7.490988</td> +<td align="right">59.94</td> +<td align="right">15.27</td> +<td align="right">16.58</td> +<td align="right">13.60</td> +<td align="right">11.74</td> +</tr> +<tr class="odd"> +<td align="right">17.122259</td> +<td align="right">39.67</td> +<td align="right">14.26</td> +<td align="right">13.36</td> +<td align="right">29.44</td> +<td align="right">10.77</td> +</tr> +<tr class="even"> +<td align="right">23.543105</td> +<td align="right">30.21</td> +<td align="right">16.07</td> +<td align="right">14.49</td> +<td align="right">35.90</td> +<td align="right">4.96</td> +</tr> +<tr class="odd"> +<td align="right">43.875788</td> +<td align="right">18.06</td> +<td align="right">9.44</td> +<td align="right">7.57</td> +<td align="right">42.30</td> +<td align="right">6.52</td> +</tr> +<tr class="even"> +<td align="right">67.418893</td> +<td align="right">8.54</td> +<td align="right">5.78</td> +<td align="right">6.39</td> +<td align="right">34.70</td> +<td align="right">8.90</td> +</tr> +<tr class="odd"> +<td align="right">107.014116</td> +<td align="right">7.26</td> +<td align="right">4.54</td> +<td align="right">1.95</td> +<td align="right">23.33</td> +<td align="right">12.93</td> +</tr> +<tr class="even"> +<td align="right">129.487080</td> +<td align="right">3.60</td> +<td align="right">4.22</td> +<td align="right">1.36</td> +<td align="right">23.56</td> +<td align="right">6.99</td> +</tr> +<tr class="odd"> +<td align="right">195.835832</td> +<td align="right">2.84</td> +<td align="right">3.05</td> +<td align="right">0.95</td> +<td align="right">16.21</td> +<td align="right">7.69</td> +</tr> +<tr class="even"> +<td align="right">254.693596</td> +<td align="right">2.00</td> +<td align="right">2.90</td> +<td align="right">0.20</td> +<td align="right">15.53</td> +<td align="right">7.16</td> +</tr> +<tr class="odd"> +<td align="right">321.042348</td> +<td align="right">1.79</td> +<td align="right">0.94</td> +<td align="right">NA</td> +<td align="right">9.80</td> +<td align="right">8.66</td> +</tr> +<tr class="even"> +<td align="right">383.110535</td> +<td align="right">NA</td> +<td align="right">1.82</td> +<td align="right">NA</td> +<td align="right">9.49</td> +<td align="right">5.56</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Tama</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">cyan</th> +<th align="right">JCZ38</th> +<th align="right">J9Z38</th> +<th align="right">JSE76</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">106.14</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">2.400833</td> +<td align="right">93.47</td> +<td align="right">6.46</td> +<td align="right">2.85</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">5.601943</td> +<td align="right">88.39</td> +<td align="right">10.86</td> +<td align="right">4.65</td> +<td align="right">3.85</td> +</tr> +<tr class="even"> +<td align="right">12.804442</td> +<td align="right">72.29</td> +<td align="right">11.97</td> +<td align="right">4.91</td> +<td align="right">11.24</td> +</tr> +<tr class="odd"> +<td align="right">17.606108</td> +<td align="right">65.79</td> +<td align="right">13.11</td> +<td align="right">6.63</td> +<td align="right">13.79</td> +</tr> +<tr class="even"> +<td align="right">32.811382</td> +<td align="right">53.16</td> +<td align="right">11.24</td> +<td align="right">8.90</td> +<td align="right">23.40</td> +</tr> +<tr class="odd"> +<td align="right">50.417490</td> +<td align="right">44.01</td> +<td align="right">11.34</td> +<td align="right">9.98</td> +<td align="right">29.56</td> +</tr> +<tr class="even"> +<td align="right">80.027761</td> +<td align="right">33.23</td> +<td align="right">8.82</td> +<td align="right">11.31</td> +<td align="right">35.63</td> +</tr> +<tr class="odd"> +<td align="right">96.833591</td> +<td align="right">40.68</td> +<td align="right">5.94</td> +<td align="right">8.32</td> +<td align="right">29.09</td> +</tr> +<tr class="even"> +<td align="right">146.450803</td> +<td align="right">20.65</td> +<td align="right">4.49</td> +<td align="right">8.72</td> +<td align="right">36.88</td> +</tr> +<tr class="odd"> +<td align="right">190.466072</td> +<td align="right">17.71</td> +<td align="right">4.66</td> +<td align="right">11.10</td> +<td align="right">40.97</td> +</tr> +<tr class="even"> +<td align="right">240.083284</td> +<td align="right">14.86</td> +<td align="right">2.27</td> +<td align="right">11.62</td> +<td align="right">40.11</td> +</tr> +<tr class="odd"> +<td align="right">286.499386</td> +<td align="right">12.02</td> +<td align="right">NA</td> +<td align="right">10.73</td> +<td align="right">42.58</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">109.11</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">2.400833</td> +<td align="right">96.84</td> +<td align="right">5.52</td> +<td align="right">2.04</td> +<td align="right">2.02</td> +</tr> +<tr class="even"> +<td align="right">5.601943</td> +<td align="right">85.29</td> +<td align="right">9.65</td> +<td align="right">2.99</td> +<td align="right">4.39</td> +</tr> +<tr class="odd"> +<td align="right">12.804442</td> +<td align="right">73.68</td> +<td align="right">12.48</td> +<td align="right">5.05</td> +<td align="right">11.47</td> +</tr> +<tr class="even"> +<td align="right">17.606108</td> +<td align="right">64.89</td> +<td align="right">12.44</td> +<td align="right">6.29</td> +<td align="right">15.00</td> +</tr> +<tr class="odd"> +<td align="right">32.811382</td> +<td align="right">52.27</td> +<td align="right">10.86</td> +<td align="right">7.65</td> +<td align="right">23.30</td> +</tr> +<tr class="even"> +<td align="right">50.417490</td> +<td align="right">42.61</td> +<td align="right">10.54</td> +<td align="right">9.37</td> +<td align="right">31.06</td> +</tr> +<tr class="odd"> +<td align="right">80.027761</td> +<td align="right">34.29</td> +<td align="right">10.02</td> +<td align="right">9.04</td> +<td align="right">37.87</td> +</tr> +<tr class="even"> +<td align="right">96.833591</td> +<td align="right">30.50</td> +<td align="right">6.34</td> +<td align="right">8.14</td> +<td align="right">33.97</td> +</tr> +<tr class="odd"> +<td align="right">146.450803</td> +<td align="right">19.21</td> +<td align="right">6.29</td> +<td align="right">8.52</td> +<td align="right">26.15</td> +</tr> +<tr class="even"> +<td align="right">190.466072</td> +<td align="right">17.55</td> +<td align="right">5.81</td> +<td align="right">9.89</td> +<td align="right">32.08</td> +</tr> +<tr class="odd"> +<td align="right">240.083284</td> +<td align="right">13.22</td> +<td align="right">5.99</td> +<td align="right">10.79</td> +<td align="right">40.66</td> +</tr> +<tr class="even"> +<td align="right">286.499386</td> +<td align="right">11.09</td> +<td align="right">6.05</td> +<td align="right">8.82</td> +<td align="right">42.90</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Gross-Umstadt</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">cyan</th> +<th align="right">JCZ38</th> +<th align="right">J9Z38</th> +<th align="right">JSE76</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">103.03</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">2.1014681</td> +<td align="right">87.85</td> +<td align="right">4.79</td> +<td align="right">3.26</td> +<td align="right">0.62</td> +</tr> +<tr class="odd"> +<td align="right">4.9034255</td> +<td align="right">77.35</td> +<td align="right">8.05</td> +<td align="right">9.89</td> +<td align="right">1.32</td> +</tr> +<tr class="even"> +<td align="right">10.5073404</td> +<td align="right">69.33</td> +<td align="right">9.74</td> +<td align="right">12.32</td> +<td align="right">4.74</td> +</tr> +<tr class="odd"> +<td align="right">21.0146807</td> +<td align="right">55.65</td> +<td align="right">14.57</td> +<td align="right">13.59</td> +<td align="right">9.84</td> +</tr> +<tr class="even"> +<td align="right">31.5220211</td> +<td align="right">49.03</td> +<td align="right">14.66</td> +<td align="right">16.71</td> +<td align="right">12.32</td> +</tr> +<tr class="odd"> +<td align="right">42.0293615</td> +<td align="right">41.86</td> +<td align="right">15.97</td> +<td align="right">13.64</td> +<td align="right">15.53</td> +</tr> +<tr class="even"> +<td align="right">63.0440422</td> +<td align="right">34.88</td> +<td align="right">18.20</td> +<td align="right">14.12</td> +<td align="right">22.02</td> +</tr> +<tr class="odd"> +<td align="right">84.0587230</td> +<td align="right">28.26</td> +<td align="right">15.64</td> +<td align="right">14.06</td> +<td align="right">25.60</td> +</tr> +<tr class="even"> +<td align="right">0.0000000</td> +<td align="right">104.05</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">2.1014681</td> +<td align="right">85.25</td> +<td align="right">2.68</td> +<td align="right">7.32</td> +<td align="right">0.69</td> +</tr> +<tr class="even"> +<td align="right">4.9034255</td> +<td align="right">77.22</td> +<td align="right">7.28</td> +<td align="right">8.37</td> +<td align="right">1.45</td> +</tr> +<tr class="odd"> +<td align="right">10.5073404</td> +<td align="right">65.23</td> +<td align="right">10.73</td> +<td align="right">10.93</td> +<td align="right">4.74</td> +</tr> +<tr class="even"> +<td align="right">21.0146807</td> +<td align="right">57.78</td> +<td align="right">12.29</td> +<td align="right">14.80</td> +<td align="right">9.05</td> +</tr> +<tr class="odd"> +<td align="right">31.5220211</td> +<td align="right">54.83</td> +<td align="right">14.05</td> +<td align="right">12.01</td> +<td align="right">11.05</td> +</tr> +<tr class="even"> +<td align="right">42.0293615</td> +<td align="right">45.17</td> +<td align="right">12.12</td> +<td align="right">17.89</td> +<td align="right">15.71</td> +</tr> +<tr class="odd"> +<td align="right">63.0440422</td> +<td align="right">34.83</td> +<td align="right">12.90</td> +<td align="right">15.86</td> +<td align="right">22.52</td> +</tr> +<tr class="even"> +<td align="right">84.0587230</td> +<td align="right">26.59</td> +<td align="right">14.28</td> +<td align="right">14.91</td> +<td align="right">28.48</td> +</tr> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">104.62</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">0.8145225</td> +<td align="right">97.21</td> +<td align="right">NA</td> +<td align="right">4.00</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">1.9005525</td> +<td align="right">89.64</td> +<td align="right">3.59</td> +<td align="right">5.24</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">4.0726125</td> +<td align="right">87.90</td> +<td align="right">4.10</td> +<td align="right">9.58</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">8.1452251</td> +<td align="right">86.90</td> +<td align="right">5.96</td> +<td align="right">9.45</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">12.2178376</td> +<td align="right">74.74</td> +<td align="right">7.83</td> +<td align="right">15.03</td> +<td align="right">5.33</td> +</tr> +<tr class="odd"> +<td align="right">16.2904502</td> +<td align="right">74.13</td> +<td align="right">8.84</td> +<td align="right">14.41</td> +<td align="right">5.10</td> +</tr> +<tr class="even"> +<td align="right">24.4356753</td> +<td align="right">65.26</td> +<td align="right">11.84</td> +<td align="right">18.33</td> +<td align="right">6.71</td> +</tr> +<tr class="odd"> +<td align="right">32.5809004</td> +<td align="right">57.70</td> +<td align="right">12.74</td> +<td align="right">19.93</td> +<td align="right">9.74</td> +</tr> +<tr class="even"> +<td align="right">0.0000000</td> +<td align="right">101.94</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">0.8145225</td> +<td align="right">99.94</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">1.9005525</td> +<td align="right">94.87</td> +<td align="right">NA</td> +<td align="right">4.56</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">4.0726125</td> +<td align="right">86.96</td> +<td align="right">6.75</td> +<td align="right">6.90</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">8.1452251</td> +<td align="right">80.51</td> +<td align="right">10.68</td> +<td align="right">7.43</td> +<td align="right">2.58</td> +</tr> +<tr class="odd"> +<td align="right">12.2178376</td> +<td align="right">78.38</td> +<td align="right">10.35</td> +<td align="right">9.46</td> +<td align="right">3.69</td> +</tr> +<tr class="even"> +<td align="right">16.2904502</td> +<td align="right">70.05</td> +<td align="right">13.73</td> +<td align="right">9.27</td> +<td align="right">7.18</td> +</tr> +<tr class="odd"> +<td align="right">24.4356753</td> +<td align="right">61.28</td> +<td align="right">12.57</td> +<td align="right">13.28</td> +<td align="right">13.19</td> +</tr> +<tr class="even"> +<td align="right">32.5809004</td> +<td align="right">52.85</td> +<td align="right">12.67</td> +<td align="right">12.95</td> +<td align="right">13.69</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Sassafras</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">cyan</th> +<th align="right">JCZ38</th> +<th align="right">J9Z38</th> +<th align="right">JSE76</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">102.17</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">2.216719</td> +<td align="right">95.49</td> +<td align="right">1.11</td> +<td align="right">0.10</td> +<td align="right">0.83</td> +</tr> +<tr class="odd"> +<td align="right">5.172343</td> +<td align="right">83.35</td> +<td align="right">6.43</td> +<td align="right">2.89</td> +<td align="right">3.30</td> +</tr> +<tr class="even"> +<td align="right">11.083593</td> +<td align="right">78.18</td> +<td align="right">10.00</td> +<td align="right">5.59</td> +<td align="right">0.81</td> +</tr> +<tr class="odd"> +<td align="right">22.167186</td> +<td align="right">70.44</td> +<td align="right">17.21</td> +<td align="right">4.23</td> +<td align="right">1.09</td> +</tr> +<tr class="even"> +<td align="right">33.250779</td> +<td align="right">68.00</td> +<td align="right">20.45</td> +<td align="right">5.86</td> +<td align="right">1.17</td> +</tr> +<tr class="odd"> +<td align="right">44.334371</td> +<td align="right">59.64</td> +<td align="right">24.64</td> +<td align="right">3.17</td> +<td align="right">2.72</td> +</tr> +<tr class="even"> +<td align="right">66.501557</td> +<td align="right">50.73</td> +<td align="right">27.50</td> +<td align="right">6.19</td> +<td align="right">1.27</td> +</tr> +<tr class="odd"> +<td align="right">88.668742</td> +<td align="right">45.65</td> +<td align="right">32.77</td> +<td align="right">5.69</td> +<td align="right">4.54</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">100.43</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">2.216719</td> +<td align="right">95.34</td> +<td align="right">3.21</td> +<td align="right">0.14</td> +<td align="right">0.46</td> +</tr> +<tr class="even"> +<td align="right">5.172343</td> +<td align="right">84.38</td> +<td align="right">5.73</td> +<td align="right">4.75</td> +<td align="right">0.62</td> +</tr> +<tr class="odd"> +<td align="right">11.083593</td> +<td align="right">78.50</td> +<td align="right">11.89</td> +<td align="right">3.99</td> +<td align="right">0.73</td> +</tr> +<tr class="even"> +<td align="right">22.167186</td> +<td align="right">71.17</td> +<td align="right">17.28</td> +<td align="right">4.39</td> +<td align="right">0.66</td> +</tr> +<tr class="odd"> +<td align="right">33.250779</td> +<td align="right">59.41</td> +<td align="right">18.73</td> +<td align="right">11.85</td> +<td align="right">2.65</td> +</tr> +<tr class="even"> +<td align="right">44.334371</td> +<td align="right">64.57</td> +<td align="right">22.93</td> +<td align="right">5.13</td> +<td align="right">2.01</td> +</tr> +<tr class="odd"> +<td align="right">66.501557</td> +<td align="right">49.08</td> +<td align="right">33.39</td> +<td align="right">5.67</td> +<td align="right">3.63</td> +</tr> +<tr class="even"> +<td align="right">88.668742</td> +<td align="right">40.41</td> +<td align="right">39.60</td> +<td align="right">5.93</td> +<td align="right">6.17</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Lleida</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">cyan</th> +<th align="right">JCZ38</th> +<th align="right">J9Z38</th> +<th align="right">JSE76</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">102.71</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">2.821051</td> +<td align="right">79.11</td> +<td align="right">5.70</td> +<td align="right">8.07</td> +<td align="right">0.97</td> +</tr> +<tr class="odd"> +<td align="right">6.582451</td> +<td align="right">70.03</td> +<td align="right">7.17</td> +<td align="right">11.31</td> +<td align="right">4.72</td> +</tr> +<tr class="even"> +<td align="right">14.105253</td> +<td align="right">50.93</td> +<td align="right">10.25</td> +<td align="right">14.84</td> +<td align="right">9.95</td> +</tr> +<tr class="odd"> +<td align="right">28.210505</td> +<td align="right">33.43</td> +<td align="right">10.40</td> +<td align="right">14.82</td> +<td align="right">24.06</td> +</tr> +<tr class="even"> +<td align="right">42.315758</td> +<td align="right">24.69</td> +<td align="right">9.75</td> +<td align="right">16.38</td> +<td align="right">29.38</td> +</tr> +<tr class="odd"> +<td align="right">56.421010</td> +<td align="right">22.99</td> +<td align="right">10.06</td> +<td align="right">15.51</td> +<td align="right">29.25</td> +</tr> +<tr class="even"> +<td align="right">84.631516</td> +<td align="right">14.63</td> +<td align="right">5.63</td> +<td align="right">14.74</td> +<td align="right">31.04</td> +</tr> +<tr class="odd"> +<td align="right">112.842021</td> +<td align="right">12.43</td> +<td align="right">4.17</td> +<td align="right">13.53</td> +<td align="right">33.28</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">99.31</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">2.821051</td> +<td align="right">82.07</td> +<td align="right">6.55</td> +<td align="right">5.60</td> +<td align="right">1.12</td> +</tr> +<tr class="even"> +<td align="right">6.582451</td> +<td align="right">70.65</td> +<td align="right">7.61</td> +<td align="right">8.01</td> +<td align="right">3.21</td> +</tr> +<tr class="odd"> +<td align="right">14.105253</td> +<td align="right">53.52</td> +<td align="right">11.48</td> +<td align="right">10.82</td> +<td align="right">12.24</td> +</tr> +<tr class="even"> +<td align="right">28.210505</td> +<td align="right">35.60</td> +<td align="right">11.19</td> +<td align="right">15.43</td> +<td align="right">23.53</td> +</tr> +<tr class="odd"> +<td align="right">42.315758</td> +<td align="right">34.26</td> +<td align="right">11.09</td> +<td align="right">13.26</td> +<td align="right">27.42</td> +</tr> +<tr class="even"> +<td align="right">56.421010</td> +<td align="right">21.79</td> +<td align="right">4.80</td> +<td align="right">18.30</td> +<td align="right">30.20</td> +</tr> +<tr class="odd"> +<td align="right">84.631516</td> +<td align="right">14.06</td> +<td align="right">6.30</td> +<td align="right">16.35</td> +<td align="right">32.32</td> +</tr> +<tr class="even"> +<td align="right">112.842021</td> +<td align="right">11.51</td> +<td align="right">5.57</td> +<td align="right">12.64</td> +<td align="right">32.51</td> +</tr> +</tbody> +</table> +</div> +</div> +<div class="section level2"> +<h2 id="parent-only-evaluations">Parent only evaluations<a class="anchor" aria-label="anchor" href="#parent-only-evaluations"></a> +</h2> +<p>As the pathway fits have very long run times, evaluations of the +parent data are performed first, in order to determine for each +hierarchical parent degradation model which random effects on the +degradation model parameters are ill-defined.</p> +<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">cyan_sep_const</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>, <span class="st">"SFORB"</span>, <span class="st">"HS"</span><span class="op">)</span>,</span> +<span> <span class="va">cyan_ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, cores <span class="op">=</span> <span class="va">n_cores</span><span class="op">)</span></span> +<span><span class="va">cyan_sep_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">cyan_sep_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span> +<span><span class="va">cyan_saem_full</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">cyan_sep_const</span>, <span class="va">cyan_sep_tc</span><span class="op">)</span><span class="op">)</span></span> +<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">cyan_saem_full</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">SFORB</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">HS</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>All fits converged successfully.</p> +<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">cyan_saem_full</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left">sd(cyan_0)</td> +<td align="left">sd(cyan_0)</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left">sd(log_beta)</td> +<td align="left">sd(cyan_0)</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">sd(cyan_0)</td> +<td align="left">sd(cyan_0), sd(log_k1)</td> +</tr> +<tr class="even"> +<td align="left">SFORB</td> +<td align="left">sd(cyan_free_0)</td> +<td align="left">sd(cyan_free_0), sd(log_k_cyan_free_bound)</td> +</tr> +<tr class="odd"> +<td align="left">HS</td> +<td align="left">sd(cyan_0)</td> +<td align="left">sd(cyan_0)</td> +</tr> +</tbody> +</table> +<p>In almost all models, the random effect for the initial concentration +of the parent compound is ill-defined. For the biexponential models DFOP +and SFORB, the random effect of one additional parameter is ill-defined +when the two-component error model is used.</p> +<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">cyan_saem_full</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="right">npar</th> +<th align="right">AIC</th> +<th align="right">BIC</th> +<th align="right">Lik</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">SFO const</td> +<td align="right">5</td> +<td align="right">833.9</td> +<td align="right">832.0</td> +<td align="right">-412.0</td> +</tr> +<tr class="even"> +<td align="left">SFO tc</td> +<td align="right">6</td> +<td align="right">831.6</td> +<td align="right">829.3</td> +<td align="right">-409.8</td> +</tr> +<tr class="odd"> +<td align="left">FOMC const</td> +<td align="right">7</td> +<td align="right">709.1</td> +<td align="right">706.4</td> +<td align="right">-347.6</td> +</tr> +<tr class="even"> +<td align="left">FOMC tc</td> +<td align="right">8</td> +<td align="right">689.2</td> +<td align="right">686.1</td> +<td align="right">-336.6</td> +</tr> +<tr class="odd"> +<td align="left">DFOP const</td> +<td align="right">9</td> +<td align="right">703.0</td> +<td align="right">699.5</td> +<td align="right">-342.5</td> +</tr> +<tr class="even"> +<td align="left">SFORB const</td> +<td align="right">9</td> +<td align="right">701.3</td> +<td align="right">697.8</td> +<td align="right">-341.7</td> +</tr> +<tr class="odd"> +<td align="left">HS const</td> +<td align="right">9</td> +<td align="right">718.6</td> +<td align="right">715.1</td> +<td align="right">-350.3</td> +</tr> +<tr class="even"> +<td align="left">DFOP tc</td> +<td align="right">10</td> +<td align="right">703.1</td> +<td align="right">699.2</td> +<td align="right">-341.6</td> +</tr> +<tr class="odd"> +<td align="left">SFORB tc</td> +<td align="right">10</td> +<td align="right">700.1</td> +<td align="right">696.2</td> +<td align="right">-340.1</td> +</tr> +<tr class="even"> +<td align="left">HS tc</td> +<td align="right">10</td> +<td align="right">716.7</td> +<td align="right">712.8</td> +<td align="right">-348.3</td> +</tr> +</tbody> +</table> +<p>Model comparison based on AIC and BIC indicates that the +two-component error model is preferable for all parent models with the +exception of DFOP. The lowest AIC and BIC values are are obtained with +the FOMC model, followed by SFORB and DFOP.</p> +</div> +<div class="section level2"> +<h2 id="pathway-fits">Pathway fits<a class="anchor" aria-label="anchor" href="#pathway-fits"></a> +</h2> +<div class="section level3"> +<h3 id="evaluations-with-pathway-established-previously">Evaluations with pathway established previously<a class="anchor" aria-label="anchor" href="#evaluations-with-pathway-established-previously"></a> +</h3> +<p>To test the technical feasibility of coupling the relevant parent +degradation models with different transformation pathway models, a list +of <code>mkinmod</code> models is set up below. As in the EU evaluation, +parallel formation of metabolites JCZ38 and J9Z38 and secondary +formation of metabolite JSE76 from JCZ38 is used.</p> +<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="kw">if</span> <span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.exists</a></span><span class="op">(</span><span class="st">"cyan_dlls"</span><span class="op">)</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.create</a></span><span class="op">(</span><span class="st">"cyan_dlls"</span><span class="op">)</span></span> +<span><span class="va">cyan_path_1</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span> +<span> sfo_path_1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span> +<span> J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> name <span class="op">=</span> <span class="st">"sfo_path_1"</span>, dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span> +<span> fomc_path_1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span> +<span> J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> name <span class="op">=</span> <span class="st">"fomc_path_1"</span>, dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span> +<span> dfop_path_1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span> +<span> J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> name <span class="op">=</span> <span class="st">"dfop_path_1"</span>, dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span> +<span> sforb_path_1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span> +<span> J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> name <span class="op">=</span> <span class="st">"sforb_path_1"</span>, dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span> +<span> hs_path_1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"HS"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span> +<span> J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> name <span class="op">=</span> <span class="st">"hs_path_1"</span>, dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span><span class="op">)</span></span></code></pre></div> +<p>To obtain suitable starting values for the NLHM fits, separate +pathway fits are performed for all datasets.</p> +<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_sep_1_const</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span></span> +<span> <span class="va">cyan_path_1</span>,</span> +<span> <span class="va">cyan_ds</span>,</span> +<span> error_model <span class="op">=</span> <span class="st">"const"</span>,</span> +<span> cluster <span class="op">=</span> <span class="va">cl</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_1_const</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">Nambsheim</th> +<th align="left">Tama</th> +<th align="left">Gross-Umstadt</th> +<th align="left">Sassafras</th> +<th align="left">Lleida</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">sfo_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">hs_path_1</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">C</td> +</tr> +</tbody> +</table> +<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_sep_1_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_sep_1_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span> +<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_1_tc</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">Nambsheim</th> +<th align="left">Tama</th> +<th align="left">Gross-Umstadt</th> +<th align="left">Sassafras</th> +<th align="left">Lleida</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">sfo_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_1</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">hs_path_1</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>Most separate fits converged successfully. The biggest convergence +problems are seen when using the HS model with constant variance.</p> +<p>For the hierarchical pathway fits, those random effects that could +not be quantified in the corresponding parent data analyses are +excluded.</p> +<p>In the code below, the output of the <code>illparms</code> function +for the parent only fits is used as an argument +<code>no_random_effect</code> to the <code>mhmkin</code> function. The +possibility to do so was introduced in mkin version <code>1.2.2</code> +which is currently under development.</p> +<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_saem_1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_sep_1_const</span>, <span class="va">f_sep_1_tc</span><span class="op">)</span>,</span> +<span> no_random_effect <span class="op">=</span> <span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">cyan_saem_full</span><span class="op">)</span>,</span> +<span> cluster <span class="op">=</span> <span class="va">cl</span><span class="op">)</span></span></code></pre></div> +<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">sfo_path_1</td> +<td align="left">Fth, FO</td> +<td align="left">Fth, FO</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_1</td> +<td align="left">OK</td> +<td align="left">Fth, FO</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1</td> +<td align="left">Fth, FO</td> +<td align="left">Fth, FO</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_1</td> +<td align="left">Fth, FO</td> +<td align="left">Fth, FO</td> +</tr> +<tr class="odd"> +<td align="left">hs_path_1</td> +<td align="left">Fth, FO</td> +<td align="left">Fth, FO</td> +</tr> +</tbody> +</table> +<p>The status information from the individual fits shows that all fits +completed successfully. The matrix entries Fth and FO indicate that the +Fisher Information Matrix could not be inverted for the fixed effects +(theta) and the random effects (Omega), respectively. For the affected +fits, ill-defined parameters cannot be determined using the +<code>illparms</code> function, because it relies on the Fisher +Information Matrix.</p> +<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<colgroup> +<col width="18%"> +<col width="77%"> +<col width="4%"> +</colgroup> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">sfo_path_1</td> +<td align="left">NA</td> +<td align="left">NA</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_1</td> +<td align="left">sd(log_k_J9Z38), sd(f_cyan_ilr_2), +sd(f_JCZ38_qlogis)</td> +<td align="left">NA</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1</td> +<td align="left">NA</td> +<td align="left">NA</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_1</td> +<td align="left">NA</td> +<td align="left">NA</td> +</tr> +<tr class="odd"> +<td align="left">hs_path_1</td> +<td align="left">NA</td> +<td align="left">NA</td> +</tr> +</tbody> +</table> +<p>The model comparison below suggests that the pathway fits using DFOP +or SFORB for the parent compound provide the best fit.</p> +<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="right">npar</th> +<th align="right">AIC</th> +<th align="right">BIC</th> +<th align="right">Lik</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">sfo_path_1 const</td> +<td align="right">16</td> +<td align="right">2692.8</td> +<td align="right">2686.6</td> +<td align="right">-1330.4</td> +</tr> +<tr class="even"> +<td align="left">sfo_path_1 tc</td> +<td align="right">17</td> +<td align="right">2657.7</td> +<td align="right">2651.1</td> +<td align="right">-1311.9</td> +</tr> +<tr class="odd"> +<td align="left">fomc_path_1 const</td> +<td align="right">18</td> +<td align="right">2427.8</td> +<td align="right">2420.8</td> +<td align="right">-1195.9</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_1 tc</td> +<td align="right">19</td> +<td align="right">2423.4</td> +<td align="right">2416.0</td> +<td align="right">-1192.7</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1 const</td> +<td align="right">20</td> +<td align="right">2403.2</td> +<td align="right">2395.4</td> +<td align="right">-1181.6</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_1 const</td> +<td align="right">20</td> +<td align="right">2401.4</td> +<td align="right">2393.6</td> +<td align="right">-1180.7</td> +</tr> +<tr class="odd"> +<td align="left">hs_path_1 const</td> +<td align="right">20</td> +<td align="right">2427.3</td> +<td align="right">2419.5</td> +<td align="right">-1193.7</td> +</tr> +<tr class="even"> +<td align="left">dfop_path_1 tc</td> +<td align="right">20</td> +<td align="right">2398.0</td> +<td align="right">2390.2</td> +<td align="right">-1179.0</td> +</tr> +<tr class="odd"> +<td align="left">sforb_path_1 tc</td> +<td align="right">20</td> +<td align="right">2399.8</td> +<td align="right">2392.0</td> +<td align="right">-1179.9</td> +</tr> +<tr class="even"> +<td align="left">hs_path_1 tc</td> +<td align="right">21</td> +<td align="right">2422.3</td> +<td align="right">2414.1</td> +<td align="right">-1190.2</td> +</tr> +</tbody> +</table> +<p>For these two parent model, successful fits are shown below. Plots of +the fits with the other parent models are shown in the Appendix.</p> +<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">[[</span><span class="st">"dfop_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-6-1.png" alt="DFOP pathway fit with two-component error" width="700"><p class="caption"> +DFOP pathway fit with two-component error +</p> +</div> +<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-7-1.png" alt="SFORB pathway fit with two-component error" width="700"><p class="caption"> +SFORB pathway fit with two-component error +</p> +</div> +<p>A closer graphical analysis of these Figures shows that the residues +of transformation product JCZ38 in the soils Tama and Nambsheim observed +at later time points are strongly and systematically underestimated.</p> +</div> +<div class="section level3"> +<h3 id="alternative-pathway-fits">Alternative pathway fits<a class="anchor" aria-label="anchor" href="#alternative-pathway-fits"></a> +</h3> +<p>To improve the fit for JCZ38, a back-reaction from JSE76 to JCZ38 was +introduced in an alternative version of the transformation pathway, in +analogy to the back-reaction from K5A78 to K5A77. Both pairs of +transformation products are pairs of an organic acid with its +corresponding amide (Addendum 2014, p. 109). As FOMC provided the best +fit for the parent, and the biexponential models DFOP and SFORB provided +the best initial pathway fits, these three parent models are used in the +alternative pathway fits.</p> +<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">cyan_path_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span> +<span> fomc_path_2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span> +<span> J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JCZ38"</span><span class="op">)</span>,</span> +<span> name <span class="op">=</span> <span class="st">"fomc_path_2"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>,</span> +<span> overwrite <span class="op">=</span> <span class="cn">TRUE</span></span> +<span> <span class="op">)</span>,</span> +<span> dfop_path_2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span> +<span> J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JCZ38"</span><span class="op">)</span>,</span> +<span> name <span class="op">=</span> <span class="st">"dfop_path_2"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>,</span> +<span> overwrite <span class="op">=</span> <span class="cn">TRUE</span></span> +<span> <span class="op">)</span>,</span> +<span> sforb_path_2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span> +<span> J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JCZ38"</span><span class="op">)</span>,</span> +<span> name <span class="op">=</span> <span class="st">"sforb_path_2"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>,</span> +<span> overwrite <span class="op">=</span> <span class="cn">TRUE</span></span> +<span> <span class="op">)</span></span> +<span><span class="op">)</span></span> +<span><span class="va">f_sep_2_const</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span></span> +<span> <span class="va">cyan_path_2</span>,</span> +<span> <span class="va">cyan_ds</span>,</span> +<span> error_model <span class="op">=</span> <span class="st">"const"</span>,</span> +<span> cluster <span class="op">=</span> <span class="va">cl</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span></span> +<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_2_const</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">Nambsheim</th> +<th align="left">Tama</th> +<th align="left">Gross-Umstadt</th> +<th align="left">Sassafras</th> +<th align="left">Lleida</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">fomc_path_2</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">dfop_path_2</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">sforb_path_2</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>Using constant variance, separate fits converge with the exception of +the fits to the Sassafras soil data.</p> +<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_sep_2_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_sep_2_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span> +<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_2_tc</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">Nambsheim</th> +<th align="left">Tama</th> +<th align="left">Gross-Umstadt</th> +<th align="left">Sassafras</th> +<th align="left">Lleida</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">fomc_path_2</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">dfop_path_2</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">sforb_path_2</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>Using the two-component error model, all separate fits converge with +the exception of the alternative pathway fit with DFOP used for the +parent and the Sassafras dataset.</p> +<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_saem_2</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_sep_2_const</span>, <span class="va">f_sep_2_tc</span><span class="op">)</span>,</span> +<span> no_random_effect <span class="op">=</span> <span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">cyan_saem_full</span><span class="op">[</span><span class="fl">2</span><span class="op">:</span><span class="fl">4</span>, <span class="op">]</span><span class="op">)</span>,</span> +<span> cluster <span class="op">=</span> <span class="va">cl</span><span class="op">)</span></span></code></pre></div> +<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">fomc_path_2</td> +<td align="left">OK</td> +<td align="left">FO</td> +</tr> +<tr class="even"> +<td align="left">dfop_path_2</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">sforb_path_2</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>The hierarchical fits for the alternative pathway completed +successfully.</p> +<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<colgroup> +<col width="14%"> +<col width="42%"> +<col width="42%"> +</colgroup> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">fomc_path_2</td> +<td align="left">sd(f_JCZ38_qlogis), sd(f_JSE76_qlogis)</td> +<td align="left">NA</td> +</tr> +<tr class="even"> +<td align="left">dfop_path_2</td> +<td align="left">sd(f_JCZ38_qlogis), sd(f_JSE76_qlogis)</td> +<td align="left">sd(f_JCZ38_qlogis), sd(f_JSE76_qlogis)</td> +</tr> +<tr class="odd"> +<td align="left">sforb_path_2</td> +<td align="left">sd(f_JCZ38_qlogis), sd(f_JSE76_qlogis)</td> +<td align="left">sd(f_JCZ38_qlogis), sd(f_JSE76_qlogis)</td> +</tr> +</tbody> +</table> +<p>In both fits, the random effects for the formation fractions for the +pathways from JCZ38 to JSE76, and for the reverse pathway from JSE76 to +JCZ38 are ill-defined.</p> +<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="right">npar</th> +<th align="right">AIC</th> +<th align="right">BIC</th> +<th align="right">Lik</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">fomc_path_2 const</td> +<td align="right">20</td> +<td align="right">2308.3</td> +<td align="right">2300.5</td> +<td align="right">-1134.2</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_2 tc</td> +<td align="right">21</td> +<td align="right">2248.3</td> +<td align="right">2240.1</td> +<td align="right">-1103.2</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_2 const</td> +<td align="right">22</td> +<td align="right">2289.6</td> +<td align="right">2281.0</td> +<td align="right">-1122.8</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_2 const</td> +<td align="right">22</td> +<td align="right">2284.1</td> +<td align="right">2275.5</td> +<td align="right">-1120.0</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_2 tc</td> +<td align="right">22</td> +<td align="right">2234.4</td> +<td align="right">2225.8</td> +<td align="right">-1095.2</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_2 tc</td> +<td align="right">22</td> +<td align="right">2240.4</td> +<td align="right">2231.8</td> +<td align="right">-1098.2</td> +</tr> +</tbody> +</table> +<p>The variants using the biexponential models DFOP and SFORB for the +parent compound and the two-component error model give the lowest AIC +and BIC values and are plotted below. Compared with the original +pathway, the AIC and BIC values indicate a large improvement. This is +confirmed by the plots, which show that the metabolite JCZ38 is fitted +much better with this model.</p> +<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"fomc_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-11-1.png" alt="FOMC pathway fit with two-component error, alternative pathway" width="700"><p class="caption"> +FOMC pathway fit with two-component error, alternative pathway +</p> +</div> +<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"dfop_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-12-1.png" alt="DFOP pathway fit with two-component error, alternative pathway" width="700"><p class="caption"> +DFOP pathway fit with two-component error, alternative pathway +</p> +</div> +<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"sforb_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-13-1.png" alt="SFORB pathway fit with two-component error, alternative pathway" width="700"><p class="caption"> +SFORB pathway fit with two-component error, alternative pathway +</p> +</div> +</div> +<div class="section level3"> +<h3 id="refinement-of-alternative-pathway-fits">Refinement of alternative pathway fits<a class="anchor" aria-label="anchor" href="#refinement-of-alternative-pathway-fits"></a> +</h3> +<p>All ill-defined random effects that were identified in the parent +only fits and in the above pathway fits, are excluded for the final +evaluations below. For this purpose, a list of character vectors is +created below that can be indexed by row and column indices, and which +contains the degradation parameter names for which random effects should +be excluded for each of the hierarchical fits contained in +<code>f_saem_2</code>.</p> +<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">no_ranef</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>, nrow <span class="op">=</span> <span class="fl">3</span>, ncol <span class="op">=</span> <span class="fl">2</span>, dimnames <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/dimnames.html" class="external-link">dimnames</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span><span class="op">)</span></span> +<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"fomc_path_2"</span>, <span class="st">"const"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"log_beta"</span>, <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span> +<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"fomc_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"cyan_0"</span>, <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span> +<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"dfop_path_2"</span>, <span class="st">"const"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"cyan_0"</span>, <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span> +<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"dfop_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"cyan_0"</span>, <span class="st">"log_k1"</span>, <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span> +<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"sforb_path_2"</span>, <span class="st">"const"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"cyan_free_0"</span>,</span> +<span> <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span> +<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"sforb_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"cyan_free_0"</span>, <span class="st">"log_k_cyan_free_bound"</span>,</span> +<span> <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/parallel/clusterApply.html" class="external-link">clusterExport</a></span><span class="op">(</span><span class="va">cl</span>, <span class="st">"no_ranef"</span><span class="op">)</span></span> +<span></span> +<span><span class="va">f_saem_3</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_2</span>,</span> +<span> no_random_effect <span class="op">=</span> <span class="va">no_ranef</span>,</span> +<span> cluster <span class="op">=</span> <span class="va">cl</span><span class="op">)</span></span></code></pre></div> +<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_saem_3</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">fomc_path_2</td> +<td align="left">E</td> +<td align="left">Fth</td> +</tr> +<tr class="even"> +<td align="left">dfop_path_2</td> +<td align="left">Fth</td> +<td align="left">Fth</td> +</tr> +<tr class="odd"> +<td align="left">sforb_path_2</td> +<td align="left">Fth</td> +<td align="left">Fth</td> +</tr> +</tbody> +</table> +<p>With the exception of the FOMC pathway fit with constant variance, +all updated fits completed successfully. However, the Fisher Information +Matrix for the fixed effects (Fth) could not be inverted, so no +confidence intervals for the optimised parameters are available.</p> +<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_3</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">fomc_path_2</td> +<td align="left">E</td> +<td align="left"></td> +</tr> +<tr class="even"> +<td align="left">dfop_path_2</td> +<td align="left"></td> +<td align="left"></td> +</tr> +<tr class="odd"> +<td align="left">sforb_path_2</td> +<td align="left"></td> +<td align="left"></td> +</tr> +</tbody> +</table> +<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_3</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="right">npar</th> +<th align="right">AIC</th> +<th align="right">BIC</th> +<th align="right">Lik</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">fomc_path_2 tc</td> +<td align="right">19</td> +<td align="right">2250.9</td> +<td align="right">2243.5</td> +<td align="right">-1106.5</td> +</tr> +<tr class="even"> +<td align="left">dfop_path_2 const</td> +<td align="right">20</td> +<td align="right">2281.7</td> +<td align="right">2273.9</td> +<td align="right">-1120.8</td> +</tr> +<tr class="odd"> +<td align="left">sforb_path_2 const</td> +<td align="right">20</td> +<td align="right">2279.5</td> +<td align="right">2271.7</td> +<td align="right">-1119.7</td> +</tr> +<tr class="even"> +<td align="left">dfop_path_2 tc</td> +<td align="right">20</td> +<td align="right">2231.5</td> +<td align="right">2223.7</td> +<td align="right">-1095.8</td> +</tr> +<tr class="odd"> +<td align="left">sforb_path_2 tc</td> +<td align="right">20</td> +<td align="right">2235.7</td> +<td align="right">2227.9</td> +<td align="right">-1097.9</td> +</tr> +</tbody> +</table> +<p>While the AIC and BIC values of the best fit (DFOP pathway fit with +two-component error) are lower than in the previous fits with the +alternative pathway, the practical value of these refined evaluations is +limited as no confidence intervals are obtained.</p> +</div> +</div> +<div class="section level2"> +<h2 id="conclusion">Conclusion<a class="anchor" aria-label="anchor" href="#conclusion"></a> +</h2> +<p>It was demonstrated that a relatively complex transformation pathway +with parallel formation of two primary metabolites and one secondary +metabolite can be fitted even if the data in the individual datasets are +quite different and partly only cover the formation phase.</p> +<p>The run times of the pathway fits were several hours, limiting the +practical feasibility of iterative refinements based on ill-defined +parameters and of alternative checks of parameter identifiability based +on multistart runs.</p> +</div> +<div class="section level2"> +<h2 id="acknowledgements">Acknowledgements<a class="anchor" aria-label="anchor" href="#acknowledgements"></a> +</h2> +<p>The helpful comments by Janina Wöltjen of the German Environment +Agency are gratefully acknowledged.</p> +</div> +<div class="section level2"> +<h2 id="appendix">Appendix<a class="anchor" aria-label="anchor" href="#appendix"></a> +</h2> +<div class="section level3"> +<h3 id="plots-of-fits-that-were-not-refined-further">Plots of fits that were not refined further<a class="anchor" aria-label="anchor" href="#plots-of-fits-that-were-not-refined-further"></a> +</h3> +<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">[[</span><span class="st">"sfo_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-17-1.png" alt="SFO pathway fit with two-component error" width="700"><p class="caption"> +SFO pathway fit with two-component error +</p> +</div> +<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">[[</span><span class="st">"fomc_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-18-1.png" alt="FOMC pathway fit with two-component error" width="700"><p class="caption"> +FOMC pathway fit with two-component error +</p> +</div> +<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-19-1.png" alt="HS pathway fit with two-component error" width="700"><p class="caption"> +HS pathway fit with two-component error +</p> +</div> +</div> +<div class="section level3"> +<h3 id="hierarchical-fit-listings">Hierarchical fit listings<a class="anchor" aria-label="anchor" href="#hierarchical-fit-listings"></a> +</h3> +<div class="section level4"> +<h4 id="pathway-1">Pathway 1<a class="anchor" aria-label="anchor" href="#pathway-1"></a> +</h4> +<caption> +Hierarchical SFO path 1 fit with constant variance +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 10:07:38 2023 +Date of summary: Sat Jan 28 11:22:29 2023 + +Equations: +d_cyan/dt = - k_cyan * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * k_cyan * cyan - k_JCZ38 * JCZ38 +d_J9Z38/dt = + f_cyan_to_J9Z38 * k_cyan * cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 1088.473 s +Using 300, 100 iterations and 10 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + cyan_0 log_k_cyan log_k_JCZ38 log_k_J9Z38 log_k_JSE76 + 95.3304 -3.8459 -3.1305 -5.0678 -5.3196 + f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis + 0.8158 22.5404 10.4289 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_cyan log_k_JCZ38 log_k_J9Z38 log_k_JSE76 +cyan_0 4.797 0.0000 0.000 0.000 0.0000 +log_k_cyan 0.000 0.9619 0.000 0.000 0.0000 +log_k_JCZ38 0.000 0.0000 2.139 0.000 0.0000 +log_k_J9Z38 0.000 0.0000 0.000 1.639 0.0000 +log_k_JSE76 0.000 0.0000 0.000 0.000 0.7894 +f_cyan_ilr_1 0.000 0.0000 0.000 0.000 0.0000 +f_cyan_ilr_2 0.000 0.0000 0.000 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.0000 0.000 0.000 0.0000 + f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis +cyan_0 0.0000 0.000 0.00 +log_k_cyan 0.0000 0.000 0.00 +log_k_JCZ38 0.0000 0.000 0.00 +log_k_J9Z38 0.0000 0.000 0.00 +log_k_JSE76 0.0000 0.000 0.00 +f_cyan_ilr_1 0.7714 0.000 0.00 +f_cyan_ilr_2 0.0000 8.684 0.00 +f_JCZ38_qlogis 0.0000 0.000 13.48 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2693 2687 -1330 + +Optimised parameters: + est. lower upper +cyan_0 95.0946 NA NA +log_k_cyan -3.8544 NA NA +log_k_JCZ38 -3.0402 NA NA +log_k_J9Z38 -5.0109 NA NA +log_k_JSE76 -5.2857 NA NA +f_cyan_ilr_1 0.8069 NA NA +f_cyan_ilr_2 16.6623 NA NA +f_JCZ38_qlogis 1.3602 NA NA +a.1 4.8326 NA NA +SD.log_k_cyan 0.5842 NA NA +SD.log_k_JCZ38 1.2680 NA NA +SD.log_k_J9Z38 0.3626 NA NA +SD.log_k_JSE76 0.5244 NA NA +SD.f_cyan_ilr_1 0.2752 NA NA +SD.f_cyan_ilr_2 2.3556 NA NA +SD.f_JCZ38_qlogis 0.2400 NA NA + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_cyan 0.5842 NA NA +SD.log_k_JCZ38 1.2680 NA NA +SD.log_k_J9Z38 0.3626 NA NA +SD.log_k_JSE76 0.5244 NA NA +SD.f_cyan_ilr_1 0.2752 NA NA +SD.f_cyan_ilr_2 2.3556 NA NA +SD.f_JCZ38_qlogis 0.2400 NA NA + +Variance model: + est. lower upper +a.1 4.833 NA NA + +Backtransformed parameters: + est. lower upper +cyan_0 95.094581 NA NA +k_cyan 0.021186 NA NA +k_JCZ38 0.047825 NA NA +k_J9Z38 0.006665 NA NA +k_JSE76 0.005063 NA NA +f_cyan_to_JCZ38 0.757885 NA NA +f_cyan_to_J9Z38 0.242115 NA NA +f_JCZ38_to_JSE76 0.795792 NA NA + +Resulting formation fractions: + ff +cyan_JCZ38 7.579e-01 +cyan_J9Z38 2.421e-01 +cyan_sink 5.877e-10 +JCZ38_JSE76 7.958e-01 +JCZ38_sink 2.042e-01 + +Estimated disappearance times: + DT50 DT90 +cyan 32.72 108.68 +JCZ38 14.49 48.15 +J9Z38 103.99 345.46 +JSE76 136.90 454.76 + +</code></pre> +<p></p> +<caption> +Hierarchical SFO path 1 fit with two-component error +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 10:08:17 2023 +Date of summary: Sat Jan 28 11:22:29 2023 + +Equations: +d_cyan/dt = - k_cyan * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * k_cyan * cyan - k_JCZ38 * JCZ38 +d_J9Z38/dt = + f_cyan_to_J9Z38 * k_cyan * cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 1127.552 s +Using 300, 100 iterations and 10 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + cyan_0 log_k_cyan log_k_JCZ38 log_k_J9Z38 log_k_JSE76 + 96.0039 -3.8907 -3.1276 -5.0069 -4.9367 + f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis + 0.7937 20.0030 15.1336 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_cyan log_k_JCZ38 log_k_J9Z38 log_k_JSE76 +cyan_0 4.859 0.000 0.00 0.00 0.0000 +log_k_cyan 0.000 0.962 0.00 0.00 0.0000 +log_k_JCZ38 0.000 0.000 2.04 0.00 0.0000 +log_k_J9Z38 0.000 0.000 0.00 1.72 0.0000 +log_k_JSE76 0.000 0.000 0.00 0.00 0.9076 +f_cyan_ilr_1 0.000 0.000 0.00 0.00 0.0000 +f_cyan_ilr_2 0.000 0.000 0.00 0.00 0.0000 +f_JCZ38_qlogis 0.000 0.000 0.00 0.00 0.0000 + f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis +cyan_0 0.0000 0.000 0.00 +log_k_cyan 0.0000 0.000 0.00 +log_k_JCZ38 0.0000 0.000 0.00 +log_k_J9Z38 0.0000 0.000 0.00 +log_k_JSE76 0.0000 0.000 0.00 +f_cyan_ilr_1 0.7598 0.000 0.00 +f_cyan_ilr_2 0.0000 7.334 0.00 +f_JCZ38_qlogis 0.0000 0.000 11.78 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2658 2651 -1312 + +Optimised parameters: + est. lower upper +cyan_0 94.72923 NA NA +log_k_cyan -3.91670 NA NA +log_k_JCZ38 -3.12917 NA NA +log_k_J9Z38 -5.06070 NA NA +log_k_JSE76 -5.09254 NA NA +f_cyan_ilr_1 0.81116 NA NA +f_cyan_ilr_2 39.97850 NA NA +f_JCZ38_qlogis 3.09728 NA NA +a.1 3.95044 NA NA +b.1 0.07998 NA NA +SD.log_k_cyan 0.58855 NA NA +SD.log_k_JCZ38 1.29753 NA NA +SD.log_k_J9Z38 0.62851 NA NA +SD.log_k_JSE76 0.37235 NA NA +SD.f_cyan_ilr_1 0.37346 NA NA +SD.f_cyan_ilr_2 1.41667 NA NA +SD.f_JCZ38_qlogis 1.81467 NA NA + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_cyan 0.5886 NA NA +SD.log_k_JCZ38 1.2975 NA NA +SD.log_k_J9Z38 0.6285 NA NA +SD.log_k_JSE76 0.3724 NA NA +SD.f_cyan_ilr_1 0.3735 NA NA +SD.f_cyan_ilr_2 1.4167 NA NA +SD.f_JCZ38_qlogis 1.8147 NA NA + +Variance model: + est. lower upper +a.1 3.95044 NA NA +b.1 0.07998 NA NA + +Backtransformed parameters: + est. lower upper +cyan_0 94.729229 NA NA +k_cyan 0.019907 NA NA +k_JCZ38 0.043754 NA NA +k_J9Z38 0.006341 NA NA +k_JSE76 0.006142 NA NA +f_cyan_to_JCZ38 0.758991 NA NA +f_cyan_to_J9Z38 0.241009 NA NA +f_JCZ38_to_JSE76 0.956781 NA NA + +Resulting formation fractions: + ff +cyan_JCZ38 0.75899 +cyan_J9Z38 0.24101 +cyan_sink 0.00000 +JCZ38_JSE76 0.95678 +JCZ38_sink 0.04322 + +Estimated disappearance times: + DT50 DT90 +cyan 34.82 115.67 +JCZ38 15.84 52.63 +J9Z38 109.31 363.12 +JSE76 112.85 374.87 + +</code></pre> +<p></p> +<caption> +Hierarchical FOMC path 1 fit with constant variance +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 10:09:12 2023 +Date of summary: Sat Jan 28 11:22:29 2023 + +Equations: +d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) * + cyan - k_JCZ38 * JCZ38 +d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) * + cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 1182.258 s +Using 300, 100 iterations and 10 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 101.2314 -3.3680 -5.1108 -5.9416 0.7144 + f_cyan_ilr_2 f_JCZ38_qlogis log_alpha log_beta + 7.3870 15.7604 -0.1791 2.9811 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 5.416 0.000 0.0 0.000 0.0000 +log_k_JCZ38 0.000 2.439 0.0 0.000 0.0000 +log_k_J9Z38 0.000 0.000 1.7 0.000 0.0000 +log_k_JSE76 0.000 0.000 0.0 1.856 0.0000 +f_cyan_ilr_1 0.000 0.000 0.0 0.000 0.7164 +f_cyan_ilr_2 0.000 0.000 0.0 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.000 0.0 0.000 0.0000 +log_alpha 0.000 0.000 0.0 0.000 0.0000 +log_beta 0.000 0.000 0.0 0.000 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis log_alpha log_beta +cyan_0 0.00 0.00 0.0000 0.0000 +log_k_JCZ38 0.00 0.00 0.0000 0.0000 +log_k_J9Z38 0.00 0.00 0.0000 0.0000 +log_k_JSE76 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_1 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_2 12.33 0.00 0.0000 0.0000 +f_JCZ38_qlogis 0.00 20.42 0.0000 0.0000 +log_alpha 0.00 0.00 0.4144 0.0000 +log_beta 0.00 0.00 0.0000 0.5077 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2428 2421 -1196 + +Optimised parameters: + est. lower upper +cyan_0 101.0225 98.306270 103.7387 +log_k_JCZ38 -3.3786 -4.770657 -1.9866 +log_k_J9Z38 -5.2603 -5.902085 -4.6186 +log_k_JSE76 -6.1427 -7.318336 -4.9671 +f_cyan_ilr_1 0.7437 0.421215 1.0663 +f_cyan_ilr_2 0.9108 0.267977 1.5537 +f_JCZ38_qlogis 2.0487 0.524897 3.5724 +log_alpha -0.2268 -0.618049 0.1644 +log_beta 2.8986 2.700701 3.0964 +a.1 3.4058 3.169913 3.6416 +SD.cyan_0 2.5279 0.454190 4.6016 +SD.log_k_JCZ38 1.5636 0.572824 2.5543 +SD.log_k_J9Z38 0.5316 -0.004405 1.0677 +SD.log_k_JSE76 0.9903 0.106325 1.8742 +SD.f_cyan_ilr_1 0.3464 0.112066 0.5807 +SD.f_cyan_ilr_2 0.2804 -0.393900 0.9546 +SD.f_JCZ38_qlogis 0.9416 -0.152986 2.0362 +SD.log_alpha 0.4273 0.161044 0.6936 + +Correlation: + cyan_0 l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 log_lph +log_k_JCZ38 -0.0156 +log_k_J9Z38 -0.0493 0.0073 +log_k_JSE76 -0.0329 0.0018 0.0069 +f_cyan_ilr_1 -0.0086 0.0180 -0.1406 0.0012 +f_cyan_ilr_2 -0.2629 0.0779 0.2826 0.0274 0.0099 +f_JCZ38_qlogis 0.0713 -0.0747 -0.0505 0.1169 -0.1022 -0.4893 +log_alpha -0.0556 0.0120 0.0336 0.0193 0.0036 0.0840 -0.0489 +log_beta -0.2898 0.0460 0.1305 0.0768 0.0190 0.4071 -0.1981 0.2772 + +Random effects: + est. lower upper +SD.cyan_0 2.5279 0.454190 4.6016 +SD.log_k_JCZ38 1.5636 0.572824 2.5543 +SD.log_k_J9Z38 0.5316 -0.004405 1.0677 +SD.log_k_JSE76 0.9903 0.106325 1.8742 +SD.f_cyan_ilr_1 0.3464 0.112066 0.5807 +SD.f_cyan_ilr_2 0.2804 -0.393900 0.9546 +SD.f_JCZ38_qlogis 0.9416 -0.152986 2.0362 +SD.log_alpha 0.4273 0.161044 0.6936 + +Variance model: + est. lower upper +a.1 3.406 3.17 3.642 + +Backtransformed parameters: + est. lower upper +cyan_0 1.010e+02 9.831e+01 1.037e+02 +k_JCZ38 3.409e-02 8.475e-03 1.372e-01 +k_J9Z38 5.194e-03 2.734e-03 9.867e-03 +k_JSE76 2.149e-03 6.633e-04 6.963e-03 +f_cyan_to_JCZ38 6.481e-01 NA NA +f_cyan_to_J9Z38 2.264e-01 NA NA +f_JCZ38_to_JSE76 8.858e-01 6.283e-01 9.727e-01 +alpha 7.971e-01 5.390e-01 1.179e+00 +beta 1.815e+01 1.489e+01 2.212e+01 + +Resulting formation fractions: + ff +cyan_JCZ38 0.6481 +cyan_J9Z38 0.2264 +cyan_sink 0.1255 +JCZ38_JSE76 0.8858 +JCZ38_sink 0.1142 + +Estimated disappearance times: + DT50 DT90 DT50back +cyan 25.15 308.01 92.72 +JCZ38 20.33 67.54 NA +J9Z38 133.46 443.35 NA +JSE76 322.53 1071.42 NA + +</code></pre> +<p></p> +<caption> +Hierarchical FOMC path 1 fit with two-component error +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 10:09:18 2023 +Date of summary: Sat Jan 28 11:22:29 2023 + +Equations: +d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) * + cyan - k_JCZ38 * JCZ38 +d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) * + cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 1188.041 s +Using 300, 100 iterations and 10 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 101.13827 -3.32493 -5.08921 -5.93478 0.71330 + f_cyan_ilr_2 f_JCZ38_qlogis log_alpha log_beta + 10.05989 12.79248 -0.09621 3.10646 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 5.643 0.000 0.000 0.00 0.0000 +log_k_JCZ38 0.000 2.319 0.000 0.00 0.0000 +log_k_J9Z38 0.000 0.000 1.731 0.00 0.0000 +log_k_JSE76 0.000 0.000 0.000 1.86 0.0000 +f_cyan_ilr_1 0.000 0.000 0.000 0.00 0.7186 +f_cyan_ilr_2 0.000 0.000 0.000 0.00 0.0000 +f_JCZ38_qlogis 0.000 0.000 0.000 0.00 0.0000 +log_alpha 0.000 0.000 0.000 0.00 0.0000 +log_beta 0.000 0.000 0.000 0.00 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis log_alpha log_beta +cyan_0 0.00 0.00 0.0000 0.0000 +log_k_JCZ38 0.00 0.00 0.0000 0.0000 +log_k_J9Z38 0.00 0.00 0.0000 0.0000 +log_k_JSE76 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_1 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_2 12.49 0.00 0.0000 0.0000 +f_JCZ38_qlogis 0.00 20.19 0.0000 0.0000 +log_alpha 0.00 0.00 0.3142 0.0000 +log_beta 0.00 0.00 0.0000 0.7331 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2423 2416 -1193 + +Optimised parameters: + est. lower upper +cyan_0 100.57649 NA NA +log_k_JCZ38 -3.46250 NA NA +log_k_J9Z38 -5.24442 NA NA +log_k_JSE76 -5.75229 NA NA +f_cyan_ilr_1 0.68480 NA NA +f_cyan_ilr_2 0.61670 NA NA +f_JCZ38_qlogis 87.97407 NA NA +log_alpha -0.15699 NA NA +log_beta 3.01540 NA NA +a.1 3.11518 NA NA +b.1 0.04445 NA NA +SD.log_k_JCZ38 1.40732 NA NA +SD.log_k_J9Z38 0.56510 NA NA +SD.log_k_JSE76 0.72067 NA NA +SD.f_cyan_ilr_1 0.31199 NA NA +SD.f_cyan_ilr_2 0.36894 NA NA +SD.f_JCZ38_qlogis 6.92892 NA NA +SD.log_alpha 0.25662 NA NA +SD.log_beta 0.35845 NA NA + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_JCZ38 1.4073 NA NA +SD.log_k_J9Z38 0.5651 NA NA +SD.log_k_JSE76 0.7207 NA NA +SD.f_cyan_ilr_1 0.3120 NA NA +SD.f_cyan_ilr_2 0.3689 NA NA +SD.f_JCZ38_qlogis 6.9289 NA NA +SD.log_alpha 0.2566 NA NA +SD.log_beta 0.3585 NA NA + +Variance model: + est. lower upper +a.1 3.11518 NA NA +b.1 0.04445 NA NA + +Backtransformed parameters: + est. lower upper +cyan_0 1.006e+02 NA NA +k_JCZ38 3.135e-02 NA NA +k_J9Z38 5.277e-03 NA NA +k_JSE76 3.175e-03 NA NA +f_cyan_to_JCZ38 5.991e-01 NA NA +f_cyan_to_J9Z38 2.275e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA +alpha 8.547e-01 NA NA +beta 2.040e+01 NA NA + +Resulting formation fractions: + ff +cyan_JCZ38 0.5991 +cyan_J9Z38 0.2275 +cyan_sink 0.1734 +JCZ38_JSE76 1.0000 +JCZ38_sink 0.0000 + +Estimated disappearance times: + DT50 DT90 DT50back +cyan 25.50 281.29 84.68 +JCZ38 22.11 73.44 NA +J9Z38 131.36 436.35 NA +JSE76 218.28 725.11 NA + +</code></pre> +<p></p> +<caption> +Hierarchical DFOP path 1 fit with constant variance +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 10:10:30 2023 +Date of summary: Sat Jan 28 11:22:29 2023 + +Equations: +d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 +d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 1260.905 s +Using 300, 100 iterations and 10 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 102.0644 -3.4008 -5.0024 -5.8613 0.6855 + f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 g_qlogis + 1.2365 13.7245 -1.8641 -4.5063 -0.6468 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 4.466 0.000 0.000 0.000 0.0000 +log_k_JCZ38 0.000 2.382 0.000 0.000 0.0000 +log_k_J9Z38 0.000 0.000 1.595 0.000 0.0000 +log_k_JSE76 0.000 0.000 0.000 1.245 0.0000 +f_cyan_ilr_1 0.000 0.000 0.000 0.000 0.6852 +f_cyan_ilr_2 0.000 0.000 0.000 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.000 0.000 0.000 0.0000 +log_k1 0.000 0.000 0.000 0.000 0.0000 +log_k2 0.000 0.000 0.000 0.000 0.0000 +g_qlogis 0.000 0.000 0.000 0.000 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 g_qlogis +cyan_0 0.00 0.00 0.0000 0.0000 0.000 +log_k_JCZ38 0.00 0.00 0.0000 0.0000 0.000 +log_k_J9Z38 0.00 0.00 0.0000 0.0000 0.000 +log_k_JSE76 0.00 0.00 0.0000 0.0000 0.000 +f_cyan_ilr_1 0.00 0.00 0.0000 0.0000 0.000 +f_cyan_ilr_2 1.28 0.00 0.0000 0.0000 0.000 +f_JCZ38_qlogis 0.00 16.11 0.0000 0.0000 0.000 +log_k1 0.00 0.00 0.9866 0.0000 0.000 +log_k2 0.00 0.00 0.0000 0.5953 0.000 +g_qlogis 0.00 0.00 0.0000 0.0000 1.583 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2403 2395 -1182 + +Optimised parameters: + est. lower upper +cyan_0 102.6079 NA NA +log_k_JCZ38 -3.4855 NA NA +log_k_J9Z38 -5.1686 NA NA +log_k_JSE76 -5.6697 NA NA +f_cyan_ilr_1 0.6714 NA NA +f_cyan_ilr_2 0.4986 NA NA +f_JCZ38_qlogis 55.4760 NA NA +log_k1 -1.8409 NA NA +log_k2 -4.4915 NA NA +g_qlogis -0.6403 NA NA +a.1 3.2387 NA NA +SD.log_k_JCZ38 1.4524 NA NA +SD.log_k_J9Z38 0.5151 NA NA +SD.log_k_JSE76 0.6514 NA NA +SD.f_cyan_ilr_1 0.3023 NA NA +SD.f_cyan_ilr_2 0.2959 NA NA +SD.f_JCZ38_qlogis 1.9984 NA NA +SD.log_k1 0.5188 NA NA +SD.log_k2 0.3894 NA NA +SD.g_qlogis 0.8579 NA NA + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_JCZ38 1.4524 NA NA +SD.log_k_J9Z38 0.5151 NA NA +SD.log_k_JSE76 0.6514 NA NA +SD.f_cyan_ilr_1 0.3023 NA NA +SD.f_cyan_ilr_2 0.2959 NA NA +SD.f_JCZ38_qlogis 1.9984 NA NA +SD.log_k1 0.5188 NA NA +SD.log_k2 0.3894 NA NA +SD.g_qlogis 0.8579 NA NA + +Variance model: + est. lower upper +a.1 3.239 NA NA + +Backtransformed parameters: + est. lower upper +cyan_0 1.026e+02 NA NA +k_JCZ38 3.064e-02 NA NA +k_J9Z38 5.692e-03 NA NA +k_JSE76 3.449e-03 NA NA +f_cyan_to_JCZ38 5.798e-01 NA NA +f_cyan_to_J9Z38 2.243e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA +k1 1.587e-01 NA NA +k2 1.120e-02 NA NA +g 3.452e-01 NA NA + +Resulting formation fractions: + ff +cyan_JCZ38 0.5798 +cyan_J9Z38 0.2243 +cyan_sink 0.1958 +JCZ38_JSE76 1.0000 +JCZ38_sink 0.0000 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +cyan 25.21 167.73 50.49 4.368 61.87 +JCZ38 22.62 75.15 NA NA NA +J9Z38 121.77 404.50 NA NA NA +JSE76 200.98 667.64 NA NA NA + +</code></pre> +<p></p> +<caption> +Hierarchical DFOP path 1 fit with two-component error +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 10:16:28 2023 +Date of summary: Sat Jan 28 11:22:29 2023 + +Equations: +d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 +d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 1617.774 s +Using 300, 100 iterations and 10 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 101.3964 -3.3626 -4.9792 -5.8727 0.6814 + f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 g_qlogis + 6.7799 13.7245 -1.9222 -4.5035 -0.7172 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 5.317 0.000 0.000 0.000 0.0000 +log_k_JCZ38 0.000 2.272 0.000 0.000 0.0000 +log_k_J9Z38 0.000 0.000 1.633 0.000 0.0000 +log_k_JSE76 0.000 0.000 0.000 1.271 0.0000 +f_cyan_ilr_1 0.000 0.000 0.000 0.000 0.6838 +f_cyan_ilr_2 0.000 0.000 0.000 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.000 0.000 0.000 0.0000 +log_k1 0.000 0.000 0.000 0.000 0.0000 +log_k2 0.000 0.000 0.000 0.000 0.0000 +g_qlogis 0.000 0.000 0.000 0.000 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 g_qlogis +cyan_0 0.00 0.00 0.0000 0.0000 0.000 +log_k_JCZ38 0.00 0.00 0.0000 0.0000 0.000 +log_k_J9Z38 0.00 0.00 0.0000 0.0000 0.000 +log_k_JSE76 0.00 0.00 0.0000 0.0000 0.000 +f_cyan_ilr_1 0.00 0.00 0.0000 0.0000 0.000 +f_cyan_ilr_2 11.77 0.00 0.0000 0.0000 0.000 +f_JCZ38_qlogis 0.00 16.11 0.0000 0.0000 0.000 +log_k1 0.00 0.00 0.9496 0.0000 0.000 +log_k2 0.00 0.00 0.0000 0.5846 0.000 +g_qlogis 0.00 0.00 0.0000 0.0000 1.719 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2398 2390 -1179 + +Optimised parameters: + est. lower upper +cyan_0 100.8076 NA NA +log_k_JCZ38 -3.4684 NA NA +log_k_J9Z38 -5.0844 NA NA +log_k_JSE76 -5.5743 NA NA +f_cyan_ilr_1 0.6669 NA NA +f_cyan_ilr_2 0.7912 NA NA +f_JCZ38_qlogis 84.1825 NA NA +log_k1 -2.1671 NA NA +log_k2 -4.5447 NA NA +g_qlogis -0.5631 NA NA +a.1 2.9627 NA NA +b.1 0.0444 NA NA +SD.log_k_JCZ38 1.4044 NA NA +SD.log_k_J9Z38 0.6410 NA NA +SD.log_k_JSE76 0.5391 NA NA +SD.f_cyan_ilr_1 0.3203 NA NA +SD.f_cyan_ilr_2 0.5038 NA NA +SD.f_JCZ38_qlogis 3.5865 NA NA +SD.log_k2 0.3119 NA NA +SD.g_qlogis 0.8276 NA NA + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_JCZ38 1.4044 NA NA +SD.log_k_J9Z38 0.6410 NA NA +SD.log_k_JSE76 0.5391 NA NA +SD.f_cyan_ilr_1 0.3203 NA NA +SD.f_cyan_ilr_2 0.5038 NA NA +SD.f_JCZ38_qlogis 3.5865 NA NA +SD.log_k2 0.3119 NA NA +SD.g_qlogis 0.8276 NA NA + +Variance model: + est. lower upper +a.1 2.9627 NA NA +b.1 0.0444 NA NA + +Backtransformed parameters: + est. lower upper +cyan_0 1.008e+02 NA NA +k_JCZ38 3.117e-02 NA NA +k_J9Z38 6.193e-03 NA NA +k_JSE76 3.794e-03 NA NA +f_cyan_to_JCZ38 6.149e-01 NA NA +f_cyan_to_J9Z38 2.395e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA +k1 1.145e-01 NA NA +k2 1.062e-02 NA NA +g 3.628e-01 NA NA + +Resulting formation fractions: + ff +cyan_JCZ38 0.6149 +cyan_J9Z38 0.2395 +cyan_sink 0.1456 +JCZ38_JSE76 1.0000 +JCZ38_sink 0.0000 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +cyan 26.26 174.32 52.47 6.053 65.25 +JCZ38 22.24 73.88 NA NA NA +J9Z38 111.93 371.82 NA NA NA +JSE76 182.69 606.88 NA NA NA + +</code></pre> +<p></p> +<caption> +Hierarchical SFORB path 1 fit with constant variance +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 10:10:49 2023 +Date of summary: Sat Jan 28 11:22:29 2023 + +Equations: +d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound * + cyan_free + k_cyan_bound_free * cyan_bound +d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free * + cyan_bound +d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38 + * JCZ38 +d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38 + * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 1279.472 s +Using 300, 100 iterations and 10 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound + 102.0643 -2.8987 -2.7077 +log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 + -3.4717 -3.4008 -5.0024 + log_k_JSE76 f_cyan_ilr_1 f_cyan_ilr_2 + -5.8613 0.6855 1.2366 + f_JCZ38_qlogis + 13.7418 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound +cyan_free_0 4.466 0.0000 0.000 +log_k_cyan_free 0.000 0.6158 0.000 +log_k_cyan_free_bound 0.000 0.0000 1.463 +log_k_cyan_bound_free 0.000 0.0000 0.000 +log_k_JCZ38 0.000 0.0000 0.000 +log_k_J9Z38 0.000 0.0000 0.000 +log_k_JSE76 0.000 0.0000 0.000 +f_cyan_ilr_1 0.000 0.0000 0.000 +f_cyan_ilr_2 0.000 0.0000 0.000 +f_JCZ38_qlogis 0.000 0.0000 0.000 + log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 +cyan_free_0 0.000 0.000 0.000 0.000 +log_k_cyan_free 0.000 0.000 0.000 0.000 +log_k_cyan_free_bound 0.000 0.000 0.000 0.000 +log_k_cyan_bound_free 1.058 0.000 0.000 0.000 +log_k_JCZ38 0.000 2.382 0.000 0.000 +log_k_J9Z38 0.000 0.000 1.595 0.000 +log_k_JSE76 0.000 0.000 0.000 1.245 +f_cyan_ilr_1 0.000 0.000 0.000 0.000 +f_cyan_ilr_2 0.000 0.000 0.000 0.000 +f_JCZ38_qlogis 0.000 0.000 0.000 0.000 + f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis +cyan_free_0 0.0000 0.00 0.00 +log_k_cyan_free 0.0000 0.00 0.00 +log_k_cyan_free_bound 0.0000 0.00 0.00 +log_k_cyan_bound_free 0.0000 0.00 0.00 +log_k_JCZ38 0.0000 0.00 0.00 +log_k_J9Z38 0.0000 0.00 0.00 +log_k_JSE76 0.0000 0.00 0.00 +f_cyan_ilr_1 0.6852 0.00 0.00 +f_cyan_ilr_2 0.0000 1.28 0.00 +f_JCZ38_qlogis 0.0000 0.00 16.14 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2401 2394 -1181 + +Optimised parameters: + est. lower upper +cyan_free_0 102.7803 NA NA +log_k_cyan_free -2.8068 NA NA +log_k_cyan_free_bound -2.5714 NA NA +log_k_cyan_bound_free -3.4426 NA NA +log_k_JCZ38 -3.4994 NA NA +log_k_J9Z38 -5.1148 NA NA +log_k_JSE76 -5.6335 NA NA +f_cyan_ilr_1 0.6597 NA NA +f_cyan_ilr_2 0.5132 NA NA +f_JCZ38_qlogis 37.2090 NA NA +a.1 3.2367 NA NA +SD.log_k_cyan_free 0.3161 NA NA +SD.log_k_cyan_free_bound 0.8103 NA NA +SD.log_k_cyan_bound_free 0.5554 NA NA +SD.log_k_JCZ38 1.4858 NA NA +SD.log_k_J9Z38 0.5859 NA NA +SD.log_k_JSE76 0.6195 NA NA +SD.f_cyan_ilr_1 0.3118 NA NA +SD.f_cyan_ilr_2 0.3344 NA NA +SD.f_JCZ38_qlogis 0.5518 NA NA + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_cyan_free 0.3161 NA NA +SD.log_k_cyan_free_bound 0.8103 NA NA +SD.log_k_cyan_bound_free 0.5554 NA NA +SD.log_k_JCZ38 1.4858 NA NA +SD.log_k_J9Z38 0.5859 NA NA +SD.log_k_JSE76 0.6195 NA NA +SD.f_cyan_ilr_1 0.3118 NA NA +SD.f_cyan_ilr_2 0.3344 NA NA +SD.f_JCZ38_qlogis 0.5518 NA NA + +Variance model: + est. lower upper +a.1 3.237 NA NA + +Backtransformed parameters: + est. lower upper +cyan_free_0 1.028e+02 NA NA +k_cyan_free 6.040e-02 NA NA +k_cyan_free_bound 7.643e-02 NA NA +k_cyan_bound_free 3.198e-02 NA NA +k_JCZ38 3.022e-02 NA NA +k_J9Z38 6.007e-03 NA NA +k_JSE76 3.576e-03 NA NA +f_cyan_free_to_JCZ38 5.787e-01 NA NA +f_cyan_free_to_J9Z38 2.277e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA + +Estimated Eigenvalues of SFORB model(s): +cyan_b1 cyan_b2 cyan_g +0.15646 0.01235 0.33341 + +Resulting formation fractions: + ff +cyan_free_JCZ38 0.5787 +cyan_free_J9Z38 0.2277 +cyan_free_sink 0.1936 +cyan_free 1.0000 +JCZ38_JSE76 1.0000 +JCZ38_sink 0.0000 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 +cyan 24.48 153.7 46.26 4.43 56.15 +JCZ38 22.94 76.2 NA NA NA +J9Z38 115.39 383.3 NA NA NA +JSE76 193.84 643.9 NA NA NA + +</code></pre> +<p></p> +<caption> +Hierarchical SFORB path 1 fit with two-component error +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 10:17:00 2023 +Date of summary: Sat Jan 28 11:22:29 2023 + +Equations: +d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound * + cyan_free + k_cyan_bound_free * cyan_bound +d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free * + cyan_bound +d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38 + * JCZ38 +d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38 + * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 1649.941 s +Using 300, 100 iterations and 10 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound + 101.3964 -2.9881 -2.7949 +log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 + -3.4376 -3.3626 -4.9792 + log_k_JSE76 f_cyan_ilr_1 f_cyan_ilr_2 + -5.8727 0.6814 6.8139 + f_JCZ38_qlogis + 13.7419 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound +cyan_free_0 5.317 0.0000 0.000 +log_k_cyan_free 0.000 0.7301 0.000 +log_k_cyan_free_bound 0.000 0.0000 1.384 +log_k_cyan_bound_free 0.000 0.0000 0.000 +log_k_JCZ38 0.000 0.0000 0.000 +log_k_J9Z38 0.000 0.0000 0.000 +log_k_JSE76 0.000 0.0000 0.000 +f_cyan_ilr_1 0.000 0.0000 0.000 +f_cyan_ilr_2 0.000 0.0000 0.000 +f_JCZ38_qlogis 0.000 0.0000 0.000 + log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 +cyan_free_0 0.000 0.000 0.000 0.000 +log_k_cyan_free 0.000 0.000 0.000 0.000 +log_k_cyan_free_bound 0.000 0.000 0.000 0.000 +log_k_cyan_bound_free 1.109 0.000 0.000 0.000 +log_k_JCZ38 0.000 2.272 0.000 0.000 +log_k_J9Z38 0.000 0.000 1.633 0.000 +log_k_JSE76 0.000 0.000 0.000 1.271 +f_cyan_ilr_1 0.000 0.000 0.000 0.000 +f_cyan_ilr_2 0.000 0.000 0.000 0.000 +f_JCZ38_qlogis 0.000 0.000 0.000 0.000 + f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis +cyan_free_0 0.0000 0.00 0.00 +log_k_cyan_free 0.0000 0.00 0.00 +log_k_cyan_free_bound 0.0000 0.00 0.00 +log_k_cyan_bound_free 0.0000 0.00 0.00 +log_k_JCZ38 0.0000 0.00 0.00 +log_k_J9Z38 0.0000 0.00 0.00 +log_k_JSE76 0.0000 0.00 0.00 +f_cyan_ilr_1 0.6838 0.00 0.00 +f_cyan_ilr_2 0.0000 11.84 0.00 +f_JCZ38_qlogis 0.0000 0.00 16.14 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2400 2392 -1180 + +Optimised parameters: + est. lower upper +cyan_free_0 100.69983 NA NA +log_k_cyan_free -3.11584 NA NA +log_k_cyan_free_bound -3.15216 NA NA +log_k_cyan_bound_free -3.65986 NA NA +log_k_JCZ38 -3.47811 NA NA +log_k_J9Z38 -5.08835 NA NA +log_k_JSE76 -5.55514 NA NA +f_cyan_ilr_1 0.66764 NA NA +f_cyan_ilr_2 0.78329 NA NA +f_JCZ38_qlogis 25.35245 NA NA +a.1 2.99088 NA NA +b.1 0.04346 NA NA +SD.log_k_cyan_free 0.48797 NA NA +SD.log_k_cyan_bound_free 0.27243 NA NA +SD.log_k_JCZ38 1.42450 NA NA +SD.log_k_J9Z38 0.63496 NA NA +SD.log_k_JSE76 0.55951 NA NA +SD.f_cyan_ilr_1 0.32687 NA NA +SD.f_cyan_ilr_2 0.48056 NA NA +SD.f_JCZ38_qlogis 0.43818 NA NA + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_cyan_free 0.4880 NA NA +SD.log_k_cyan_bound_free 0.2724 NA NA +SD.log_k_JCZ38 1.4245 NA NA +SD.log_k_J9Z38 0.6350 NA NA +SD.log_k_JSE76 0.5595 NA NA +SD.f_cyan_ilr_1 0.3269 NA NA +SD.f_cyan_ilr_2 0.4806 NA NA +SD.f_JCZ38_qlogis 0.4382 NA NA + +Variance model: + est. lower upper +a.1 2.99088 NA NA +b.1 0.04346 NA NA + +Backtransformed parameters: + est. lower upper +cyan_free_0 1.007e+02 NA NA +k_cyan_free 4.434e-02 NA NA +k_cyan_free_bound 4.276e-02 NA NA +k_cyan_bound_free 2.574e-02 NA NA +k_JCZ38 3.087e-02 NA NA +k_J9Z38 6.168e-03 NA NA +k_JSE76 3.868e-03 NA NA +f_cyan_free_to_JCZ38 6.143e-01 NA NA +f_cyan_free_to_J9Z38 2.389e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA + +Estimated Eigenvalues of SFORB model(s): +cyan_b1 cyan_b2 cyan_g +0.10161 0.01123 0.36636 + +Resulting formation fractions: + ff +cyan_free_JCZ38 6.143e-01 +cyan_free_J9Z38 2.389e-01 +cyan_free_sink 1.468e-01 +cyan_free 1.000e+00 +JCZ38_JSE76 1.000e+00 +JCZ38_sink 9.763e-12 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 +cyan 25.91 164.4 49.49 6.822 61.72 +JCZ38 22.46 74.6 NA NA NA +J9Z38 112.37 373.3 NA NA NA +JSE76 179.22 595.4 NA NA NA + +</code></pre> +<p></p> +<caption> +Hierarchical HS path 1 fit with constant variance +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 10:11:04 2023 +Date of summary: Sat Jan 28 11:22:29 2023 + +Equations: +d_cyan/dt = - ifelse(time <= tb, k1, k2) * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * ifelse(time <= tb, k1, k2) * cyan - + k_JCZ38 * JCZ38 +d_J9Z38/dt = + f_cyan_to_J9Z38 * ifelse(time <= tb, k1, k2) * cyan - + k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 1294.259 s +Using 300, 100 iterations and 10 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 102.8738 -3.4490 -4.9348 -5.5989 0.6469 + f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 log_tb + 1.2854 9.7193 -2.9084 -4.1810 1.7813 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 5.409 0.00 0.00 0.000 0.0000 +log_k_JCZ38 0.000 2.33 0.00 0.000 0.0000 +log_k_J9Z38 0.000 0.00 1.59 0.000 0.0000 +log_k_JSE76 0.000 0.00 0.00 1.006 0.0000 +f_cyan_ilr_1 0.000 0.00 0.00 0.000 0.6371 +f_cyan_ilr_2 0.000 0.00 0.00 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.00 0.00 0.000 0.0000 +log_k1 0.000 0.00 0.00 0.000 0.0000 +log_k2 0.000 0.00 0.00 0.000 0.0000 +log_tb 0.000 0.00 0.00 0.000 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 log_tb +cyan_0 0.000 0.00 0.0000 0.0000 0.0000 +log_k_JCZ38 0.000 0.00 0.0000 0.0000 0.0000 +log_k_J9Z38 0.000 0.00 0.0000 0.0000 0.0000 +log_k_JSE76 0.000 0.00 0.0000 0.0000 0.0000 +f_cyan_ilr_1 0.000 0.00 0.0000 0.0000 0.0000 +f_cyan_ilr_2 2.167 0.00 0.0000 0.0000 0.0000 +f_JCZ38_qlogis 0.000 10.22 0.0000 0.0000 0.0000 +log_k1 0.000 0.00 0.7003 0.0000 0.0000 +log_k2 0.000 0.00 0.0000 0.8928 0.0000 +log_tb 0.000 0.00 0.0000 0.0000 0.6774 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2427 2420 -1194 + +Optimised parameters: + est. lower upper +cyan_0 101.84849 NA NA +log_k_JCZ38 -3.47365 NA NA +log_k_J9Z38 -5.10562 NA NA +log_k_JSE76 -5.60318 NA NA +f_cyan_ilr_1 0.66127 NA NA +f_cyan_ilr_2 0.60283 NA NA +f_JCZ38_qlogis 45.06408 NA NA +log_k1 -3.10124 NA NA +log_k2 -4.39028 NA NA +log_tb 2.32256 NA NA +a.1 3.32683 NA NA +SD.log_k_JCZ38 1.41427 NA NA +SD.log_k_J9Z38 0.54767 NA NA +SD.log_k_JSE76 0.62147 NA NA +SD.f_cyan_ilr_1 0.30189 NA NA +SD.f_cyan_ilr_2 0.34960 NA NA +SD.f_JCZ38_qlogis 0.04644 NA NA +SD.log_k1 0.39534 NA NA +SD.log_k2 0.43468 NA NA +SD.log_tb 0.60781 NA NA + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_JCZ38 1.41427 NA NA +SD.log_k_J9Z38 0.54767 NA NA +SD.log_k_JSE76 0.62147 NA NA +SD.f_cyan_ilr_1 0.30189 NA NA +SD.f_cyan_ilr_2 0.34960 NA NA +SD.f_JCZ38_qlogis 0.04644 NA NA +SD.log_k1 0.39534 NA NA +SD.log_k2 0.43468 NA NA +SD.log_tb 0.60781 NA NA + +Variance model: + est. lower upper +a.1 3.327 NA NA + +Backtransformed parameters: + est. lower upper +cyan_0 1.018e+02 NA NA +k_JCZ38 3.100e-02 NA NA +k_J9Z38 6.063e-03 NA NA +k_JSE76 3.686e-03 NA NA +f_cyan_to_JCZ38 5.910e-01 NA NA +f_cyan_to_J9Z38 2.320e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA +k1 4.499e-02 NA NA +k2 1.240e-02 NA NA +tb 1.020e+01 NA NA + +Resulting formation fractions: + ff +cyan_JCZ38 0.591 +cyan_J9Z38 0.232 +cyan_sink 0.177 +JCZ38_JSE76 1.000 +JCZ38_sink 0.000 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +cyan 29.09 158.91 47.84 15.41 55.91 +JCZ38 22.36 74.27 NA NA NA +J9Z38 114.33 379.80 NA NA NA +JSE76 188.04 624.66 NA NA NA + +</code></pre> +<p></p> +<caption> +Hierarchical HS path 1 fit with two-component error +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 10:11:24 2023 +Date of summary: Sat Jan 28 11:22:29 2023 + +Equations: +d_cyan/dt = - ifelse(time <= tb, k1, k2) * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * ifelse(time <= tb, k1, k2) * cyan - + k_JCZ38 * JCZ38 +d_J9Z38/dt = + f_cyan_to_J9Z38 * ifelse(time <= tb, k1, k2) * cyan - + k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 1313.805 s +Using 300, 100 iterations and 10 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 101.168 -3.358 -4.941 -5.794 0.676 + f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 log_tb + 5.740 13.863 -3.147 -4.262 2.173 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 5.79 0.000 0.000 0.000 0.0000 +log_k_JCZ38 0.00 2.271 0.000 0.000 0.0000 +log_k_J9Z38 0.00 0.000 1.614 0.000 0.0000 +log_k_JSE76 0.00 0.000 0.000 1.264 0.0000 +f_cyan_ilr_1 0.00 0.000 0.000 0.000 0.6761 +f_cyan_ilr_2 0.00 0.000 0.000 0.000 0.0000 +f_JCZ38_qlogis 0.00 0.000 0.000 0.000 0.0000 +log_k1 0.00 0.000 0.000 0.000 0.0000 +log_k2 0.00 0.000 0.000 0.000 0.0000 +log_tb 0.00 0.000 0.000 0.000 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 log_tb +cyan_0 0.000 0.00 0.0000 0.0000 0.000 +log_k_JCZ38 0.000 0.00 0.0000 0.0000 0.000 +log_k_J9Z38 0.000 0.00 0.0000 0.0000 0.000 +log_k_JSE76 0.000 0.00 0.0000 0.0000 0.000 +f_cyan_ilr_1 0.000 0.00 0.0000 0.0000 0.000 +f_cyan_ilr_2 9.572 0.00 0.0000 0.0000 0.000 +f_JCZ38_qlogis 0.000 19.19 0.0000 0.0000 0.000 +log_k1 0.000 0.00 0.8705 0.0000 0.000 +log_k2 0.000 0.00 0.0000 0.9288 0.000 +log_tb 0.000 0.00 0.0000 0.0000 1.065 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2422 2414 -1190 + +Optimised parameters: + est. lower upper +cyan_0 100.9521 NA NA +log_k_JCZ38 -3.4629 NA NA +log_k_J9Z38 -5.0346 NA NA +log_k_JSE76 -5.5722 NA NA +f_cyan_ilr_1 0.6560 NA NA +f_cyan_ilr_2 0.7983 NA NA +f_JCZ38_qlogis 42.7949 NA NA +log_k1 -3.1721 NA NA +log_k2 -4.4039 NA NA +log_tb 2.3994 NA NA +a.1 3.0586 NA NA +b.1 0.0380 NA NA +SD.log_k_JCZ38 1.3754 NA NA +SD.log_k_J9Z38 0.6703 NA NA +SD.log_k_JSE76 0.5876 NA NA +SD.f_cyan_ilr_1 0.3272 NA NA +SD.f_cyan_ilr_2 0.5300 NA NA +SD.f_JCZ38_qlogis 6.4465 NA NA +SD.log_k1 0.4135 NA NA +SD.log_k2 0.4182 NA NA +SD.log_tb 0.6035 NA NA + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_JCZ38 1.3754 NA NA +SD.log_k_J9Z38 0.6703 NA NA +SD.log_k_JSE76 0.5876 NA NA +SD.f_cyan_ilr_1 0.3272 NA NA +SD.f_cyan_ilr_2 0.5300 NA NA +SD.f_JCZ38_qlogis 6.4465 NA NA +SD.log_k1 0.4135 NA NA +SD.log_k2 0.4182 NA NA +SD.log_tb 0.6035 NA NA + +Variance model: + est. lower upper +a.1 3.059 NA NA +b.1 0.038 NA NA + +Backtransformed parameters: + est. lower upper +cyan_0 1.010e+02 NA NA +k_JCZ38 3.134e-02 NA NA +k_J9Z38 6.509e-03 NA NA +k_JSE76 3.802e-03 NA NA +f_cyan_to_JCZ38 6.127e-01 NA NA +f_cyan_to_J9Z38 2.423e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA +k1 4.191e-02 NA NA +k2 1.223e-02 NA NA +tb 1.102e+01 NA NA + +Resulting formation fractions: + ff +cyan_JCZ38 0.6127 +cyan_J9Z38 0.2423 +cyan_sink 0.1449 +JCZ38_JSE76 1.0000 +JCZ38_sink 0.0000 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +cyan 29.94 161.54 48.63 16.54 56.68 +JCZ38 22.12 73.47 NA NA NA +J9Z38 106.50 353.77 NA NA NA +JSE76 182.30 605.60 NA NA NA + +</code></pre> +<p></p> +</div> +<div class="section level4"> +<h4 id="pathway-2">Pathway 2<a class="anchor" aria-label="anchor" href="#pathway-2"></a> +</h4> +<caption> +Hierarchical FOMC path 2 fit with constant variance +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 10:34:28 2023 +Date of summary: Sat Jan 28 11:22:29 2023 + +Equations: +d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) * + cyan - k_JCZ38 * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 +d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) * + cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 1030.246 s +Using 300, 100 iterations and 10 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 101.8173 -1.8998 -5.1449 -2.5415 0.6705 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta + 4.4669 16.1281 13.3327 -0.2314 2.8738 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 5.742 0.000 0.000 0.00 0.0000 +log_k_JCZ38 0.000 1.402 0.000 0.00 0.0000 +log_k_J9Z38 0.000 0.000 1.718 0.00 0.0000 +log_k_JSE76 0.000 0.000 0.000 3.57 0.0000 +f_cyan_ilr_1 0.000 0.000 0.000 0.00 0.5926 +f_cyan_ilr_2 0.000 0.000 0.000 0.00 0.0000 +f_JCZ38_qlogis 0.000 0.000 0.000 0.00 0.0000 +f_JSE76_qlogis 0.000 0.000 0.000 0.00 0.0000 +log_alpha 0.000 0.000 0.000 0.00 0.0000 +log_beta 0.000 0.000 0.000 0.00 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta +cyan_0 0.00 0.00 0.00 0.0000 0.0000 +log_k_JCZ38 0.00 0.00 0.00 0.0000 0.0000 +log_k_J9Z38 0.00 0.00 0.00 0.0000 0.0000 +log_k_JSE76 0.00 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_1 0.00 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_2 10.56 0.00 0.00 0.0000 0.0000 +f_JCZ38_qlogis 0.00 12.04 0.00 0.0000 0.0000 +f_JSE76_qlogis 0.00 0.00 15.26 0.0000 0.0000 +log_alpha 0.00 0.00 0.00 0.4708 0.0000 +log_beta 0.00 0.00 0.00 0.0000 0.4432 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2308 2301 -1134 + +Optimised parameters: + est. lower upper +cyan_0 101.9586 99.22024 104.69700 +log_k_JCZ38 -2.4861 -3.17661 -1.79560 +log_k_J9Z38 -5.3926 -6.08842 -4.69684 +log_k_JSE76 -3.1193 -4.12904 -2.10962 +f_cyan_ilr_1 0.7368 0.42085 1.05276 +f_cyan_ilr_2 0.6196 0.06052 1.17861 +f_JCZ38_qlogis 4.8970 -4.68003 14.47398 +f_JSE76_qlogis 4.4066 -1.02087 9.83398 +log_alpha -0.3021 -0.68264 0.07838 +log_beta 2.7438 2.57970 2.90786 +a.1 2.9008 2.69920 3.10245 +SD.cyan_0 2.7081 0.64216 4.77401 +SD.log_k_JCZ38 0.7043 0.19951 1.20907 +SD.log_k_J9Z38 0.6248 0.05790 1.19180 +SD.log_k_JSE76 1.0750 0.33157 1.81839 +SD.f_cyan_ilr_1 0.3429 0.11688 0.56892 +SD.f_cyan_ilr_2 0.4774 0.09381 0.86097 +SD.f_JCZ38_qlogis 1.5565 -7.83970 10.95279 +SD.f_JSE76_qlogis 1.6871 -1.25577 4.63000 +SD.log_alpha 0.4216 0.15913 0.68405 + +Correlation: + cyan_0 l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 f_JSE76 +log_k_JCZ38 -0.0167 +log_k_J9Z38 -0.0307 0.0057 +log_k_JSE76 -0.0032 0.1358 0.0009 +f_cyan_ilr_1 -0.0087 0.0206 -0.1158 -0.0009 +f_cyan_ilr_2 -0.1598 0.0690 0.1770 0.0002 -0.0007 +f_JCZ38_qlogis 0.0966 -0.1132 -0.0440 0.0182 -0.1385 -0.4583 +f_JSE76_qlogis -0.0647 0.1157 0.0333 -0.0026 0.1110 0.3620 -0.8586 +log_alpha -0.0389 0.0113 0.0209 0.0021 0.0041 0.0451 -0.0605 0.0412 +log_beta -0.2508 0.0533 0.0977 0.0098 0.0220 0.2741 -0.2934 0.1999 + log_lph +log_k_JCZ38 +log_k_J9Z38 +log_k_JSE76 +f_cyan_ilr_1 +f_cyan_ilr_2 +f_JCZ38_qlogis +f_JSE76_qlogis +log_alpha +log_beta 0.2281 + +Random effects: + est. lower upper +SD.cyan_0 2.7081 0.64216 4.7740 +SD.log_k_JCZ38 0.7043 0.19951 1.2091 +SD.log_k_J9Z38 0.6248 0.05790 1.1918 +SD.log_k_JSE76 1.0750 0.33157 1.8184 +SD.f_cyan_ilr_1 0.3429 0.11688 0.5689 +SD.f_cyan_ilr_2 0.4774 0.09381 0.8610 +SD.f_JCZ38_qlogis 1.5565 -7.83970 10.9528 +SD.f_JSE76_qlogis 1.6871 -1.25577 4.6300 +SD.log_alpha 0.4216 0.15913 0.6840 + +Variance model: + est. lower upper +a.1 2.901 2.699 3.102 + +Backtransformed parameters: + est. lower upper +cyan_0 101.95862 99.220240 1.047e+02 +k_JCZ38 0.08323 0.041727 1.660e-01 +k_J9Z38 0.00455 0.002269 9.124e-03 +k_JSE76 0.04419 0.016098 1.213e-01 +f_cyan_to_JCZ38 0.61318 NA NA +f_cyan_to_J9Z38 0.21630 NA NA +f_JCZ38_to_JSE76 0.99259 0.009193 1.000e+00 +f_JSE76_to_JCZ38 0.98795 0.264857 9.999e-01 +alpha 0.73924 0.505281 1.082e+00 +beta 15.54568 13.193194 1.832e+01 + +Resulting formation fractions: + ff +cyan_JCZ38 0.613182 +cyan_J9Z38 0.216298 +cyan_sink 0.170519 +JCZ38_JSE76 0.992586 +JCZ38_sink 0.007414 +JSE76_JCZ38 0.987950 +JSE76_sink 0.012050 + +Estimated disappearance times: + DT50 DT90 DT50back +cyan 24.157 334.68 100.7 +JCZ38 8.328 27.66 NA +J9Z38 152.341 506.06 NA +JSE76 15.687 52.11 NA + +</code></pre> +<p></p> +<caption> +Hierarchical FOMC path 2 fit with two-component error +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 10:37:36 2023 +Date of summary: Sat Jan 28 11:22:29 2023 + +Equations: +d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) * + cyan - k_JCZ38 * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 +d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) * + cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 1217.619 s +Using 300, 100 iterations and 10 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 101.9028 -1.9055 -5.0249 -2.5646 0.6807 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta + 4.8883 16.0676 9.3923 -0.1346 3.0364 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 6.321 0.000 0.000 0.000 0.0000 +log_k_JCZ38 0.000 1.392 0.000 0.000 0.0000 +log_k_J9Z38 0.000 0.000 1.561 0.000 0.0000 +log_k_JSE76 0.000 0.000 0.000 3.614 0.0000 +f_cyan_ilr_1 0.000 0.000 0.000 0.000 0.6339 +f_cyan_ilr_2 0.000 0.000 0.000 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.000 0.000 0.000 0.0000 +f_JSE76_qlogis 0.000 0.000 0.000 0.000 0.0000 +log_alpha 0.000 0.000 0.000 0.000 0.0000 +log_beta 0.000 0.000 0.000 0.000 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta +cyan_0 0.00 0.00 0.00 0.0000 0.0000 +log_k_JCZ38 0.00 0.00 0.00 0.0000 0.0000 +log_k_J9Z38 0.00 0.00 0.00 0.0000 0.0000 +log_k_JSE76 0.00 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_1 0.00 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_2 10.41 0.00 0.00 0.0000 0.0000 +f_JCZ38_qlogis 0.00 12.24 0.00 0.0000 0.0000 +f_JSE76_qlogis 0.00 0.00 15.13 0.0000 0.0000 +log_alpha 0.00 0.00 0.00 0.3701 0.0000 +log_beta 0.00 0.00 0.00 0.0000 0.5662 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2248 2240 -1103 + +Optimised parameters: + est. lower upper +cyan_0 101.55545 9.920e+01 1.039e+02 +log_k_JCZ38 -2.37354 -2.928e+00 -1.819e+00 +log_k_J9Z38 -5.14736 -5.960e+00 -4.335e+00 +log_k_JSE76 -3.07802 -4.243e+00 -1.913e+00 +f_cyan_ilr_1 0.71263 3.655e-01 1.060e+00 +f_cyan_ilr_2 0.95202 2.701e-01 1.634e+00 +f_JCZ38_qlogis 3.58473 1.251e+00 5.919e+00 +f_JSE76_qlogis 19.03623 -1.037e+07 1.037e+07 +log_alpha -0.15297 -4.490e-01 1.431e-01 +log_beta 2.99230 2.706e+00 3.278e+00 +a.1 2.04816 NA NA +b.1 0.06886 NA NA +SD.log_k_JCZ38 0.56174 NA NA +SD.log_k_J9Z38 0.86509 NA NA +SD.log_k_JSE76 1.28450 NA NA +SD.f_cyan_ilr_1 0.38705 NA NA +SD.f_cyan_ilr_2 0.54153 NA NA +SD.f_JCZ38_qlogis 1.65311 NA NA +SD.f_JSE76_qlogis 7.51468 NA NA +SD.log_alpha 0.31586 NA NA +SD.log_beta 0.24696 NA NA + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_JCZ38 0.5617 NA NA +SD.log_k_J9Z38 0.8651 NA NA +SD.log_k_JSE76 1.2845 NA NA +SD.f_cyan_ilr_1 0.3870 NA NA +SD.f_cyan_ilr_2 0.5415 NA NA +SD.f_JCZ38_qlogis 1.6531 NA NA +SD.f_JSE76_qlogis 7.5147 NA NA +SD.log_alpha 0.3159 NA NA +SD.log_beta 0.2470 NA NA + +Variance model: + est. lower upper +a.1 2.04816 NA NA +b.1 0.06886 NA NA + +Backtransformed parameters: + est. lower upper +cyan_0 1.016e+02 99.20301 103.9079 +k_JCZ38 9.315e-02 0.05349 0.1622 +k_J9Z38 5.815e-03 0.00258 0.0131 +k_JSE76 4.605e-02 0.01436 0.1477 +f_cyan_to_JCZ38 6.438e-01 NA NA +f_cyan_to_J9Z38 2.350e-01 NA NA +f_JCZ38_to_JSE76 9.730e-01 0.77745 0.9973 +f_JSE76_to_JCZ38 1.000e+00 0.00000 1.0000 +alpha 8.582e-01 0.63824 1.1538 +beta 1.993e+01 14.97621 26.5262 + +Resulting formation fractions: + ff +cyan_JCZ38 6.438e-01 +cyan_J9Z38 2.350e-01 +cyan_sink 1.212e-01 +JCZ38_JSE76 9.730e-01 +JCZ38_sink 2.700e-02 +JSE76_JCZ38 1.000e+00 +JSE76_sink 5.403e-09 + +Estimated disappearance times: + DT50 DT90 DT50back +cyan 24.771 271.70 81.79 +JCZ38 7.441 24.72 NA +J9Z38 119.205 395.99 NA +JSE76 15.052 50.00 NA + +</code></pre> +<p></p> +<caption> +Hierarchical DFOP path 2 fit with constant variance +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 10:38:34 2023 +Date of summary: Sat Jan 28 11:22:29 2023 + +Equations: +d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 + + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 +d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 1276.128 s +Using 300, 100 iterations and 10 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 102.4358 -2.3107 -5.3123 -3.7120 0.6753 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 + 1.1462 12.4095 12.3630 -1.9317 -4.4557 + g_qlogis + -0.5648 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 4.594 0.0000 0.000 0.0 0.0000 +log_k_JCZ38 0.000 0.7966 0.000 0.0 0.0000 +log_k_J9Z38 0.000 0.0000 1.561 0.0 0.0000 +log_k_JSE76 0.000 0.0000 0.000 0.8 0.0000 +f_cyan_ilr_1 0.000 0.0000 0.000 0.0 0.6349 +f_cyan_ilr_2 0.000 0.0000 0.000 0.0 0.0000 +f_JCZ38_qlogis 0.000 0.0000 0.000 0.0 0.0000 +f_JSE76_qlogis 0.000 0.0000 0.000 0.0 0.0000 +log_k1 0.000 0.0000 0.000 0.0 0.0000 +log_k2 0.000 0.0000 0.000 0.0 0.0000 +g_qlogis 0.000 0.0000 0.000 0.0 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 +cyan_0 0.000 0.00 0.0 0.000 0.0000 +log_k_JCZ38 0.000 0.00 0.0 0.000 0.0000 +log_k_J9Z38 0.000 0.00 0.0 0.000 0.0000 +log_k_JSE76 0.000 0.00 0.0 0.000 0.0000 +f_cyan_ilr_1 0.000 0.00 0.0 0.000 0.0000 +f_cyan_ilr_2 1.797 0.00 0.0 0.000 0.0000 +f_JCZ38_qlogis 0.000 13.85 0.0 0.000 0.0000 +f_JSE76_qlogis 0.000 0.00 14.1 0.000 0.0000 +log_k1 0.000 0.00 0.0 1.106 0.0000 +log_k2 0.000 0.00 0.0 0.000 0.6141 +g_qlogis 0.000 0.00 0.0 0.000 0.0000 + g_qlogis +cyan_0 0.000 +log_k_JCZ38 0.000 +log_k_J9Z38 0.000 +log_k_JSE76 0.000 +f_cyan_ilr_1 0.000 +f_cyan_ilr_2 0.000 +f_JCZ38_qlogis 0.000 +f_JSE76_qlogis 0.000 +log_k1 0.000 +log_k2 0.000 +g_qlogis 1.595 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2290 2281 -1123 + +Optimised parameters: + est. lower upper +cyan_0 102.6903 101.44420 103.9365 +log_k_JCZ38 -2.4018 -2.98058 -1.8230 +log_k_J9Z38 -5.1865 -5.92931 -4.4437 +log_k_JSE76 -3.0784 -4.25226 -1.9045 +f_cyan_ilr_1 0.7157 0.37625 1.0551 +f_cyan_ilr_2 0.7073 0.20136 1.2132 +f_JCZ38_qlogis 4.6797 0.43240 8.9269 +f_JSE76_qlogis 5.0080 -1.01380 11.0299 +log_k1 -1.9620 -2.62909 -1.2949 +log_k2 -4.4894 -4.94958 -4.0292 +g_qlogis -0.4658 -1.34443 0.4129 +a.1 2.7158 2.52576 2.9059 +SD.log_k_JCZ38 0.5818 0.15679 1.0067 +SD.log_k_J9Z38 0.7421 0.16751 1.3167 +SD.log_k_JSE76 1.2841 0.43247 2.1356 +SD.f_cyan_ilr_1 0.3748 0.13040 0.6192 +SD.f_cyan_ilr_2 0.4550 0.08396 0.8261 +SD.f_JCZ38_qlogis 2.0862 -0.73390 4.9062 +SD.f_JSE76_qlogis 1.9585 -3.14773 7.0647 +SD.log_k1 0.7389 0.25761 1.2201 +SD.log_k2 0.5132 0.18143 0.8450 +SD.g_qlogis 0.9870 0.35773 1.6164 + +Correlation: + cyan_0 l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 f_JSE76 +log_k_JCZ38 -0.0170 +log_k_J9Z38 -0.0457 0.0016 +log_k_JSE76 -0.0046 0.1183 0.0005 +f_cyan_ilr_1 0.0079 0.0072 -0.0909 0.0003 +f_cyan_ilr_2 -0.3114 0.0343 0.1542 0.0023 -0.0519 +f_JCZ38_qlogis 0.0777 -0.0601 -0.0152 0.0080 -0.0520 -0.2524 +f_JSE76_qlogis -0.0356 0.0817 0.0073 0.0051 0.0388 0.1959 -0.6236 +log_k1 0.0848 -0.0028 0.0010 -0.0010 -0.0014 -0.0245 0.0121 -0.0177 +log_k2 0.0274 -0.0001 0.0075 0.0000 -0.0023 -0.0060 0.0000 -0.0130 +g_qlogis 0.0159 0.0002 -0.0095 0.0002 0.0029 -0.0140 -0.0001 0.0149 + log_k1 log_k2 +log_k_JCZ38 +log_k_J9Z38 +log_k_JSE76 +f_cyan_ilr_1 +f_cyan_ilr_2 +f_JCZ38_qlogis +f_JSE76_qlogis +log_k1 +log_k2 0.0280 +g_qlogis -0.0278 -0.0310 + +Random effects: + est. lower upper +SD.log_k_JCZ38 0.5818 0.15679 1.0067 +SD.log_k_J9Z38 0.7421 0.16751 1.3167 +SD.log_k_JSE76 1.2841 0.43247 2.1356 +SD.f_cyan_ilr_1 0.3748 0.13040 0.6192 +SD.f_cyan_ilr_2 0.4550 0.08396 0.8261 +SD.f_JCZ38_qlogis 2.0862 -0.73390 4.9062 +SD.f_JSE76_qlogis 1.9585 -3.14773 7.0647 +SD.log_k1 0.7389 0.25761 1.2201 +SD.log_k2 0.5132 0.18143 0.8450 +SD.g_qlogis 0.9870 0.35773 1.6164 + +Variance model: + est. lower upper +a.1 2.716 2.526 2.906 + +Backtransformed parameters: + est. lower upper +cyan_0 1.027e+02 1.014e+02 103.93649 +k_JCZ38 9.056e-02 5.076e-02 0.16154 +k_J9Z38 5.591e-03 2.660e-03 0.01175 +k_JSE76 4.603e-02 1.423e-02 0.14890 +f_cyan_to_JCZ38 6.184e-01 NA NA +f_cyan_to_J9Z38 2.248e-01 NA NA +f_JCZ38_to_JSE76 9.908e-01 6.064e-01 0.99987 +f_JSE76_to_JCZ38 9.934e-01 2.662e-01 0.99998 +k1 1.406e-01 7.214e-02 0.27393 +k2 1.123e-02 7.086e-03 0.01779 +g 3.856e-01 2.068e-01 0.60177 + +Resulting formation fractions: + ff +cyan_JCZ38 0.618443 +cyan_J9Z38 0.224770 +cyan_sink 0.156787 +JCZ38_JSE76 0.990803 +JCZ38_sink 0.009197 +JSE76_JCZ38 0.993360 +JSE76_sink 0.006640 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +cyan 21.674 161.70 48.68 4.931 61.74 +JCZ38 7.654 25.43 NA NA NA +J9Z38 123.966 411.81 NA NA NA +JSE76 15.057 50.02 NA NA NA + +</code></pre> +<p></p> +<caption> +Hierarchical DFOP path 2 fit with two-component error +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 10:45:32 2023 +Date of summary: Sat Jan 28 11:22:29 2023 + +Equations: +d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 + + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 +d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 1693.767 s +Using 300, 100 iterations and 10 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 101.7523 -1.5948 -5.0119 -2.2723 0.6719 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 + 5.1681 12.8238 12.4130 -2.0057 -4.5526 + g_qlogis + -0.5805 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 5.627 0.000 0.000 0.000 0.0000 +log_k_JCZ38 0.000 2.327 0.000 0.000 0.0000 +log_k_J9Z38 0.000 0.000 1.664 0.000 0.0000 +log_k_JSE76 0.000 0.000 0.000 4.566 0.0000 +f_cyan_ilr_1 0.000 0.000 0.000 0.000 0.6519 +f_cyan_ilr_2 0.000 0.000 0.000 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.000 0.000 0.000 0.0000 +f_JSE76_qlogis 0.000 0.000 0.000 0.000 0.0000 +log_k1 0.000 0.000 0.000 0.000 0.0000 +log_k2 0.000 0.000 0.000 0.000 0.0000 +g_qlogis 0.000 0.000 0.000 0.000 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 +cyan_0 0.0 0.00 0.00 0.0000 0.0000 +log_k_JCZ38 0.0 0.00 0.00 0.0000 0.0000 +log_k_J9Z38 0.0 0.00 0.00 0.0000 0.0000 +log_k_JSE76 0.0 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_1 0.0 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_2 10.1 0.00 0.00 0.0000 0.0000 +f_JCZ38_qlogis 0.0 13.99 0.00 0.0000 0.0000 +f_JSE76_qlogis 0.0 0.00 14.15 0.0000 0.0000 +log_k1 0.0 0.00 0.00 0.8452 0.0000 +log_k2 0.0 0.00 0.00 0.0000 0.5968 +g_qlogis 0.0 0.00 0.00 0.0000 0.0000 + g_qlogis +cyan_0 0.000 +log_k_JCZ38 0.000 +log_k_J9Z38 0.000 +log_k_JSE76 0.000 +f_cyan_ilr_1 0.000 +f_cyan_ilr_2 0.000 +f_JCZ38_qlogis 0.000 +f_JSE76_qlogis 0.000 +log_k1 0.000 +log_k2 0.000 +g_qlogis 1.691 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2234 2226 -1095 + +Optimised parameters: + est. lower upper +cyan_0 101.10667 9.903e+01 103.18265 +log_k_JCZ38 -2.49437 -3.297e+00 -1.69221 +log_k_J9Z38 -5.08171 -5.875e+00 -4.28846 +log_k_JSE76 -3.20072 -4.180e+00 -2.22163 +f_cyan_ilr_1 0.71059 3.639e-01 1.05727 +f_cyan_ilr_2 1.15398 2.981e-01 2.00984 +f_JCZ38_qlogis 3.18027 1.056e+00 5.30452 +f_JSE76_qlogis 5.61578 -2.505e+01 36.28077 +log_k1 -2.38875 -2.517e+00 -2.26045 +log_k2 -4.67246 -4.928e+00 -4.41715 +g_qlogis -0.28231 -1.135e+00 0.57058 +a.1 2.08190 1.856e+00 2.30785 +b.1 0.06114 5.015e-02 0.07214 +SD.log_k_JCZ38 0.84622 2.637e-01 1.42873 +SD.log_k_J9Z38 0.84564 2.566e-01 1.43464 +SD.log_k_JSE76 1.04385 3.242e-01 1.76351 +SD.f_cyan_ilr_1 0.38568 1.362e-01 0.63514 +SD.f_cyan_ilr_2 0.68046 7.166e-02 1.28925 +SD.f_JCZ38_qlogis 1.25244 -4.213e-02 2.54700 +SD.f_JSE76_qlogis 0.28202 -1.515e+03 1515.87968 +SD.log_k2 0.25749 7.655e-02 0.43843 +SD.g_qlogis 0.94535 3.490e-01 1.54174 + +Correlation: + cyan_0 l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 f_JSE76 +log_k_JCZ38 -0.0086 +log_k_J9Z38 -0.0363 -0.0007 +log_k_JSE76 0.0015 0.1210 -0.0017 +f_cyan_ilr_1 -0.0048 0.0095 -0.0572 0.0030 +f_cyan_ilr_2 -0.4788 0.0328 0.1143 0.0027 -0.0316 +f_JCZ38_qlogis 0.0736 -0.0664 -0.0137 0.0145 -0.0444 -0.2175 +f_JSE76_qlogis -0.0137 0.0971 0.0035 0.0009 0.0293 0.1333 -0.6767 +log_k1 0.2345 -0.0350 -0.0099 -0.0113 -0.0126 -0.1652 0.1756 -0.2161 +log_k2 0.0440 -0.0133 0.0199 -0.0040 -0.0097 -0.0119 0.0604 -0.1306 +g_qlogis 0.0438 0.0078 -0.0123 0.0029 0.0046 -0.0363 -0.0318 0.0736 + log_k1 log_k2 +log_k_JCZ38 +log_k_J9Z38 +log_k_JSE76 +f_cyan_ilr_1 +f_cyan_ilr_2 +f_JCZ38_qlogis +f_JSE76_qlogis +log_k1 +log_k2 0.3198 +g_qlogis -0.1666 -0.0954 + +Random effects: + est. lower upper +SD.log_k_JCZ38 0.8462 2.637e-01 1.4287 +SD.log_k_J9Z38 0.8456 2.566e-01 1.4346 +SD.log_k_JSE76 1.0439 3.242e-01 1.7635 +SD.f_cyan_ilr_1 0.3857 1.362e-01 0.6351 +SD.f_cyan_ilr_2 0.6805 7.166e-02 1.2893 +SD.f_JCZ38_qlogis 1.2524 -4.213e-02 2.5470 +SD.f_JSE76_qlogis 0.2820 -1.515e+03 1515.8797 +SD.log_k2 0.2575 7.655e-02 0.4384 +SD.g_qlogis 0.9453 3.490e-01 1.5417 + +Variance model: + est. lower upper +a.1 2.08190 1.85595 2.30785 +b.1 0.06114 0.05015 0.07214 + +Backtransformed parameters: + est. lower upper +cyan_0 1.011e+02 9.903e+01 103.18265 +k_JCZ38 8.255e-02 3.701e-02 0.18411 +k_J9Z38 6.209e-03 2.809e-03 0.01373 +k_JSE76 4.073e-02 1.530e-02 0.10843 +f_cyan_to_JCZ38 6.608e-01 NA NA +f_cyan_to_J9Z38 2.419e-01 NA NA +f_JCZ38_to_JSE76 9.601e-01 7.419e-01 0.99506 +f_JSE76_to_JCZ38 9.964e-01 1.322e-11 1.00000 +k1 9.174e-02 8.070e-02 0.10430 +k2 9.349e-03 7.243e-03 0.01207 +g 4.299e-01 2.432e-01 0.63890 + +Resulting formation fractions: + ff +cyan_JCZ38 0.660808 +cyan_J9Z38 0.241904 +cyan_sink 0.097288 +JCZ38_JSE76 0.960085 +JCZ38_sink 0.039915 +JSE76_JCZ38 0.996373 +JSE76_sink 0.003627 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +cyan 24.359 186.18 56.05 7.555 74.14 +JCZ38 8.397 27.89 NA NA NA +J9Z38 111.631 370.83 NA NA NA +JSE76 17.017 56.53 NA NA NA + +</code></pre> +<p></p> +<caption> +Hierarchical SFORB path 2 fit with constant variance +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 10:38:37 2023 +Date of summary: Sat Jan 28 11:22:29 2023 + +Equations: +d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound * + cyan_free + k_cyan_bound_free * cyan_bound +d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free * + cyan_bound +d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38 + * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 +d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38 + * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 1279.102 s +Using 300, 100 iterations and 10 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound + 102.4394 -2.7673 -2.8942 +log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 + -3.6201 -2.3107 -5.3123 + log_k_JSE76 f_cyan_ilr_1 f_cyan_ilr_2 + -3.7120 0.6754 1.1448 + f_JCZ38_qlogis f_JSE76_qlogis + 13.2672 13.3538 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound +cyan_free_0 4.589 0.0000 0.00 +log_k_cyan_free 0.000 0.4849 0.00 +log_k_cyan_free_bound 0.000 0.0000 1.62 +log_k_cyan_bound_free 0.000 0.0000 0.00 +log_k_JCZ38 0.000 0.0000 0.00 +log_k_J9Z38 0.000 0.0000 0.00 +log_k_JSE76 0.000 0.0000 0.00 +f_cyan_ilr_1 0.000 0.0000 0.00 +f_cyan_ilr_2 0.000 0.0000 0.00 +f_JCZ38_qlogis 0.000 0.0000 0.00 +f_JSE76_qlogis 0.000 0.0000 0.00 + log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 +cyan_free_0 0.000 0.0000 0.000 0.0 +log_k_cyan_free 0.000 0.0000 0.000 0.0 +log_k_cyan_free_bound 0.000 0.0000 0.000 0.0 +log_k_cyan_bound_free 1.197 0.0000 0.000 0.0 +log_k_JCZ38 0.000 0.7966 0.000 0.0 +log_k_J9Z38 0.000 0.0000 1.561 0.0 +log_k_JSE76 0.000 0.0000 0.000 0.8 +f_cyan_ilr_1 0.000 0.0000 0.000 0.0 +f_cyan_ilr_2 0.000 0.0000 0.000 0.0 +f_JCZ38_qlogis 0.000 0.0000 0.000 0.0 +f_JSE76_qlogis 0.000 0.0000 0.000 0.0 + f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis +cyan_free_0 0.0000 0.000 0.00 0.00 +log_k_cyan_free 0.0000 0.000 0.00 0.00 +log_k_cyan_free_bound 0.0000 0.000 0.00 0.00 +log_k_cyan_bound_free 0.0000 0.000 0.00 0.00 +log_k_JCZ38 0.0000 0.000 0.00 0.00 +log_k_J9Z38 0.0000 0.000 0.00 0.00 +log_k_JSE76 0.0000 0.000 0.00 0.00 +f_cyan_ilr_1 0.6349 0.000 0.00 0.00 +f_cyan_ilr_2 0.0000 1.797 0.00 0.00 +f_JCZ38_qlogis 0.0000 0.000 13.84 0.00 +f_JSE76_qlogis 0.0000 0.000 0.00 14.66 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2284 2275 -1120 + +Optimised parameters: + est. lower upper +cyan_free_0 102.7730 1.015e+02 1.041e+02 +log_k_cyan_free -2.8530 -3.167e+00 -2.539e+00 +log_k_cyan_free_bound -2.7326 -3.543e+00 -1.922e+00 +log_k_cyan_bound_free -3.5582 -4.126e+00 -2.990e+00 +log_k_JCZ38 -2.3810 -2.921e+00 -1.841e+00 +log_k_J9Z38 -5.2301 -5.963e+00 -4.497e+00 +log_k_JSE76 -3.0286 -4.286e+00 -1.771e+00 +f_cyan_ilr_1 0.7081 3.733e-01 1.043e+00 +f_cyan_ilr_2 0.5847 7.846e-03 1.162e+00 +f_JCZ38_qlogis 9.5676 -1.323e+03 1.342e+03 +f_JSE76_qlogis 3.7042 7.254e-02 7.336e+00 +a.1 2.7222 2.532e+00 2.913e+00 +SD.log_k_cyan_free 0.3338 1.086e-01 5.589e-01 +SD.log_k_cyan_free_bound 0.8888 3.023e-01 1.475e+00 +SD.log_k_cyan_bound_free 0.6220 2.063e-01 1.038e+00 +SD.log_k_JCZ38 0.5221 1.334e-01 9.108e-01 +SD.log_k_J9Z38 0.7104 1.371e-01 1.284e+00 +SD.log_k_JSE76 1.3837 4.753e-01 2.292e+00 +SD.f_cyan_ilr_1 0.3620 1.248e-01 5.992e-01 +SD.f_cyan_ilr_2 0.4259 8.145e-02 7.704e-01 +SD.f_JCZ38_qlogis 3.5332 -1.037e+05 1.037e+05 +SD.f_JSE76_qlogis 1.6990 -2.771e-01 3.675e+00 + +Correlation: + cyn_f_0 lg_k_c_ lg_k_cyn_f_ lg_k_cyn_b_ l__JCZ3 l__J9Z3 +log_k_cyan_free 0.2126 +log_k_cyan_free_bound 0.0894 0.0871 +log_k_cyan_bound_free 0.0033 0.0410 0.0583 +log_k_JCZ38 -0.0708 -0.0280 -0.0147 0.0019 +log_k_J9Z38 -0.0535 -0.0138 0.0012 0.0148 0.0085 +log_k_JSE76 -0.0066 -0.0030 -0.0021 -0.0005 0.1090 0.0010 +f_cyan_ilr_1 -0.0364 -0.0157 -0.0095 -0.0015 0.0458 -0.0960 +f_cyan_ilr_2 -0.3814 -0.1104 -0.0423 0.0146 0.1540 0.1526 +f_JCZ38_qlogis 0.2507 0.0969 0.0482 -0.0097 -0.2282 -0.0363 +f_JSE76_qlogis -0.1648 -0.0710 -0.0443 -0.0087 0.2002 0.0226 + l__JSE7 f_cy__1 f_cy__2 f_JCZ38 +log_k_cyan_free +log_k_cyan_free_bound +log_k_cyan_bound_free +log_k_JCZ38 +log_k_J9Z38 +log_k_JSE76 +f_cyan_ilr_1 0.0001 +f_cyan_ilr_2 0.0031 0.0586 +f_JCZ38_qlogis 0.0023 -0.1867 -0.6255 +f_JSE76_qlogis 0.0082 0.1356 0.4519 -0.7951 + +Random effects: + est. lower upper +SD.log_k_cyan_free 0.3338 1.086e-01 5.589e-01 +SD.log_k_cyan_free_bound 0.8888 3.023e-01 1.475e+00 +SD.log_k_cyan_bound_free 0.6220 2.063e-01 1.038e+00 +SD.log_k_JCZ38 0.5221 1.334e-01 9.108e-01 +SD.log_k_J9Z38 0.7104 1.371e-01 1.284e+00 +SD.log_k_JSE76 1.3837 4.753e-01 2.292e+00 +SD.f_cyan_ilr_1 0.3620 1.248e-01 5.992e-01 +SD.f_cyan_ilr_2 0.4259 8.145e-02 7.704e-01 +SD.f_JCZ38_qlogis 3.5332 -1.037e+05 1.037e+05 +SD.f_JSE76_qlogis 1.6990 -2.771e-01 3.675e+00 + +Variance model: + est. lower upper +a.1 2.722 2.532 2.913 + +Backtransformed parameters: + est. lower upper +cyan_free_0 1.028e+02 1.015e+02 104.06475 +k_cyan_free 5.767e-02 4.213e-02 0.07894 +k_cyan_free_bound 6.505e-02 2.892e-02 0.14633 +k_cyan_bound_free 2.849e-02 1.614e-02 0.05028 +k_JCZ38 9.246e-02 5.390e-02 0.15859 +k_J9Z38 5.353e-03 2.572e-03 0.01114 +k_JSE76 4.838e-02 1.376e-02 0.17009 +f_cyan_free_to_JCZ38 6.011e-01 5.028e-01 0.83792 +f_cyan_free_to_J9Z38 2.208e-01 5.028e-01 0.83792 +f_JCZ38_to_JSE76 9.999e-01 0.000e+00 1.00000 +f_JSE76_to_JCZ38 9.760e-01 5.181e-01 0.99935 + +Estimated Eigenvalues of SFORB model(s): +cyan_b1 cyan_b2 cyan_g +0.13942 0.01178 0.35948 + +Resulting formation fractions: + ff +cyan_free_JCZ38 6.011e-01 +cyan_free_J9Z38 2.208e-01 +cyan_free_sink 1.780e-01 +cyan_free 1.000e+00 +JCZ38_JSE76 9.999e-01 +JCZ38_sink 6.996e-05 +JSE76_JCZ38 9.760e-01 +JSE76_sink 2.403e-02 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 +cyan 23.390 157.60 47.44 4.971 58.82 +JCZ38 7.497 24.90 NA NA NA +J9Z38 129.482 430.13 NA NA NA +JSE76 14.326 47.59 NA NA NA + +</code></pre> +<p></p> +<caption> +Hierarchical SFORB path 2 fit with two-component error +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 10:46:02 2023 +Date of summary: Sat Jan 28 11:22:29 2023 + +Equations: +d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound * + cyan_free + k_cyan_bound_free * cyan_bound +d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free * + cyan_bound +d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38 + * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 +d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38 + * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 1723.343 s +Using 300, 100 iterations and 10 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound + 101.751 -2.837 -3.016 +log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 + -3.660 -2.299 -5.313 + log_k_JSE76 f_cyan_ilr_1 f_cyan_ilr_2 + -3.699 0.672 5.873 + f_JCZ38_qlogis f_JSE76_qlogis + 13.216 13.338 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound +cyan_free_0 5.629 0.000 0.000 +log_k_cyan_free 0.000 0.446 0.000 +log_k_cyan_free_bound 0.000 0.000 1.449 +log_k_cyan_bound_free 0.000 0.000 0.000 +log_k_JCZ38 0.000 0.000 0.000 +log_k_J9Z38 0.000 0.000 0.000 +log_k_JSE76 0.000 0.000 0.000 +f_cyan_ilr_1 0.000 0.000 0.000 +f_cyan_ilr_2 0.000 0.000 0.000 +f_JCZ38_qlogis 0.000 0.000 0.000 +f_JSE76_qlogis 0.000 0.000 0.000 + log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 +cyan_free_0 0.000 0.0000 0.000 0.0000 +log_k_cyan_free 0.000 0.0000 0.000 0.0000 +log_k_cyan_free_bound 0.000 0.0000 0.000 0.0000 +log_k_cyan_bound_free 1.213 0.0000 0.000 0.0000 +log_k_JCZ38 0.000 0.7801 0.000 0.0000 +log_k_J9Z38 0.000 0.0000 1.575 0.0000 +log_k_JSE76 0.000 0.0000 0.000 0.8078 +f_cyan_ilr_1 0.000 0.0000 0.000 0.0000 +f_cyan_ilr_2 0.000 0.0000 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.0000 0.000 0.0000 +f_JSE76_qlogis 0.000 0.0000 0.000 0.0000 + f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis +cyan_free_0 0.0000 0.00 0.00 0.00 +log_k_cyan_free 0.0000 0.00 0.00 0.00 +log_k_cyan_free_bound 0.0000 0.00 0.00 0.00 +log_k_cyan_bound_free 0.0000 0.00 0.00 0.00 +log_k_JCZ38 0.0000 0.00 0.00 0.00 +log_k_J9Z38 0.0000 0.00 0.00 0.00 +log_k_JSE76 0.0000 0.00 0.00 0.00 +f_cyan_ilr_1 0.6519 0.00 0.00 0.00 +f_cyan_ilr_2 0.0000 10.78 0.00 0.00 +f_JCZ38_qlogis 0.0000 0.00 13.96 0.00 +f_JSE76_qlogis 0.0000 0.00 0.00 14.69 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2240 2232 -1098 + +Optimised parameters: + est. lower upper +cyan_free_0 101.10205 98.99221 103.2119 +log_k_cyan_free -3.16929 -3.61395 -2.7246 +log_k_cyan_free_bound -3.38259 -3.63022 -3.1350 +log_k_cyan_bound_free -3.81075 -4.13888 -3.4826 +log_k_JCZ38 -2.42057 -3.00756 -1.8336 +log_k_J9Z38 -5.07501 -5.85138 -4.2986 +log_k_JSE76 -3.12442 -4.21277 -2.0361 +f_cyan_ilr_1 0.70577 0.35788 1.0537 +f_cyan_ilr_2 1.14824 0.15810 2.1384 +f_JCZ38_qlogis 3.52245 0.43257 6.6123 +f_JSE76_qlogis 5.65140 -21.22295 32.5257 +a.1 2.07062 1.84329 2.2980 +b.1 0.06227 0.05124 0.0733 +SD.log_k_cyan_free 0.49468 0.18566 0.8037 +SD.log_k_cyan_bound_free 0.28972 0.07188 0.5076 +SD.log_k_JCZ38 0.58852 0.16800 1.0090 +SD.log_k_J9Z38 0.82500 0.24730 1.4027 +SD.log_k_JSE76 1.19201 0.40313 1.9809 +SD.f_cyan_ilr_1 0.38534 0.13640 0.6343 +SD.f_cyan_ilr_2 0.72463 0.10076 1.3485 +SD.f_JCZ38_qlogis 1.38223 -0.20997 2.9744 +SD.f_JSE76_qlogis 2.07989 -72.53027 76.6901 + +Correlation: + cyn_f_0 lg_k_c_ lg_k_cyn_f_ lg_k_cyn_b_ l__JCZ3 l__J9Z3 +log_k_cyan_free 0.1117 +log_k_cyan_free_bound 0.1763 0.1828 +log_k_cyan_bound_free 0.0120 0.0593 0.5030 +log_k_JCZ38 -0.0459 -0.0230 -0.0931 -0.0337 +log_k_J9Z38 -0.0381 -0.0123 -0.0139 0.0237 0.0063 +log_k_JSE76 -0.0044 -0.0038 -0.0175 -0.0072 0.1120 0.0003 +f_cyan_ilr_1 -0.0199 -0.0087 -0.0407 -0.0233 0.0268 -0.0552 +f_cyan_ilr_2 -0.4806 -0.1015 -0.2291 -0.0269 0.1156 0.1113 +f_JCZ38_qlogis 0.1805 0.0825 0.3085 0.0963 -0.1674 -0.0314 +f_JSE76_qlogis -0.1586 -0.0810 -0.3560 -0.1563 0.2025 0.0278 + l__JSE7 f_cy__1 f_cy__2 f_JCZ38 +log_k_cyan_free +log_k_cyan_free_bound +log_k_cyan_bound_free +log_k_JCZ38 +log_k_J9Z38 +log_k_JSE76 +f_cyan_ilr_1 0.0024 +f_cyan_ilr_2 0.0087 0.0172 +f_JCZ38_qlogis -0.0016 -0.1047 -0.4656 +f_JSE76_qlogis 0.0119 0.1034 0.4584 -0.8137 + +Random effects: + est. lower upper +SD.log_k_cyan_free 0.4947 0.18566 0.8037 +SD.log_k_cyan_bound_free 0.2897 0.07188 0.5076 +SD.log_k_JCZ38 0.5885 0.16800 1.0090 +SD.log_k_J9Z38 0.8250 0.24730 1.4027 +SD.log_k_JSE76 1.1920 0.40313 1.9809 +SD.f_cyan_ilr_1 0.3853 0.13640 0.6343 +SD.f_cyan_ilr_2 0.7246 0.10076 1.3485 +SD.f_JCZ38_qlogis 1.3822 -0.20997 2.9744 +SD.f_JSE76_qlogis 2.0799 -72.53027 76.6901 + +Variance model: + est. lower upper +a.1 2.07062 1.84329 2.2980 +b.1 0.06227 0.05124 0.0733 + +Backtransformed parameters: + est. lower upper +cyan_free_0 1.011e+02 9.899e+01 103.21190 +k_cyan_free 4.203e-02 2.695e-02 0.06557 +k_cyan_free_bound 3.396e-02 2.651e-02 0.04350 +k_cyan_bound_free 2.213e-02 1.594e-02 0.03073 +k_JCZ38 8.887e-02 4.941e-02 0.15984 +k_J9Z38 6.251e-03 2.876e-03 0.01359 +k_JSE76 4.396e-02 1.481e-02 0.13054 +f_cyan_free_to_JCZ38 6.590e-01 5.557e-01 0.95365 +f_cyan_free_to_J9Z38 2.429e-01 5.557e-01 0.95365 +f_JCZ38_to_JSE76 9.713e-01 6.065e-01 0.99866 +f_JSE76_to_JCZ38 9.965e-01 6.067e-10 1.00000 + +Estimated Eigenvalues of SFORB model(s): +cyan_b1 cyan_b2 cyan_g +0.08749 0.01063 0.40855 + +Resulting formation fractions: + ff +cyan_free_JCZ38 0.65905 +cyan_free_J9Z38 0.24291 +cyan_free_sink 0.09805 +cyan_free 1.00000 +JCZ38_JSE76 0.97132 +JCZ38_sink 0.02868 +JSE76_JCZ38 0.99650 +JSE76_sink 0.00350 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 +cyan 24.91 167.16 50.32 7.922 65.19 +JCZ38 7.80 25.91 NA NA NA +J9Z38 110.89 368.36 NA NA NA +JSE76 15.77 52.38 NA NA NA + +</code></pre> +<p></p> +</div> +<div class="section level4"> +<h4 id="pathway-2-refined-fits">Pathway 2, refined fits<a class="anchor" aria-label="anchor" href="#pathway-2-refined-fits"></a> +</h4> +<caption> +Hierarchical FOMC path 2 fit with reduced random effects, two-component +error +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 11:18:41 2023 +Date of summary: Sat Jan 28 11:22:29 2023 + +Equations: +d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) * + cyan - k_JCZ38 * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 +d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) * + cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 1957.271 s +Using 300, 100 iterations and 10 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 101.9028 -1.9055 -5.0249 -2.5646 0.6807 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta + 4.8883 16.0676 9.3923 -0.1346 3.0364 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 6.321 0.000 0.000 0.000 0.0000 +log_k_JCZ38 0.000 1.392 0.000 0.000 0.0000 +log_k_J9Z38 0.000 0.000 1.561 0.000 0.0000 +log_k_JSE76 0.000 0.000 0.000 3.614 0.0000 +f_cyan_ilr_1 0.000 0.000 0.000 0.000 0.6339 +f_cyan_ilr_2 0.000 0.000 0.000 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.000 0.000 0.000 0.0000 +f_JSE76_qlogis 0.000 0.000 0.000 0.000 0.0000 +log_alpha 0.000 0.000 0.000 0.000 0.0000 +log_beta 0.000 0.000 0.000 0.000 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta +cyan_0 0.00 0.00 0.00 0.0000 0.0000 +log_k_JCZ38 0.00 0.00 0.00 0.0000 0.0000 +log_k_J9Z38 0.00 0.00 0.00 0.0000 0.0000 +log_k_JSE76 0.00 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_1 0.00 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_2 10.41 0.00 0.00 0.0000 0.0000 +f_JCZ38_qlogis 0.00 12.24 0.00 0.0000 0.0000 +f_JSE76_qlogis 0.00 0.00 15.13 0.0000 0.0000 +log_alpha 0.00 0.00 0.00 0.3701 0.0000 +log_beta 0.00 0.00 0.00 0.0000 0.5662 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2251 2244 -1106 + +Optimised parameters: + est. lower upper +cyan_0 101.05768 NA NA +log_k_JCZ38 -2.73252 NA NA +log_k_J9Z38 -5.07399 NA NA +log_k_JSE76 -3.52863 NA NA +f_cyan_ilr_1 0.72176 NA NA +f_cyan_ilr_2 1.34610 NA NA +f_JCZ38_qlogis 2.08337 NA NA +f_JSE76_qlogis 1590.31880 NA NA +log_alpha -0.09336 NA NA +log_beta 3.10191 NA NA +a.1 2.08557 1.85439 2.31675 +b.1 0.06998 0.05800 0.08197 +SD.log_k_JCZ38 1.20053 0.43329 1.96777 +SD.log_k_J9Z38 0.85854 0.26708 1.45000 +SD.log_k_JSE76 0.62528 0.16061 1.08995 +SD.f_cyan_ilr_1 0.35190 0.12340 0.58039 +SD.f_cyan_ilr_2 0.85385 0.15391 1.55378 +SD.log_alpha 0.28971 0.08718 0.49225 +SD.log_beta 0.31614 0.05938 0.57290 + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_JCZ38 1.2005 0.43329 1.9678 +SD.log_k_J9Z38 0.8585 0.26708 1.4500 +SD.log_k_JSE76 0.6253 0.16061 1.0900 +SD.f_cyan_ilr_1 0.3519 0.12340 0.5804 +SD.f_cyan_ilr_2 0.8538 0.15391 1.5538 +SD.log_alpha 0.2897 0.08718 0.4923 +SD.log_beta 0.3161 0.05938 0.5729 + +Variance model: + est. lower upper +a.1 2.08557 1.854 2.31675 +b.1 0.06998 0.058 0.08197 + +Backtransformed parameters: + est. lower upper +cyan_0 1.011e+02 NA NA +k_JCZ38 6.506e-02 NA NA +k_J9Z38 6.257e-03 NA NA +k_JSE76 2.935e-02 NA NA +f_cyan_to_JCZ38 6.776e-01 NA NA +f_cyan_to_J9Z38 2.442e-01 NA NA +f_JCZ38_to_JSE76 8.893e-01 NA NA +f_JSE76_to_JCZ38 1.000e+00 NA NA +alpha 9.109e-01 NA NA +beta 2.224e+01 NA NA + +Resulting formation fractions: + ff +cyan_JCZ38 0.67761 +cyan_J9Z38 0.24417 +cyan_sink 0.07822 +JCZ38_JSE76 0.88928 +JCZ38_sink 0.11072 +JSE76_JCZ38 1.00000 +JSE76_sink 0.00000 + +Estimated disappearance times: + DT50 DT90 DT50back +cyan 25.36 256.37 77.18 +JCZ38 10.65 35.39 NA +J9Z38 110.77 367.98 NA +JSE76 23.62 78.47 NA + +</code></pre> +<p></p> +<caption> +Hierarchical DFOP path 2 fit with reduced random effects, constant +variance +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 11:16:32 2023 +Date of summary: Sat Jan 28 11:22:29 2023 + +Equations: +d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 + + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 +d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 1828.403 s +Using 300, 100 iterations and 10 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 102.4358 -2.3107 -5.3123 -3.7120 0.6753 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 + 1.1462 12.4095 12.3630 -1.9317 -4.4557 + g_qlogis + -0.5648 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 4.594 0.0000 0.000 0.0 0.0000 +log_k_JCZ38 0.000 0.7966 0.000 0.0 0.0000 +log_k_J9Z38 0.000 0.0000 1.561 0.0 0.0000 +log_k_JSE76 0.000 0.0000 0.000 0.8 0.0000 +f_cyan_ilr_1 0.000 0.0000 0.000 0.0 0.6349 +f_cyan_ilr_2 0.000 0.0000 0.000 0.0 0.0000 +f_JCZ38_qlogis 0.000 0.0000 0.000 0.0 0.0000 +f_JSE76_qlogis 0.000 0.0000 0.000 0.0 0.0000 +log_k1 0.000 0.0000 0.000 0.0 0.0000 +log_k2 0.000 0.0000 0.000 0.0 0.0000 +g_qlogis 0.000 0.0000 0.000 0.0 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 +cyan_0 0.000 0.00 0.0 0.000 0.0000 +log_k_JCZ38 0.000 0.00 0.0 0.000 0.0000 +log_k_J9Z38 0.000 0.00 0.0 0.000 0.0000 +log_k_JSE76 0.000 0.00 0.0 0.000 0.0000 +f_cyan_ilr_1 0.000 0.00 0.0 0.000 0.0000 +f_cyan_ilr_2 1.797 0.00 0.0 0.000 0.0000 +f_JCZ38_qlogis 0.000 13.85 0.0 0.000 0.0000 +f_JSE76_qlogis 0.000 0.00 14.1 0.000 0.0000 +log_k1 0.000 0.00 0.0 1.106 0.0000 +log_k2 0.000 0.00 0.0 0.000 0.6141 +g_qlogis 0.000 0.00 0.0 0.000 0.0000 + g_qlogis +cyan_0 0.000 +log_k_JCZ38 0.000 +log_k_J9Z38 0.000 +log_k_JSE76 0.000 +f_cyan_ilr_1 0.000 +f_cyan_ilr_2 0.000 +f_JCZ38_qlogis 0.000 +f_JSE76_qlogis 0.000 +log_k1 0.000 +log_k2 0.000 +g_qlogis 1.595 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2282 2274 -1121 + +Optimised parameters: + est. lower upper +cyan_0 102.5254 NA NA +log_k_JCZ38 -2.9358 NA NA +log_k_J9Z38 -5.1424 NA NA +log_k_JSE76 -3.6458 NA NA +f_cyan_ilr_1 0.6957 NA NA +f_cyan_ilr_2 0.6635 NA NA +f_JCZ38_qlogis 4984.8163 NA NA +f_JSE76_qlogis 1.9415 NA NA +log_k1 -1.9456 NA NA +log_k2 -4.4705 NA NA +g_qlogis -0.5117 NA NA +a.1 2.7455 2.55392 2.9370 +SD.log_k_JCZ38 1.3163 0.47635 2.1563 +SD.log_k_J9Z38 0.7162 0.16133 1.2711 +SD.log_k_JSE76 0.6457 0.15249 1.1390 +SD.f_cyan_ilr_1 0.3424 0.11714 0.5677 +SD.f_cyan_ilr_2 0.4524 0.09709 0.8077 +SD.log_k1 0.7353 0.25445 1.2161 +SD.log_k2 0.5137 0.18206 0.8453 +SD.g_qlogis 0.9857 0.35651 1.6148 + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_JCZ38 1.3163 0.47635 2.1563 +SD.log_k_J9Z38 0.7162 0.16133 1.2711 +SD.log_k_JSE76 0.6457 0.15249 1.1390 +SD.f_cyan_ilr_1 0.3424 0.11714 0.5677 +SD.f_cyan_ilr_2 0.4524 0.09709 0.8077 +SD.log_k1 0.7353 0.25445 1.2161 +SD.log_k2 0.5137 0.18206 0.8453 +SD.g_qlogis 0.9857 0.35651 1.6148 + +Variance model: + est. lower upper +a.1 2.745 2.554 2.937 + +Backtransformed parameters: + est. lower upper +cyan_0 1.025e+02 NA NA +k_JCZ38 5.309e-02 NA NA +k_J9Z38 5.844e-03 NA NA +k_JSE76 2.610e-02 NA NA +f_cyan_to_JCZ38 6.079e-01 NA NA +f_cyan_to_J9Z38 2.272e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA +f_JSE76_to_JCZ38 8.745e-01 NA NA +k1 1.429e-01 NA NA +k2 1.144e-02 NA NA +g 3.748e-01 NA NA + +Resulting formation fractions: + ff +cyan_JCZ38 0.6079 +cyan_J9Z38 0.2272 +cyan_sink 0.1649 +JCZ38_JSE76 1.0000 +JCZ38_sink 0.0000 +JSE76_JCZ38 0.8745 +JSE76_sink 0.1255 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +cyan 22.29 160.20 48.22 4.85 60.58 +JCZ38 13.06 43.37 NA NA NA +J9Z38 118.61 394.02 NA NA NA +JSE76 26.56 88.22 NA NA NA + +</code></pre> +<p></p> +<caption> +Hierarchical DFOP path 2 fit with reduced random effects, two-component +error +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 11:22:28 2023 +Date of summary: Sat Jan 28 11:22:29 2023 + +Equations: +d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 + + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 +d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 2183.989 s +Using 300, 100 iterations and 10 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 101.7523 -1.5948 -5.0119 -2.2723 0.6719 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 + 5.1681 12.8238 12.4130 -2.0057 -4.5526 + g_qlogis + -0.5805 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 5.627 0.000 0.000 0.000 0.0000 +log_k_JCZ38 0.000 2.327 0.000 0.000 0.0000 +log_k_J9Z38 0.000 0.000 1.664 0.000 0.0000 +log_k_JSE76 0.000 0.000 0.000 4.566 0.0000 +f_cyan_ilr_1 0.000 0.000 0.000 0.000 0.6519 +f_cyan_ilr_2 0.000 0.000 0.000 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.000 0.000 0.000 0.0000 +f_JSE76_qlogis 0.000 0.000 0.000 0.000 0.0000 +log_k1 0.000 0.000 0.000 0.000 0.0000 +log_k2 0.000 0.000 0.000 0.000 0.0000 +g_qlogis 0.000 0.000 0.000 0.000 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 +cyan_0 0.0 0.00 0.00 0.0000 0.0000 +log_k_JCZ38 0.0 0.00 0.00 0.0000 0.0000 +log_k_J9Z38 0.0 0.00 0.00 0.0000 0.0000 +log_k_JSE76 0.0 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_1 0.0 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_2 10.1 0.00 0.00 0.0000 0.0000 +f_JCZ38_qlogis 0.0 13.99 0.00 0.0000 0.0000 +f_JSE76_qlogis 0.0 0.00 14.15 0.0000 0.0000 +log_k1 0.0 0.00 0.00 0.8452 0.0000 +log_k2 0.0 0.00 0.00 0.0000 0.5968 +g_qlogis 0.0 0.00 0.00 0.0000 0.0000 + g_qlogis +cyan_0 0.000 +log_k_JCZ38 0.000 +log_k_J9Z38 0.000 +log_k_JSE76 0.000 +f_cyan_ilr_1 0.000 +f_cyan_ilr_2 0.000 +f_JCZ38_qlogis 0.000 +f_JSE76_qlogis 0.000 +log_k1 0.000 +log_k2 0.000 +g_qlogis 1.691 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2232 2224 -1096 + +Optimised parameters: + est. lower upper +cyan_0 101.20051 NA NA +log_k_JCZ38 -2.93542 NA NA +log_k_J9Z38 -5.03151 NA NA +log_k_JSE76 -3.67679 NA NA +f_cyan_ilr_1 0.67290 NA NA +f_cyan_ilr_2 0.99787 NA NA +f_JCZ38_qlogis 348.32484 NA NA +f_JSE76_qlogis 1.87846 NA NA +log_k1 -2.32738 NA NA +log_k2 -4.61295 NA NA +g_qlogis -0.38342 NA NA +a.1 2.06184 1.83746 2.28622 +b.1 0.06329 0.05211 0.07447 +SD.log_k_JCZ38 1.29042 0.47468 2.10617 +SD.log_k_J9Z38 0.84235 0.25903 1.42566 +SD.log_k_JSE76 0.56930 0.13934 0.99926 +SD.f_cyan_ilr_1 0.35183 0.12298 0.58068 +SD.f_cyan_ilr_2 0.77269 0.17908 1.36631 +SD.log_k2 0.28549 0.09210 0.47888 +SD.g_qlogis 0.93830 0.34568 1.53093 + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_JCZ38 1.2904 0.4747 2.1062 +SD.log_k_J9Z38 0.8423 0.2590 1.4257 +SD.log_k_JSE76 0.5693 0.1393 0.9993 +SD.f_cyan_ilr_1 0.3518 0.1230 0.5807 +SD.f_cyan_ilr_2 0.7727 0.1791 1.3663 +SD.log_k2 0.2855 0.0921 0.4789 +SD.g_qlogis 0.9383 0.3457 1.5309 + +Variance model: + est. lower upper +a.1 2.06184 1.83746 2.28622 +b.1 0.06329 0.05211 0.07447 + +Backtransformed parameters: + est. lower upper +cyan_0 1.012e+02 NA NA +k_JCZ38 5.311e-02 NA NA +k_J9Z38 6.529e-03 NA NA +k_JSE76 2.530e-02 NA NA +f_cyan_to_JCZ38 6.373e-01 NA NA +f_cyan_to_J9Z38 2.461e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA +f_JSE76_to_JCZ38 8.674e-01 NA NA +k1 9.755e-02 NA NA +k2 9.922e-03 NA NA +g 4.053e-01 NA NA + +Resulting formation fractions: + ff +cyan_JCZ38 0.6373 +cyan_J9Z38 0.2461 +cyan_sink 0.1167 +JCZ38_JSE76 1.0000 +JCZ38_sink 0.0000 +JSE76_JCZ38 0.8674 +JSE76_sink 0.1326 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +cyan 24.93 179.68 54.09 7.105 69.86 +JCZ38 13.05 43.36 NA NA NA +J9Z38 106.16 352.67 NA NA NA +JSE76 27.39 91.00 NA NA NA + +</code></pre> +<p></p> +<caption> +Hierarchical SFORB path 2 fit with reduced random effects, constant +variance +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 11:17:37 2023 +Date of summary: Sat Jan 28 11:22:29 2023 + +Equations: +d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound * + cyan_free + k_cyan_bound_free * cyan_bound +d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free * + cyan_bound +d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38 + * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 +d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38 + * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 1893.29 s +Using 300, 100 iterations and 10 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound + 102.4394 -2.7673 -2.8942 +log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 + -3.6201 -2.3107 -5.3123 + log_k_JSE76 f_cyan_ilr_1 f_cyan_ilr_2 + -3.7120 0.6754 1.1448 + f_JCZ38_qlogis f_JSE76_qlogis + 13.2672 13.3538 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound +cyan_free_0 4.589 0.0000 0.00 +log_k_cyan_free 0.000 0.4849 0.00 +log_k_cyan_free_bound 0.000 0.0000 1.62 +log_k_cyan_bound_free 0.000 0.0000 0.00 +log_k_JCZ38 0.000 0.0000 0.00 +log_k_J9Z38 0.000 0.0000 0.00 +log_k_JSE76 0.000 0.0000 0.00 +f_cyan_ilr_1 0.000 0.0000 0.00 +f_cyan_ilr_2 0.000 0.0000 0.00 +f_JCZ38_qlogis 0.000 0.0000 0.00 +f_JSE76_qlogis 0.000 0.0000 0.00 + log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 +cyan_free_0 0.000 0.0000 0.000 0.0 +log_k_cyan_free 0.000 0.0000 0.000 0.0 +log_k_cyan_free_bound 0.000 0.0000 0.000 0.0 +log_k_cyan_bound_free 1.197 0.0000 0.000 0.0 +log_k_JCZ38 0.000 0.7966 0.000 0.0 +log_k_J9Z38 0.000 0.0000 1.561 0.0 +log_k_JSE76 0.000 0.0000 0.000 0.8 +f_cyan_ilr_1 0.000 0.0000 0.000 0.0 +f_cyan_ilr_2 0.000 0.0000 0.000 0.0 +f_JCZ38_qlogis 0.000 0.0000 0.000 0.0 +f_JSE76_qlogis 0.000 0.0000 0.000 0.0 + f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis +cyan_free_0 0.0000 0.000 0.00 0.00 +log_k_cyan_free 0.0000 0.000 0.00 0.00 +log_k_cyan_free_bound 0.0000 0.000 0.00 0.00 +log_k_cyan_bound_free 0.0000 0.000 0.00 0.00 +log_k_JCZ38 0.0000 0.000 0.00 0.00 +log_k_J9Z38 0.0000 0.000 0.00 0.00 +log_k_JSE76 0.0000 0.000 0.00 0.00 +f_cyan_ilr_1 0.6349 0.000 0.00 0.00 +f_cyan_ilr_2 0.0000 1.797 0.00 0.00 +f_JCZ38_qlogis 0.0000 0.000 13.84 0.00 +f_JSE76_qlogis 0.0000 0.000 0.00 14.66 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2279 2272 -1120 + +Optimised parameters: + est. lower upper +cyan_free_0 102.5621 NA NA +log_k_cyan_free -2.8531 NA NA +log_k_cyan_free_bound -2.6916 NA NA +log_k_cyan_bound_free -3.5032 NA NA +log_k_JCZ38 -2.9436 NA NA +log_k_J9Z38 -5.1140 NA NA +log_k_JSE76 -3.6472 NA NA +f_cyan_ilr_1 0.6887 NA NA +f_cyan_ilr_2 0.6874 NA NA +f_JCZ38_qlogis 4063.6389 NA NA +f_JSE76_qlogis 1.9556 NA NA +a.1 2.7460 2.55451 2.9376 +SD.log_k_cyan_free 0.3131 0.09841 0.5277 +SD.log_k_cyan_free_bound 0.8850 0.29909 1.4710 +SD.log_k_cyan_bound_free 0.6167 0.20391 1.0295 +SD.log_k_JCZ38 1.3555 0.49101 2.2200 +SD.log_k_J9Z38 0.7200 0.16166 1.2783 +SD.log_k_JSE76 0.6252 0.14619 1.1042 +SD.f_cyan_ilr_1 0.3386 0.11447 0.5627 +SD.f_cyan_ilr_2 0.4699 0.09810 0.8417 + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_cyan_free 0.3131 0.09841 0.5277 +SD.log_k_cyan_free_bound 0.8850 0.29909 1.4710 +SD.log_k_cyan_bound_free 0.6167 0.20391 1.0295 +SD.log_k_JCZ38 1.3555 0.49101 2.2200 +SD.log_k_J9Z38 0.7200 0.16166 1.2783 +SD.log_k_JSE76 0.6252 0.14619 1.1042 +SD.f_cyan_ilr_1 0.3386 0.11447 0.5627 +SD.f_cyan_ilr_2 0.4699 0.09810 0.8417 + +Variance model: + est. lower upper +a.1 2.746 2.555 2.938 + +Backtransformed parameters: + est. lower upper +cyan_free_0 1.026e+02 NA NA +k_cyan_free 5.767e-02 NA NA +k_cyan_free_bound 6.777e-02 NA NA +k_cyan_bound_free 3.010e-02 NA NA +k_JCZ38 5.267e-02 NA NA +k_J9Z38 6.012e-03 NA NA +k_JSE76 2.606e-02 NA NA +f_cyan_free_to_JCZ38 6.089e-01 NA NA +f_cyan_free_to_J9Z38 2.299e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA +f_JSE76_to_JCZ38 8.761e-01 NA NA + +Estimated Eigenvalues of SFORB model(s): +cyan_b1 cyan_b2 cyan_g + 0.1434 0.0121 0.3469 + +Resulting formation fractions: + ff +cyan_free_JCZ38 0.6089 +cyan_free_J9Z38 0.2299 +cyan_free_sink 0.1612 +cyan_free 1.0000 +JCZ38_JSE76 1.0000 +JCZ38_sink 0.0000 +JSE76_JCZ38 0.8761 +JSE76_sink 0.1239 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 +cyan 23.94 155.06 46.68 4.832 57.28 +JCZ38 13.16 43.71 NA NA NA +J9Z38 115.30 383.02 NA NA NA +JSE76 26.59 88.35 NA NA NA + +</code></pre> +<p></p> +<caption> +Hierarchical SFORB path 2 fit with reduced random effects, two-component +error +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 11:21:01 2023 +Date of summary: Sat Jan 28 11:22:29 2023 + +Equations: +d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound * + cyan_free + k_cyan_bound_free * cyan_bound +d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free * + cyan_bound +d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38 + * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 +d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38 + * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 2097.842 s +Using 300, 100 iterations and 10 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound + 101.751 -2.837 -3.016 +log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 + -3.660 -2.299 -5.313 + log_k_JSE76 f_cyan_ilr_1 f_cyan_ilr_2 + -3.699 0.672 5.873 + f_JCZ38_qlogis f_JSE76_qlogis + 13.216 13.338 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound +cyan_free_0 5.629 0.000 0.000 +log_k_cyan_free 0.000 0.446 0.000 +log_k_cyan_free_bound 0.000 0.000 1.449 +log_k_cyan_bound_free 0.000 0.000 0.000 +log_k_JCZ38 0.000 0.000 0.000 +log_k_J9Z38 0.000 0.000 0.000 +log_k_JSE76 0.000 0.000 0.000 +f_cyan_ilr_1 0.000 0.000 0.000 +f_cyan_ilr_2 0.000 0.000 0.000 +f_JCZ38_qlogis 0.000 0.000 0.000 +f_JSE76_qlogis 0.000 0.000 0.000 + log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 +cyan_free_0 0.000 0.0000 0.000 0.0000 +log_k_cyan_free 0.000 0.0000 0.000 0.0000 +log_k_cyan_free_bound 0.000 0.0000 0.000 0.0000 +log_k_cyan_bound_free 1.213 0.0000 0.000 0.0000 +log_k_JCZ38 0.000 0.7801 0.000 0.0000 +log_k_J9Z38 0.000 0.0000 1.575 0.0000 +log_k_JSE76 0.000 0.0000 0.000 0.8078 +f_cyan_ilr_1 0.000 0.0000 0.000 0.0000 +f_cyan_ilr_2 0.000 0.0000 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.0000 0.000 0.0000 +f_JSE76_qlogis 0.000 0.0000 0.000 0.0000 + f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis +cyan_free_0 0.0000 0.00 0.00 0.00 +log_k_cyan_free 0.0000 0.00 0.00 0.00 +log_k_cyan_free_bound 0.0000 0.00 0.00 0.00 +log_k_cyan_bound_free 0.0000 0.00 0.00 0.00 +log_k_JCZ38 0.0000 0.00 0.00 0.00 +log_k_J9Z38 0.0000 0.00 0.00 0.00 +log_k_JSE76 0.0000 0.00 0.00 0.00 +f_cyan_ilr_1 0.6519 0.00 0.00 0.00 +f_cyan_ilr_2 0.0000 10.78 0.00 0.00 +f_JCZ38_qlogis 0.0000 0.00 13.96 0.00 +f_JSE76_qlogis 0.0000 0.00 0.00 14.69 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2236 2228 -1098 + +Optimised parameters: + est. lower upper +cyan_free_0 100.72760 NA NA +log_k_cyan_free -3.18281 NA NA +log_k_cyan_free_bound -3.37924 NA NA +log_k_cyan_bound_free -3.77107 NA NA +log_k_JCZ38 -2.92811 NA NA +log_k_J9Z38 -5.02759 NA NA +log_k_JSE76 -3.65835 NA NA +f_cyan_ilr_1 0.67390 NA NA +f_cyan_ilr_2 1.15106 NA NA +f_JCZ38_qlogis 827.82299 NA NA +f_JSE76_qlogis 1.83064 NA NA +a.1 2.06921 1.84443 2.29399 +b.1 0.06391 0.05267 0.07515 +SD.log_k_cyan_free 0.50518 0.18962 0.82075 +SD.log_k_cyan_bound_free 0.30991 0.08170 0.53813 +SD.log_k_JCZ38 1.26661 0.46578 2.06744 +SD.log_k_J9Z38 0.88272 0.27813 1.48730 +SD.log_k_JSE76 0.53050 0.12561 0.93538 +SD.f_cyan_ilr_1 0.35547 0.12461 0.58633 +SD.f_cyan_ilr_2 0.91446 0.20131 1.62761 + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_cyan_free 0.5052 0.1896 0.8207 +SD.log_k_cyan_bound_free 0.3099 0.0817 0.5381 +SD.log_k_JCZ38 1.2666 0.4658 2.0674 +SD.log_k_J9Z38 0.8827 0.2781 1.4873 +SD.log_k_JSE76 0.5305 0.1256 0.9354 +SD.f_cyan_ilr_1 0.3555 0.1246 0.5863 +SD.f_cyan_ilr_2 0.9145 0.2013 1.6276 + +Variance model: + est. lower upper +a.1 2.06921 1.84443 2.29399 +b.1 0.06391 0.05267 0.07515 + +Backtransformed parameters: + est. lower upper +cyan_free_0 1.007e+02 NA NA +k_cyan_free 4.147e-02 NA NA +k_cyan_free_bound 3.407e-02 NA NA +k_cyan_bound_free 2.303e-02 NA NA +k_JCZ38 5.350e-02 NA NA +k_J9Z38 6.555e-03 NA NA +k_JSE76 2.578e-02 NA NA +f_cyan_free_to_JCZ38 6.505e-01 NA NA +f_cyan_free_to_J9Z38 2.508e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA +f_JSE76_to_JCZ38 8.618e-01 NA NA + +Estimated Eigenvalues of SFORB model(s): +cyan_b1 cyan_b2 cyan_g +0.08768 0.01089 0.39821 + +Resulting formation fractions: + ff +cyan_free_JCZ38 0.65053 +cyan_free_J9Z38 0.25082 +cyan_free_sink 0.09864 +cyan_free 1.00000 +JCZ38_JSE76 1.00000 +JCZ38_sink 0.00000 +JSE76_JCZ38 0.86184 +JSE76_sink 0.13816 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 +cyan 25.32 164.79 49.61 7.906 63.64 +JCZ38 12.96 43.04 NA NA NA +J9Z38 105.75 351.29 NA NA NA +JSE76 26.89 89.33 NA NA NA + +</code></pre> +<p></p> +</div> +</div> +<div class="section level3"> +<h3 id="session-info">Session info<a class="anchor" aria-label="anchor" href="#session-info"></a> +</h3> +<pre><code>R version 4.2.2 Patched (2022-11-10 r83330) +Platform: x86_64-pc-linux-gnu (64-bit) +Running under: Debian GNU/Linux bookworm/sid + +Matrix products: default +BLAS: /usr/lib/x86_64-linux-gnu/openblas-serial/libblas.so.3 +LAPACK: /usr/lib/x86_64-linux-gnu/openblas-serial/libopenblas-r0.3.21.so + +locale: + [1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C + [3] LC_TIME=de_DE.UTF-8 LC_COLLATE=de_DE.UTF-8 + [5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=de_DE.UTF-8 + [7] LC_PAPER=de_DE.UTF-8 LC_NAME=C + [9] LC_ADDRESS=C LC_TELEPHONE=C +[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C + +attached base packages: +[1] parallel stats graphics grDevices utils datasets methods +[8] base + +other attached packages: +[1] saemix_3.2 npde_3.3 knitr_1.41 mkin_1.2.2 + +loaded via a namespace (and not attached): + [1] mclust_6.0.0 lattice_0.20-45 prettyunits_1.1.1 ps_1.7.2 + [5] zoo_1.8-11 assertthat_0.2.1 rprojroot_2.0.3 digest_0.6.31 + [9] lmtest_0.9-40 utf8_1.2.2 R6_2.5.1 cellranger_1.1.0 +[13] evaluate_0.19 ggplot2_3.4.0 highr_0.9 pillar_1.8.1 +[17] rlang_1.0.6 readxl_1.4.1 callr_3.7.3 jquerylib_0.1.4 +[21] rmarkdown_2.19 pkgdown_2.0.7 textshaping_0.3.6 desc_1.4.2 +[25] stringr_1.5.0 munsell_0.5.0 compiler_4.2.2 xfun_0.35 +[29] pkgconfig_2.0.3 systemfonts_1.0.4 pkgbuild_1.4.0 htmltools_0.5.4 +[33] tidyselect_1.2.0 tibble_3.1.8 gridExtra_2.3 codetools_0.2-18 +[37] fansi_1.0.3 crayon_1.5.2 dplyr_1.0.10 grid_4.2.2 +[41] nlme_3.1-161 jsonlite_1.8.4 gtable_0.3.1 lifecycle_1.0.3 +[45] DBI_1.1.3 magrittr_2.0.3 scales_1.2.1 cli_3.5.0 +[49] stringi_1.7.8 cachem_1.0.6 fs_1.5.2 bslib_0.4.2 +[53] ragg_1.2.4 generics_0.1.3 vctrs_0.5.1 deSolve_1.34 +[57] tools_4.2.2 glue_1.6.2 purrr_1.0.0 processx_3.8.0 +[61] fastmap_1.1.0 yaml_2.3.6 inline_0.3.19 colorspace_2.0-3 +[65] memoise_2.0.1 sass_0.4.4 </code></pre> +</div> +<div class="section level3"> +<h3 id="hardware-info">Hardware info<a class="anchor" aria-label="anchor" href="#hardware-info"></a> +</h3> +<pre><code>CPU model: AMD Ryzen 9 7950X 16-Core Processor</code></pre> +<pre><code>MemTotal: 64940452 kB</code></pre> +</div> +</div> + </div> + + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + + <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2> + </nav> +</div> + +</div> + + + + <footer><div class="copyright"> + <p></p> +<p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> +</div> + + </footer> +</div> + + + + + + + </body> +</html> diff --git a/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-11-1.png b/docs/dev/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-11-1.png Binary files differnew file mode 100644 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class="col-md-9 contents"> + <div class="page-header toc-ignore"> + <h1 data-toc-skip>Testing hierarchical parent degradation kinetics +with residue data on dimethenamid and dimethenamid-P</h1> + <h4 data-toc-skip class="author">Johannes +Ranke</h4> + + <h4 data-toc-skip class="date">Last change on 5 January +2023, last compiled on 28 Januar 2023</h4> + + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/prebuilt/2022_dmta_parent.rmd" class="external-link"><code>vignettes/prebuilt/2022_dmta_parent.rmd</code></a></small> + <div class="hidden name"><code>2022_dmta_parent.rmd</code></div> + + </div> + + + +<div class="section level2"> +<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a> +</h2> +<p>The purpose of this document is to demonstrate how nonlinear +hierarchical models (NLHM) based on the parent degradation models SFO, +FOMC, DFOP and HS can be fitted with the mkin package.</p> +<p>It was assembled in the course of work package 1.1 of Project Number +173340 (Application of nonlinear hierarchical models to the kinetic +evaluation of chemical degradation data) of the German Environment +Agency carried out in 2022 and 2023.</p> +<p>The mkin package is used in version 1.2.2. It contains the test data +and the functions used in the evaluations. The <code>saemix</code> +package is used as a backend for fitting the NLHM, but is also loaded to +make the convergence plot function available.</p> +<p>This document is processed with the <code>knitr</code> package, which +also provides the <code>kable</code> function that is used to improve +the display of tabular data in R markdown documents. For parallel +processing, the <code>parallel</code> package is used.</p> +<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span> +<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://yihui.org/knitr/" class="external-link">knitr</a></span><span class="op">)</span></span> +<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span> +<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">parallel</span><span class="op">)</span></span> +<span><span class="va">n_cores</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span></span> +<span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="op">{</span></span> +<span> <span class="va">cl</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span> +<span><span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span> +<span> <span class="va">cl</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makeForkCluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span> +<span><span class="op">}</span></span></code></pre></div> +</div> +<div class="section level2"> +<h2 id="data">Data<a class="anchor" aria-label="anchor" href="#data"></a> +</h2> +<p>The test data are available in the mkin package as an object of class +<code>mkindsg</code> (mkin dataset group) under the identifier +<code>dimethenamid_2018</code>. The following preprocessing steps are +still necessary:</p> +<ul> +<li>The data available for the enantiomer dimethenamid-P (DMTAP) are +renamed to have the same substance name as the data for the racemic +mixture dimethenamid (DMTA). The reason for this is that no difference +between their degradation behaviour was identified in the EU risk +assessment.</li> +<li>The data for transformation products and unnecessary columns are +discarded</li> +<li>The observation times of each dataset are multiplied with the +corresponding normalisation factor also available in the dataset, in +order to make it possible to describe all datasets with a single set of +parameters that are independent of temperature</li> +<li>Finally, datasets observed in the same soil (<code>Elliot 1</code> +and <code>Elliot 2</code>) are combined, resulting in dimethenamid +(DMTA) data from six soils.</li> +</ul> +<p>The following commented R code performs this preprocessing.</p> +<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="co"># Apply a function to each of the seven datasets in the mkindsg object to create a list</span></span> +<span><span class="va">dmta_ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span> +<span> <span class="va">ds_i</span> <span class="op"><-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span> <span class="co"># Get a dataset</span></span> +<span> <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op"><-</span> <span class="st">"DMTA"</span> <span class="co"># Rename DMTAP to DMTA</span></span> +<span> <span class="va">ds_i</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">ds_i</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"DMTA"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">)</span> <span class="co"># Select data</span></span> +<span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op"><-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span> <span class="co"># Normalise time</span></span> +<span> <span class="va">ds_i</span> <span class="co"># Return the dataset</span></span> +<span><span class="op">}</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># Use dataset titles as names for the list elements</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># Combine data for Elliot soil to obtain a named list with six elements</span></span> +<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span> <span class="co">#</span></span> +<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span></span> +<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span></span></code></pre></div> +<p>The following tables show the 6 datasets.</p> +<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="kw">for</span> <span class="op">(</span><span class="va">ds_name</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span> +<span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mkin_long_to_wide.html">mkin_long_to_wide</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="va">ds_name</span><span class="op">]</span><span class="op">]</span><span class="op">)</span>,</span> +<span> caption <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="va">ds_name</span><span class="op">)</span>,</span> +<span> label <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="st">"tab:"</span>, <span class="va">ds_name</span><span class="op">)</span>, booktabs <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span> +<span> <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"\n\\clearpage\n"</span><span class="op">)</span></span> +<span><span class="op">}</span></span></code></pre></div> +<table class="table"> +<caption>Dataset Calke</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0</td> +<td align="right">95.8</td> +</tr> +<tr class="even"> +<td align="right">0</td> +<td align="right">98.7</td> +</tr> +<tr class="odd"> +<td align="right">14</td> +<td align="right">60.5</td> +</tr> +<tr class="even"> +<td align="right">30</td> +<td align="right">39.1</td> +</tr> +<tr class="odd"> +<td align="right">59</td> +<td align="right">15.2</td> +</tr> +<tr class="even"> +<td align="right">120</td> +<td align="right">4.8</td> +</tr> +<tr class="odd"> +<td align="right">120</td> +<td align="right">4.6</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Borstel</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">100.5</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">99.6</td> +</tr> +<tr class="odd"> +<td align="right">1.941295</td> +<td align="right">91.9</td> +</tr> +<tr class="even"> +<td align="right">1.941295</td> +<td align="right">91.3</td> +</tr> +<tr class="odd"> +<td align="right">6.794534</td> +<td align="right">81.8</td> +</tr> +<tr class="even"> +<td align="right">6.794534</td> +<td align="right">82.1</td> +</tr> +<tr class="odd"> +<td align="right">13.589067</td> +<td align="right">69.1</td> +</tr> +<tr class="even"> +<td align="right">13.589067</td> +<td align="right">68.0</td> +</tr> +<tr class="odd"> +<td align="right">27.178135</td> +<td align="right">51.4</td> +</tr> +<tr class="even"> +<td align="right">27.178135</td> +<td align="right">51.4</td> +</tr> +<tr class="odd"> +<td align="right">56.297565</td> +<td align="right">27.6</td> +</tr> +<tr class="even"> +<td align="right">56.297565</td> +<td align="right">26.8</td> +</tr> +<tr class="odd"> +<td align="right">86.387643</td> +<td align="right">15.7</td> +</tr> +<tr class="even"> +<td align="right">86.387643</td> +<td align="right">15.3</td> +</tr> +<tr class="odd"> +<td align="right">115.507073</td> +<td align="right">7.9</td> +</tr> +<tr class="even"> +<td align="right">115.507073</td> +<td align="right">8.1</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Flaach</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">96.5</td> +</tr> +<tr class="even"> +<td align="right">0.0000000</td> +<td align="right">96.8</td> +</tr> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">97.0</td> +</tr> +<tr class="even"> +<td align="right">0.6233856</td> +<td align="right">82.9</td> +</tr> +<tr class="odd"> +<td align="right">0.6233856</td> +<td align="right">86.7</td> +</tr> +<tr class="even"> +<td align="right">0.6233856</td> +<td align="right">87.4</td> +</tr> +<tr class="odd"> +<td align="right">1.8701567</td> +<td align="right">72.8</td> +</tr> +<tr class="even"> +<td align="right">1.8701567</td> +<td align="right">69.9</td> +</tr> +<tr class="odd"> +<td align="right">1.8701567</td> +<td align="right">71.9</td> +</tr> +<tr class="even"> +<td align="right">4.3636989</td> +<td align="right">51.4</td> +</tr> +<tr class="odd"> +<td align="right">4.3636989</td> +<td align="right">52.9</td> +</tr> +<tr class="even"> +<td align="right">4.3636989</td> +<td align="right">48.6</td> +</tr> +<tr class="odd"> +<td align="right">8.7273979</td> +<td align="right">28.5</td> +</tr> +<tr class="even"> +<td align="right">8.7273979</td> +<td align="right">27.3</td> +</tr> +<tr class="odd"> +<td align="right">8.7273979</td> +<td align="right">27.5</td> +</tr> +<tr class="even"> +<td align="right">13.0910968</td> +<td align="right">14.8</td> +</tr> +<tr class="odd"> +<td align="right">13.0910968</td> +<td align="right">13.4</td> +</tr> +<tr class="even"> +<td align="right">13.0910968</td> +<td align="right">14.4</td> +</tr> +<tr class="odd"> +<td align="right">17.4547957</td> +<td align="right">7.7</td> +</tr> +<tr class="even"> +<td align="right">17.4547957</td> +<td align="right">7.3</td> +</tr> +<tr class="odd"> +<td align="right">17.4547957</td> +<td align="right">8.1</td> +</tr> +<tr class="even"> +<td align="right">26.1821936</td> +<td align="right">2.0</td> +</tr> +<tr class="odd"> +<td align="right">26.1821936</td> +<td align="right">1.5</td> +</tr> +<tr class="even"> +<td align="right">26.1821936</td> +<td align="right">1.9</td> +</tr> +<tr class="odd"> +<td align="right">34.9095915</td> +<td align="right">1.3</td> +</tr> +<tr class="even"> +<td align="right">34.9095915</td> +<td align="right">1.0</td> +</tr> +<tr class="odd"> +<td align="right">34.9095915</td> +<td align="right">1.1</td> +</tr> +<tr class="even"> +<td align="right">43.6369893</td> +<td align="right">0.9</td> +</tr> +<tr class="odd"> +<td align="right">43.6369893</td> +<td align="right">0.7</td> +</tr> +<tr class="even"> +<td align="right">43.6369893</td> +<td align="right">0.7</td> +</tr> +<tr class="odd"> +<td align="right">52.3643872</td> +<td align="right">0.6</td> +</tr> +<tr class="even"> +<td align="right">52.3643872</td> +<td align="right">0.4</td> +</tr> +<tr class="odd"> +<td align="right">52.3643872</td> +<td align="right">0.5</td> +</tr> +<tr class="even"> +<td align="right">74.8062674</td> +<td align="right">0.4</td> +</tr> +<tr class="odd"> +<td align="right">74.8062674</td> +<td align="right">0.3</td> +</tr> +<tr class="even"> +<td align="right">74.8062674</td> +<td align="right">0.3</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset BBA 2.2</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">98.09</td> +</tr> +<tr class="even"> +<td align="right">0.0000000</td> +<td align="right">98.77</td> +</tr> +<tr class="odd"> +<td align="right">0.7678922</td> +<td align="right">93.52</td> +</tr> +<tr class="even"> +<td align="right">0.7678922</td> +<td align="right">92.03</td> +</tr> +<tr class="odd"> +<td align="right">2.3036765</td> +<td align="right">88.39</td> +</tr> +<tr class="even"> +<td align="right">2.3036765</td> +<td align="right">87.18</td> +</tr> +<tr class="odd"> +<td align="right">5.3752452</td> +<td align="right">69.38</td> +</tr> +<tr class="even"> +<td align="right">5.3752452</td> +<td align="right">71.06</td> +</tr> +<tr class="odd"> +<td align="right">10.7504904</td> +<td align="right">45.21</td> +</tr> +<tr class="even"> +<td align="right">10.7504904</td> +<td align="right">46.81</td> +</tr> +<tr class="odd"> +<td align="right">16.1257355</td> +<td align="right">30.54</td> +</tr> +<tr class="even"> +<td align="right">16.1257355</td> +<td align="right">30.07</td> +</tr> +<tr class="odd"> +<td align="right">21.5009807</td> +<td align="right">21.60</td> +</tr> +<tr class="even"> +<td align="right">21.5009807</td> +<td align="right">20.41</td> +</tr> +<tr class="odd"> +<td align="right">32.2514711</td> +<td align="right">9.10</td> +</tr> +<tr class="even"> +<td align="right">32.2514711</td> +<td align="right">9.70</td> +</tr> +<tr class="odd"> +<td align="right">43.0019614</td> +<td align="right">6.58</td> +</tr> +<tr class="even"> +<td align="right">43.0019614</td> +<td align="right">6.31</td> +</tr> +<tr class="odd"> +<td align="right">53.7524518</td> +<td align="right">3.47</td> +</tr> +<tr class="even"> +<td align="right">53.7524518</td> +<td align="right">3.52</td> +</tr> +<tr class="odd"> +<td align="right">64.5029421</td> +<td align="right">3.40</td> +</tr> +<tr class="even"> +<td align="right">64.5029421</td> +<td align="right">3.67</td> +</tr> +<tr class="odd"> +<td align="right">91.3791680</td> +<td align="right">1.62</td> +</tr> +<tr class="even"> +<td align="right">91.3791680</td> +<td align="right">1.62</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset BBA 2.3</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">99.33</td> +</tr> +<tr class="even"> +<td align="right">0.0000000</td> +<td align="right">97.44</td> +</tr> +<tr class="odd"> +<td align="right">0.6733938</td> +<td align="right">93.73</td> +</tr> +<tr class="even"> +<td align="right">0.6733938</td> +<td align="right">93.77</td> +</tr> +<tr class="odd"> +<td align="right">2.0201814</td> +<td align="right">87.84</td> +</tr> +<tr class="even"> +<td align="right">2.0201814</td> +<td align="right">89.82</td> +</tr> +<tr class="odd"> +<td align="right">4.7137565</td> +<td align="right">71.61</td> +</tr> +<tr class="even"> +<td align="right">4.7137565</td> +<td align="right">71.42</td> +</tr> +<tr class="odd"> +<td align="right">9.4275131</td> +<td align="right">45.60</td> +</tr> +<tr class="even"> +<td align="right">9.4275131</td> +<td align="right">45.42</td> +</tr> +<tr class="odd"> +<td align="right">14.1412696</td> +<td align="right">31.12</td> +</tr> +<tr class="even"> +<td align="right">14.1412696</td> +<td align="right">31.68</td> +</tr> +<tr class="odd"> +<td align="right">18.8550262</td> +<td align="right">23.20</td> +</tr> +<tr class="even"> +<td align="right">18.8550262</td> +<td align="right">24.13</td> +</tr> +<tr class="odd"> +<td align="right">28.2825393</td> +<td align="right">9.43</td> +</tr> +<tr class="even"> +<td align="right">28.2825393</td> +<td align="right">9.82</td> +</tr> +<tr class="odd"> +<td align="right">37.7100523</td> +<td align="right">7.08</td> +</tr> +<tr class="even"> +<td align="right">37.7100523</td> +<td align="right">8.64</td> +</tr> +<tr class="odd"> +<td align="right">47.1375654</td> +<td align="right">4.41</td> +</tr> +<tr class="even"> +<td align="right">47.1375654</td> +<td align="right">4.78</td> +</tr> +<tr class="odd"> +<td align="right">56.5650785</td> +<td align="right">4.92</td> +</tr> +<tr class="even"> +<td align="right">56.5650785</td> +<td align="right">5.08</td> +</tr> +<tr class="odd"> +<td align="right">80.1338612</td> +<td align="right">2.13</td> +</tr> +<tr class="even"> +<td align="right">80.1338612</td> +<td align="right">2.23</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Elliot</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">97.5</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">100.7</td> +</tr> +<tr class="odd"> +<td align="right">1.228478</td> +<td align="right">86.4</td> +</tr> +<tr class="even"> +<td align="right">1.228478</td> +<td align="right">88.5</td> +</tr> +<tr class="odd"> +<td align="right">3.685435</td> +<td align="right">69.8</td> +</tr> +<tr class="even"> +<td align="right">3.685435</td> +<td align="right">77.1</td> +</tr> +<tr class="odd"> +<td align="right">8.599349</td> +<td align="right">59.0</td> +</tr> +<tr class="even"> +<td align="right">8.599349</td> +<td align="right">54.2</td> +</tr> +<tr class="odd"> +<td align="right">17.198697</td> +<td align="right">31.3</td> +</tr> +<tr class="even"> +<td align="right">17.198697</td> +<td align="right">33.5</td> +</tr> +<tr class="odd"> +<td align="right">25.798046</td> +<td align="right">19.6</td> +</tr> +<tr class="even"> +<td align="right">25.798046</td> +<td align="right">20.9</td> +</tr> +<tr class="odd"> +<td align="right">34.397395</td> +<td align="right">13.3</td> +</tr> +<tr class="even"> +<td align="right">34.397395</td> +<td align="right">15.8</td> +</tr> +<tr class="odd"> +<td align="right">51.596092</td> +<td align="right">6.7</td> +</tr> +<tr class="even"> +<td align="right">51.596092</td> +<td align="right">8.7</td> +</tr> +<tr class="odd"> +<td align="right">68.794789</td> +<td align="right">8.8</td> +</tr> +<tr class="even"> +<td align="right">68.794789</td> +<td align="right">8.7</td> +</tr> +<tr class="odd"> +<td align="right">103.192184</td> +<td align="right">6.0</td> +</tr> +<tr class="even"> +<td align="right">103.192184</td> +<td align="right">4.4</td> +</tr> +<tr class="odd"> +<td align="right">146.188928</td> +<td align="right">3.3</td> +</tr> +<tr class="even"> +<td align="right">146.188928</td> +<td align="right">2.8</td> +</tr> +<tr class="odd"> +<td align="right">223.583066</td> +<td align="right">1.4</td> +</tr> +<tr class="even"> +<td align="right">223.583066</td> +<td align="right">1.8</td> +</tr> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">93.4</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">103.2</td> +</tr> +<tr class="odd"> +<td align="right">1.228478</td> +<td align="right">89.2</td> +</tr> +<tr class="even"> +<td align="right">1.228478</td> +<td align="right">86.6</td> +</tr> +<tr class="odd"> +<td align="right">3.685435</td> +<td align="right">78.2</td> +</tr> +<tr class="even"> +<td align="right">3.685435</td> +<td align="right">78.1</td> +</tr> +<tr class="odd"> +<td align="right">8.599349</td> +<td align="right">55.6</td> +</tr> +<tr class="even"> +<td align="right">8.599349</td> +<td align="right">53.0</td> +</tr> +<tr class="odd"> +<td align="right">17.198697</td> +<td align="right">33.7</td> +</tr> +<tr class="even"> +<td align="right">17.198697</td> +<td align="right">33.2</td> +</tr> +<tr class="odd"> +<td align="right">25.798046</td> +<td align="right">20.9</td> +</tr> +<tr class="even"> +<td align="right">25.798046</td> +<td align="right">19.9</td> +</tr> +<tr class="odd"> +<td align="right">34.397395</td> +<td align="right">18.2</td> +</tr> +<tr class="even"> +<td align="right">34.397395</td> +<td align="right">12.7</td> +</tr> +<tr class="odd"> +<td align="right">51.596092</td> +<td align="right">7.8</td> +</tr> +<tr class="even"> +<td align="right">51.596092</td> +<td align="right">9.0</td> +</tr> +<tr class="odd"> +<td align="right">68.794789</td> +<td align="right">11.4</td> +</tr> +<tr class="even"> +<td align="right">68.794789</td> +<td align="right">9.0</td> +</tr> +<tr class="odd"> +<td align="right">103.192184</td> +<td align="right">3.9</td> +</tr> +<tr class="even"> +<td align="right">103.192184</td> +<td align="right">4.4</td> +</tr> +<tr class="odd"> +<td align="right">146.188928</td> +<td align="right">2.6</td> +</tr> +<tr class="even"> +<td align="right">146.188928</td> +<td align="right">3.4</td> +</tr> +<tr class="odd"> +<td align="right">223.583066</td> +<td align="right">2.0</td> +</tr> +<tr class="even"> +<td align="right">223.583066</td> +<td align="right">1.7</td> +</tr> +</tbody> +</table> +</div> +<div class="section level2"> +<h2 id="separate-evaluations">Separate evaluations<a class="anchor" aria-label="anchor" href="#separate-evaluations"></a> +</h2> +<p>In order to obtain suitable starting parameters for the NLHM fits, +separate fits of the four models to the data for each soil are generated +using the <code>mmkin</code> function from the <code>mkin</code> +package. In a first step, constant variance is assumed. Convergence is +checked with the <code>status</code> function.</p> +<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">deg_mods</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>, <span class="st">"HS"</span><span class="op">)</span></span> +<span><span class="va">f_sep_const</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span></span> +<span> <span class="va">deg_mods</span>,</span> +<span> <span class="va">dmta_ds</span>,</span> +<span> error_model <span class="op">=</span> <span class="st">"const"</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span></span> +<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_const</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">Calke</th> +<th align="left">Borstel</th> +<th align="left">Flaach</th> +<th align="left">BBA 2.2</th> +<th align="left">BBA 2.3</th> +<th align="left">Elliot</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">HS</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>In the table above, OK indicates convergence, and C indicates failure +to converge. All separate fits with constant variance converged, with +the sole exception of the HS fit to the BBA 2.2 data. To prepare for +fitting NLHM using the two-component error model, the separate fits are +updated assuming two-component error.</p> +<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_sep_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_sep_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span> +<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_tc</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">Calke</th> +<th align="left">Borstel</th> +<th align="left">Flaach</th> +<th align="left">BBA 2.2</th> +<th align="left">BBA 2.3</th> +<th align="left">Elliot</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">HS</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>Using the two-component error model, the one fit that did not +converge with constant variance did converge, but other non-SFO fits +failed to converge.</p> +</div> +<div class="section level2"> +<h2 id="hierarchichal-model-fits">Hierarchichal model fits<a class="anchor" aria-label="anchor" href="#hierarchichal-model-fits"></a> +</h2> +<p>The following code fits eight versions of hierarchical models to the +data, using SFO, FOMC, DFOP and HS for the parent compound, and using +either constant variance or two-component error for the error model. The +default parameter distribution model in mkin allows for variation of all +degradation parameters across the assumed population of soils. In other +words, each degradation parameter is associated with a random effect as +a first step. The <code>mhmkin</code> function makes it possible to fit +all eight versions in parallel (given a sufficient number of computing +cores being available) to save execution time.</p> +<p>Convergence plots and summaries for these fits are shown in the +appendix.</p> +<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_saem</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_sep_const</span>, <span class="va">f_sep_tc</span><span class="op">)</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span></code></pre></div> +<p>The output of the <code>status</code> function shows that all fits +terminated successfully.</p> +<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">HS</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>The AIC and BIC values show that the biphasic models DFOP and HS give +the best fits.</p> +<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="right">npar</th> +<th align="right">AIC</th> +<th align="right">BIC</th> +<th align="right">Lik</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">SFO const</td> +<td align="right">5</td> +<td align="right">796.3</td> +<td align="right">795.3</td> +<td align="right">-393.2</td> +</tr> +<tr class="even"> +<td align="left">SFO tc</td> +<td align="right">6</td> +<td align="right">798.3</td> +<td align="right">797.1</td> +<td align="right">-393.2</td> +</tr> +<tr class="odd"> +<td align="left">FOMC const</td> +<td align="right">7</td> +<td align="right">734.2</td> +<td align="right">732.7</td> +<td align="right">-360.1</td> +</tr> +<tr class="even"> +<td align="left">FOMC tc</td> +<td align="right">8</td> +<td align="right">720.4</td> +<td align="right">718.8</td> +<td align="right">-352.2</td> +</tr> +<tr class="odd"> +<td align="left">DFOP const</td> +<td align="right">9</td> +<td align="right">711.8</td> +<td align="right">710.0</td> +<td align="right">-346.9</td> +</tr> +<tr class="even"> +<td align="left">HS const</td> +<td align="right">9</td> +<td align="right">714.0</td> +<td align="right">712.1</td> +<td align="right">-348.0</td> +</tr> +<tr class="odd"> +<td align="left">DFOP tc</td> +<td align="right">10</td> +<td align="right">665.5</td> +<td align="right">663.4</td> +<td align="right">-322.8</td> +</tr> +<tr class="even"> +<td align="left">HS tc</td> +<td align="right">10</td> +<td align="right">667.1</td> +<td align="right">665.0</td> +<td align="right">-323.6</td> +</tr> +</tbody> +</table> +<p>The DFOP model is preferred here, as it has a better mechanistic +basis for batch experiments with constant incubation conditions. Also, +it shows the lowest AIC and BIC values in the first set of fits when +combined with the two-component error model. Therefore, the DFOP model +was selected for further refinements of the fits with the aim to make +the model fully identifiable.</p> +<div class="section level3"> +<h3 id="parameter-identifiability-based-on-the-fisher-information-matrix">Parameter identifiability based on the Fisher Information +Matrix<a class="anchor" aria-label="anchor" href="#parameter-identifiability-based-on-the-fisher-information-matrix"></a> +</h3> +<p>Using the <code>illparms</code> function, ill-defined statistical +model parameters such as standard deviations of the degradation +parameters in the population and error model parameters can be +found.</p> +<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left"></td> +<td align="left">b.1</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left"></td> +<td align="left">sd(DMTA_0)</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">sd(k2)</td> +<td align="left">sd(k2)</td> +</tr> +<tr class="even"> +<td align="left">HS</td> +<td align="left"></td> +<td align="left">sd(tb)</td> +</tr> +</tbody> +</table> +<p>According to the <code>illparms</code> function, the fitted standard +deviation of the second kinetic rate constant <code>k2</code> is +ill-defined in both DFOP fits. This suggests that different values would +be obtained for this standard deviation when using different starting +values.</p> +<p>The thus identified overparameterisation is addressed by removing the +random effect for <code>k2</code> from the parameter model.</p> +<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_saem_dfop_tc_no_ranef_k2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>,</span> +<span> no_random_effect <span class="op">=</span> <span class="st">"k2"</span><span class="op">)</span></span></code></pre></div> +<p>For the resulting fit, it is checked whether there are still +ill-defined parameters,</p> +<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span><span class="op">)</span></span></code></pre></div> +<p>which is not the case. Below, the refined model is compared with the +previous best model. The model without random effect for <code>k2</code> +is a reduced version of the previous model. Therefore, the models are +nested and can be compared using the likelihood ratio test. This is +achieved with the argument <code>test = TRUE</code> to the +<code>anova</code> function.</p> +<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>, <span class="va">f_saem_dfop_tc_no_ranef_k2</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">|></span></span> +<span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>format.args <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<colgroup> +<col width="37%"> +<col width="6%"> +<col width="8%"> +<col width="8%"> +<col width="9%"> +<col width="9%"> +<col width="4%"> +<col width="15%"> +</colgroup> +<thead><tr class="header"> +<th align="left"></th> +<th align="right">npar</th> +<th align="right">AIC</th> +<th align="right">BIC</th> +<th align="right">Lik</th> +<th align="right">Chisq</th> +<th align="right">Df</th> +<th align="right">Pr(>Chisq)</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">f_saem_dfop_tc_no_ranef_k2</td> +<td align="right">9</td> +<td align="right">663.8</td> +<td align="right">661.9</td> +<td align="right">-322.9</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="left">f_saem[[“DFOP”, “tc”]]</td> +<td align="right">10</td> +<td align="right">665.5</td> +<td align="right">663.4</td> +<td align="right">-322.8</td> +<td align="right">0.2809</td> +<td align="right">1</td> +<td align="right">0.5961</td> +</tr> +</tbody> +</table> +<p>The AIC and BIC criteria are lower after removal of the ill-defined +random effect for <code>k2</code>. The p value of the likelihood ratio +test is much greater than 0.05, indicating that the model with the +higher likelihood (here the model with random effects for all +degradation parameters <code>f_saem[["DFOP", "tc"]]</code>) does not fit +significantly better than the model with the lower likelihood (the +reduced model <code>f_saem_dfop_tc_no_ranef_k2</code>).</p> +<p>Therefore, AIC, BIC and likelihood ratio test suggest the use of the +reduced model.</p> +<p>The convergence of the fit is checked visually.</p> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-no-ranef-k2-1.png" alt="Convergence plot for the NLHM DFOP fit with two-component error and without a random effect on 'k2'" width="864"><p class="caption"> +Convergence plot for the NLHM DFOP fit with two-component error and +without a random effect on ‘k2’ +</p> +</div> +<p>All parameters appear to have converged to a satisfactory degree. The +final fit is plotted using the plot method from the mkin package.</p> +<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/plot-saem-dfop-tc-no-ranef-k2-1.png" alt="Plot of the final NLHM DFOP fit" width="864"><p class="caption"> +Plot of the final NLHM DFOP fit +</p> +</div> +<p>Finally, a summary report of the fit is produced.</p> +<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span><span class="op">)</span></span></code></pre></div> +<pre><code>saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 11:22:51 2023 +Date of summary: Sat Jan 28 11:22:52 2023 + +Equations: +d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 3.74 s +Using 300, 100 iterations and 9 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + DMTA_0 k1 k2 g +98.759266 0.087034 0.009933 0.930827 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 k1 k2 g +DMTA_0 98.76 0 0 0 +k1 0.00 1 0 0 +k2 0.00 0 1 0 +g 0.00 0 0 1 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 663.8 661.9 -322.9 + +Optimised parameters: + est. lower upper +DMTA_0 98.228939 96.285869 100.17201 +k1 0.064063 0.033477 0.09465 +k2 0.008297 0.005824 0.01077 +g 0.953821 0.914328 0.99331 +a.1 1.068479 0.869538 1.26742 +b.1 0.029424 0.022406 0.03644 +SD.DMTA_0 2.030437 0.404824 3.65605 +SD.k1 0.594692 0.256660 0.93272 +SD.g 1.006754 0.361327 1.65218 + +Correlation: + DMTA_0 k1 k2 +k1 0.0218 +k2 0.0556 0.0355 +g -0.0516 -0.0284 -0.2800 + +Random effects: + est. lower upper +SD.DMTA_0 2.0304 0.4048 3.6560 +SD.k1 0.5947 0.2567 0.9327 +SD.g 1.0068 0.3613 1.6522 + +Variance model: + est. lower upper +a.1 1.06848 0.86954 1.26742 +b.1 0.02942 0.02241 0.03644 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +DMTA 11.45 41.4 12.46 10.82 83.54</code></pre> +</div> +<div class="section level3"> +<h3 id="alternative-check-of-parameter-identifiability">Alternative check of parameter identifiability<a class="anchor" aria-label="anchor" href="#alternative-check-of-parameter-identifiability"></a> +</h3> +<p>The parameter check used in the <code>illparms</code> function is +based on a quadratic approximation of the likelihood surface near its +optimum, which is calculated using the Fisher Information Matrix (FIM). +An alternative way to check parameter identifiability <span class="citation">(Duchesne et al. 2021)</span> based on a multistart +approach has recently been implemented in mkin.</p> +<p>The graph below shows boxplots of the parameters obtained in 50 runs +of the saem algorithm with different parameter combinations, sampled +from the range of the parameters obtained for the individual datasets +fitted separately using nonlinear regression.</p> +<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_saem_dfop_tc_multi</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>, n <span class="op">=</span> <span class="fl">50</span>, cores <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></span></code></pre></div> +<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span> +<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_multi</span>, lpos <span class="op">=</span> <span class="st">"bottomright"</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.3</span>, <span class="fl">10</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/multistart-full-par-1.png" alt="Scaled parameters from the multistart runs, full model" width="960"><p class="caption"> +Scaled parameters from the multistart runs, full model +</p> +</div> +<p>The graph clearly confirms the lack of identifiability of the +variance of <code>k2</code> in the full model. The overparameterisation +of the model also indicates a lack of identifiability of the variance of +parameter <code>g</code>.</p> +<p>The parameter boxplots of the multistart runs with the reduced model +shown below indicate that all runs give similar results, regardless of +the starting parameters.</p> +<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_saem_dfop_tc_no_ranef_k2_multi</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span>,</span> +<span> n <span class="op">=</span> <span class="fl">50</span>, cores <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></span></code></pre></div> +<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span> +<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2_multi</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span>,</span> +<span> lpos <span class="op">=</span> <span class="st">"bottomright"</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/multistart-reduced-par-1.png" alt="Scaled parameters from the multistart runs, reduced model" width="960"><p class="caption"> +Scaled parameters from the multistart runs, reduced model +</p> +</div> +<p>When only the parameters of the top 25% of the fits are shown (based +on a feature introduced in mkin 1.2.2 currently under development), the +scatter is even less as shown below.</p> +<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span> +<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2_multi</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span>, llquant <span class="op">=</span> <span class="fl">0.25</span>,</span> +<span> lpos <span class="op">=</span> <span class="st">"bottomright"</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/multistart-reduced-par-llquant-1.png" alt="Scaled parameters from the multistart runs, reduced model, fits with the top 25\% likelihood values" width="960"><p class="caption"> +Scaled parameters from the multistart runs, reduced model, fits with the +top 25% likelihood values +</p> +</div> +</div> +</div> +<div class="section level2"> +<h2 id="conclusions">Conclusions<a class="anchor" aria-label="anchor" href="#conclusions"></a> +</h2> +<p>Fitting the four parent degradation models SFO, FOMC, DFOP and HS as +part of hierarchical model fits with two different error models and +normal distributions of the transformed degradation parameters works +without technical problems. The biphasic models DFOP and HS gave the +best fit to the data, but the default parameter distribution model was +not fully identifiable. Removing the random effect for the second +kinetic rate constant of the DFOP model resulted in a reduced model that +was fully identifiable and showed the lowest values for the model +selection criteria AIC and BIC. The reliability of the identification of +all model parameters was confirmed using multiple starting values.</p> +</div> +<div class="section level2"> +<h2 id="acknowledgements">Acknowledgements<a class="anchor" aria-label="anchor" href="#acknowledgements"></a> +</h2> +<p>The helpful comments by Janina Wöltjen of the German Environment +Agency are gratefully acknowledged.</p> +</div> +<div class="section level2"> +<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a> +</h2> +<div id="refs" class="references csl-bib-body hanging-indent"> +<div id="ref-duchesne_2021" class="csl-entry"> +Duchesne, Ronan, Anissa Guillemin, Olivier Gandrillon, and Fabien +Crauste. 2021. <span>“Practical Identifiability in the Frame of +Nonlinear Mixed Effects Models: The Example of the in Vitro +Erythropoiesis.”</span> <em>BMC Bioinformatics</em> 22 (478). <a href="https://doi.org/10.1186/s12859-021-04373-4" class="external-link">https://doi.org/10.1186/s12859-021-04373-4</a>. +</div> +</div> +</div> +<div class="section level2"> +<h2 id="appendix">Appendix<a class="anchor" aria-label="anchor" href="#appendix"></a> +</h2> +<div class="section level3"> +<h3 id="hierarchical-model-fit-listings">Hierarchical model fit listings<a class="anchor" aria-label="anchor" href="#hierarchical-model-fit-listings"></a> +</h3> +<caption> +Hierarchical mkin fit of the SFO model with error model const +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 11:22:44 2023 +Date of summary: Sat Jan 28 11:23:57 2023 + +Equations: +d_DMTA/dt = - k_DMTA * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 0.982 s +Using 300, 100 iterations and 9 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + DMTA_0 k_DMTA +97.2953 0.0566 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 k_DMTA +DMTA_0 97.3 0 +k_DMTA 0.0 1 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 796.3 795.3 -393.2 + +Optimised parameters: + est. lower upper +DMTA_0 97.28130 95.71113 98.8515 +k_DMTA 0.05665 0.02909 0.0842 +a.1 2.66442 2.35579 2.9731 +SD.DMTA_0 1.54776 0.15447 2.9411 +SD.k_DMTA 0.60690 0.26248 0.9513 + +Correlation: + DMTA_0 +k_DMTA 0.0168 + +Random effects: + est. lower upper +SD.DMTA_0 1.5478 0.1545 2.9411 +SD.k_DMTA 0.6069 0.2625 0.9513 + +Variance model: + est. lower upper +a.1 2.664 2.356 2.973 + +Estimated disappearance times: + DT50 DT90 +DMTA 12.24 40.65 + +</code></pre> +<p></p> +<caption> +Hierarchical mkin fit of the SFO model with error model tc +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 11:22:46 2023 +Date of summary: Sat Jan 28 11:23:57 2023 + +Equations: +d_DMTA/dt = - k_DMTA * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 2.39 s +Using 300, 100 iterations and 9 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + DMTA_0 k_DMTA +96.99175 0.05603 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 k_DMTA +DMTA_0 96.99 0 +k_DMTA 0.00 1 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 798.3 797.1 -393.2 + +Optimised parameters: + est. lower upper +DMTA_0 97.271822 95.703157 98.84049 +k_DMTA 0.056638 0.029110 0.08417 +a.1 2.660081 2.230398 3.08976 +b.1 0.001665 -0.006911 0.01024 +SD.DMTA_0 1.545520 0.145035 2.94601 +SD.k_DMTA 0.606422 0.262274 0.95057 + +Correlation: + DMTA_0 +k_DMTA 0.0169 + +Random effects: + est. lower upper +SD.DMTA_0 1.5455 0.1450 2.9460 +SD.k_DMTA 0.6064 0.2623 0.9506 + +Variance model: + est. lower upper +a.1 2.660081 2.230398 3.08976 +b.1 0.001665 -0.006911 0.01024 + +Estimated disappearance times: + DT50 DT90 +DMTA 12.24 40.65 + +</code></pre> +<p></p> +<caption> +Hierarchical mkin fit of the FOMC model with error model const +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 11:22:45 2023 +Date of summary: Sat Jan 28 11:23:57 2023 + +Equations: +d_DMTA/dt = - (alpha/beta) * 1/((time/beta) + 1) * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 1.552 s +Using 300, 100 iterations and 9 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + DMTA_0 alpha beta + 98.292 9.909 156.341 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 alpha beta +DMTA_0 98.29 0 0 +alpha 0.00 1 0 +beta 0.00 0 1 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 734.2 732.7 -360.1 + +Optimised parameters: + est. lower upper +DMTA_0 98.3435 96.9033 99.784 +alpha 7.2007 2.5889 11.812 +beta 112.8746 34.8816 190.868 +a.1 2.0459 1.8054 2.286 +SD.DMTA_0 1.4795 0.2717 2.687 +SD.alpha 0.6396 0.1509 1.128 +SD.beta 0.6874 0.1587 1.216 + +Correlation: + DMTA_0 alpha +alpha -0.1125 +beta -0.1227 0.3632 + +Random effects: + est. lower upper +SD.DMTA_0 1.4795 0.2717 2.687 +SD.alpha 0.6396 0.1509 1.128 +SD.beta 0.6874 0.1587 1.216 + +Variance model: + est. lower upper +a.1 2.046 1.805 2.286 + +Estimated disappearance times: + DT50 DT90 DT50back +DMTA 11.41 42.53 12.8 + +</code></pre> +<p></p> +<caption> +Hierarchical mkin fit of the FOMC model with error model tc +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 11:22:46 2023 +Date of summary: Sat Jan 28 11:23:57 2023 + +Equations: +d_DMTA/dt = - (alpha/beta) * 1/((time/beta) + 1) * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 2.764 s +Using 300, 100 iterations and 9 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: +DMTA_0 alpha beta +98.772 4.663 92.597 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 alpha beta +DMTA_0 98.77 0 0 +alpha 0.00 1 0 +beta 0.00 0 1 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 720.4 718.8 -352.2 + +Optimised parameters: + est. lower upper +DMTA_0 98.99136 97.26011 100.72261 +alpha 5.86312 2.57485 9.15138 +beta 88.55571 29.20889 147.90254 +a.1 1.51063 1.24384 1.77741 +b.1 0.02824 0.02040 0.03609 +SD.DMTA_0 1.57436 -0.04867 3.19739 +SD.alpha 0.59871 0.17132 1.02611 +SD.beta 0.72994 0.22849 1.23139 + +Correlation: + DMTA_0 alpha +alpha -0.1363 +beta -0.1414 0.2542 + +Random effects: + est. lower upper +SD.DMTA_0 1.5744 -0.04867 3.197 +SD.alpha 0.5987 0.17132 1.026 +SD.beta 0.7299 0.22849 1.231 + +Variance model: + est. lower upper +a.1 1.51063 1.2438 1.77741 +b.1 0.02824 0.0204 0.03609 + +Estimated disappearance times: + DT50 DT90 DT50back +DMTA 11.11 42.6 12.82 + +</code></pre> +<p></p> +<caption> +Hierarchical mkin fit of the DFOP model with error model const +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 11:22:45 2023 +Date of summary: Sat Jan 28 11:23:57 2023 + +Equations: +d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 1.649 s +Using 300, 100 iterations and 9 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + DMTA_0 k1 k2 g +98.64383 0.09211 0.02999 0.76814 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 k1 k2 g +DMTA_0 98.64 0 0 0 +k1 0.00 1 0 0 +k2 0.00 0 1 0 +g 0.00 0 0 1 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 711.8 710 -346.9 + +Optimised parameters: + est. lower upper +DMTA_0 98.092481 96.573898 99.61106 +k1 0.062499 0.030336 0.09466 +k2 0.009065 -0.005133 0.02326 +g 0.948967 0.862079 1.03586 +a.1 1.821671 1.604774 2.03857 +SD.DMTA_0 1.677785 0.472066 2.88350 +SD.k1 0.634962 0.270788 0.99914 +SD.k2 1.033498 -0.205994 2.27299 +SD.g 1.710046 0.428642 2.99145 + +Correlation: + DMTA_0 k1 k2 +k1 0.0246 +k2 0.0491 0.0953 +g -0.0552 -0.0889 -0.4795 + +Random effects: + est. lower upper +SD.DMTA_0 1.678 0.4721 2.8835 +SD.k1 0.635 0.2708 0.9991 +SD.k2 1.033 -0.2060 2.2730 +SD.g 1.710 0.4286 2.9914 + +Variance model: + est. lower upper +a.1 1.822 1.605 2.039 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +DMTA 11.79 42.8 12.88 11.09 76.46 + +</code></pre> +<p></p> +<caption> +Hierarchical mkin fit of the DFOP model with error model tc +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 11:22:46 2023 +Date of summary: Sat Jan 28 11:23:57 2023 + +Equations: +d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 3.288 s +Using 300, 100 iterations and 9 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + DMTA_0 k1 k2 g +98.759266 0.087034 0.009933 0.930827 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 k1 k2 g +DMTA_0 98.76 0 0 0 +k1 0.00 1 0 0 +k2 0.00 0 1 0 +g 0.00 0 0 1 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 665.5 663.4 -322.8 + +Optimised parameters: + est. lower upper +DMTA_0 98.377019 96.447952 100.30609 +k1 0.064843 0.034607 0.09508 +k2 0.008895 0.006368 0.01142 +g 0.949696 0.903815 0.99558 +a.1 1.065241 0.865754 1.26473 +b.1 0.029340 0.022336 0.03634 +SD.DMTA_0 2.007754 0.387982 3.62753 +SD.k1 0.580473 0.250286 0.91066 +SD.k2 0.006105 -4.920337 4.93255 +SD.g 1.097149 0.412779 1.78152 + +Correlation: + DMTA_0 k1 k2 +k1 0.0235 +k2 0.0595 0.0424 +g -0.0470 -0.0278 -0.2731 + +Random effects: + est. lower upper +SD.DMTA_0 2.007754 0.3880 3.6275 +SD.k1 0.580473 0.2503 0.9107 +SD.k2 0.006105 -4.9203 4.9325 +SD.g 1.097149 0.4128 1.7815 + +Variance model: + est. lower upper +a.1 1.06524 0.86575 1.26473 +b.1 0.02934 0.02234 0.03634 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +DMTA 11.36 41.32 12.44 10.69 77.92 + +</code></pre> +<p></p> +<caption> +Hierarchical mkin fit of the HS model with error model const +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 11:22:45 2023 +Date of summary: Sat Jan 28 11:23:57 2023 + +Equations: +d_DMTA/dt = - ifelse(time <= tb, k1, k2) * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 2.006 s +Using 300, 100 iterations and 9 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + DMTA_0 k1 k2 tb +97.82176 0.06931 0.02997 11.13945 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 k1 k2 tb +DMTA_0 97.82 0 0 0 +k1 0.00 1 0 0 +k2 0.00 0 1 0 +tb 0.00 0 0 1 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 714 712.1 -348 + +Optimised parameters: + est. lower upper +DMTA_0 98.16102 96.47747 99.84456 +k1 0.07876 0.05261 0.10491 +k2 0.02227 0.01706 0.02747 +tb 13.99089 -7.40049 35.38228 +a.1 1.82305 1.60700 2.03910 +SD.DMTA_0 1.88413 0.56204 3.20622 +SD.k1 0.34292 0.10482 0.58102 +SD.k2 0.19851 0.01718 0.37985 +SD.tb 1.68168 0.58064 2.78272 + +Correlation: + DMTA_0 k1 k2 +k1 0.0142 +k2 0.0001 -0.0025 +tb 0.0165 -0.1256 -0.0301 + +Random effects: + est. lower upper +SD.DMTA_0 1.8841 0.56204 3.2062 +SD.k1 0.3429 0.10482 0.5810 +SD.k2 0.1985 0.01718 0.3798 +SD.tb 1.6817 0.58064 2.7827 + +Variance model: + est. lower upper +a.1 1.823 1.607 2.039 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +DMTA 8.801 67.91 20.44 8.801 31.13 + +</code></pre> +<p></p> +<caption> +Hierarchical mkin fit of the HS model with error model tc +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.2 +R version used for fitting: 4.2.2 +Date of fit: Sat Jan 28 11:22:46 2023 +Date of summary: Sat Jan 28 11:23:57 2023 + +Equations: +d_DMTA/dt = - ifelse(time <= tb, k1, k2) * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 3.267 s +Using 300, 100 iterations and 9 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + DMTA_0 k1 k2 tb +98.45190 0.07525 0.02576 19.19375 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 k1 k2 tb +DMTA_0 98.45 0 0 0 +k1 0.00 1 0 0 +k2 0.00 0 1 0 +tb 0.00 0 0 1 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 667.1 665 -323.6 + +Optimised parameters: + est. lower upper +DMTA_0 97.76570 95.81350 99.71791 +k1 0.05855 0.03080 0.08630 +k2 0.02337 0.01664 0.03010 +tb 31.09638 29.38289 32.80987 +a.1 1.08835 0.88590 1.29080 +b.1 0.02964 0.02257 0.03671 +SD.DMTA_0 2.04877 0.42607 3.67147 +SD.k1 0.59166 0.25621 0.92711 +SD.k2 0.30698 0.09561 0.51835 +SD.tb 0.01274 -0.10914 0.13462 + +Correlation: + DMTA_0 k1 k2 +k1 0.0160 +k2 -0.0070 -0.0024 +tb -0.0668 -0.0103 -0.2013 + +Random effects: + est. lower upper +SD.DMTA_0 2.04877 0.42607 3.6715 +SD.k1 0.59166 0.25621 0.9271 +SD.k2 0.30698 0.09561 0.5183 +SD.tb 0.01274 -0.10914 0.1346 + +Variance model: + est. lower upper +a.1 1.08835 0.88590 1.29080 +b.1 0.02964 0.02257 0.03671 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +DMTA 11.84 51.71 15.57 11.84 29.66 + +</code></pre> +<p></p> +</div> +<div class="section level3"> +<h3 id="hierarchical-model-convergence-plots">Hierarchical model convergence plots<a class="anchor" aria-label="anchor" href="#hierarchical-model-convergence-plots"></a> +</h3> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/convergence-saem-sfo-const-1.png" alt="Convergence plot for the NLHM SFO fit with constant variance" width="864"><p class="caption"> +Convergence plot for the NLHM SFO fit with constant variance +</p> +</div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/convergence-saem-sfo-tc-1.png" alt="Convergence plot for the NLHM SFO fit with two-component error" width="864"><p class="caption"> +Convergence plot for the NLHM SFO fit with two-component error +</p> +</div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/convergence-saem-fomc-const-1.png" alt="Convergence plot for the NLHM FOMC fit with constant variance" width="864"><p class="caption"> +Convergence plot for the NLHM FOMC fit with constant variance +</p> +</div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/convergence-saem-fomc-tc-1.png" alt="Convergence plot for the NLHM FOMC fit with two-component error" width="864"><p class="caption"> +Convergence plot for the NLHM FOMC fit with two-component error +</p> +</div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png" alt="Convergence plot for the NLHM DFOP fit with constant variance" width="864"><p class="caption"> +Convergence plot for the NLHM DFOP fit with constant variance +</p> +</div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-1.png" alt="Convergence plot for the NLHM DFOP fit with two-component error" width="864"><p class="caption"> +Convergence plot for the NLHM DFOP fit with two-component error +</p> +</div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/convergence-saem-hs-const-1.png" alt="Convergence plot for the NLHM HS fit with constant variance" width="864"><p class="caption"> +Convergence plot for the NLHM HS fit with constant variance +</p> +</div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/convergence-saem-hs-tc-1.png" alt="Convergence plot for the NLHM HS fit with two-component error" width="864"><p class="caption"> +Convergence plot for the NLHM HS fit with two-component error +</p> +</div> +</div> +<div class="section level3"> +<h3 id="session-info">Session info<a class="anchor" aria-label="anchor" href="#session-info"></a> +</h3> +<pre><code>R version 4.2.2 Patched (2022-11-10 r83330) +Platform: x86_64-pc-linux-gnu (64-bit) +Running under: Debian GNU/Linux bookworm/sid + +Matrix products: default +BLAS: /usr/lib/x86_64-linux-gnu/openblas-serial/libblas.so.3 +LAPACK: /usr/lib/x86_64-linux-gnu/openblas-serial/libopenblas-r0.3.21.so + +locale: + [1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C + [3] LC_TIME=de_DE.UTF-8 LC_COLLATE=de_DE.UTF-8 + [5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=de_DE.UTF-8 + [7] LC_PAPER=de_DE.UTF-8 LC_NAME=C + [9] LC_ADDRESS=C LC_TELEPHONE=C +[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C + +attached base packages: +[1] parallel stats graphics grDevices utils datasets methods +[8] base + +other attached packages: +[1] saemix_3.2 npde_3.3 knitr_1.41 mkin_1.2.2 + +loaded via a namespace (and not attached): + [1] deSolve_1.34 zoo_1.8-11 tidyselect_1.2.0 xfun_0.35 + [5] bslib_0.4.2 purrr_1.0.0 lattice_0.20-45 colorspace_2.0-3 + [9] vctrs_0.5.1 generics_0.1.3 htmltools_0.5.4 yaml_2.3.6 +[13] utf8_1.2.2 rlang_1.0.6 pkgdown_2.0.7 jquerylib_0.1.4 +[17] pillar_1.8.1 glue_1.6.2 DBI_1.1.3 lifecycle_1.0.3 +[21] stringr_1.5.0 munsell_0.5.0 gtable_0.3.1 ragg_1.2.4 +[25] codetools_0.2-18 memoise_2.0.1 evaluate_0.19 fastmap_1.1.0 +[29] lmtest_0.9-40 fansi_1.0.3 highr_0.9 scales_1.2.1 +[33] cachem_1.0.6 desc_1.4.2 jsonlite_1.8.4 systemfonts_1.0.4 +[37] fs_1.5.2 textshaping_0.3.6 gridExtra_2.3 ggplot2_3.4.0 +[41] digest_0.6.31 stringi_1.7.8 dplyr_1.0.10 grid_4.2.2 +[45] rprojroot_2.0.3 cli_3.5.0 tools_4.2.2 magrittr_2.0.3 +[49] sass_0.4.4 tibble_3.1.8 pkgconfig_2.0.3 assertthat_0.2.1 +[53] rmarkdown_2.19 R6_2.5.1 mclust_6.0.0 nlme_3.1-161 +[57] compiler_4.2.2 </code></pre> +</div> +<div class="section level3"> +<h3 id="hardware-info">Hardware info<a class="anchor" aria-label="anchor" href="#hardware-info"></a> +</h3> +<pre><code>CPU model: AMD Ryzen 9 7950X 16-Core Processor</code></pre> +<pre><code>MemTotal: 64940452 kB</code></pre> +</div> +</div> + </div> + + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + + <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2> + </nav> +</div> + +</div> + + + + <footer><div class="copyright"> + <p></p> +<p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> +</div> + + </footer> +</div> + + + + + + + </body> +</html> diff --git a/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png b/docs/dev/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png Binary files differnew file mode 100644 index 00000000..3f145074 --- /dev/null +++ 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+</div> +<!--/.nav-collapse --> + </div> +<!--/.container --> +</div> +<!--/.navbar --> + + + + </header><div class="row"> + <div class="col-md-9 contents"> + <div class="page-header toc-ignore"> + <h1 data-toc-skip>Testing hierarchical pathway kinetics with +residue data on dimethenamid and dimethenamid-P</h1> + <h4 data-toc-skip class="author">Johannes +Ranke</h4> + + <h4 data-toc-skip class="date">Last change on 8 January +2023, last compiled on 28 Januar 2023</h4> + + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/prebuilt/2022_dmta_pathway.rmd" class="external-link"><code>vignettes/prebuilt/2022_dmta_pathway.rmd</code></a></small> + <div class="hidden name"><code>2022_dmta_pathway.rmd</code></div> + + </div> + + + +<div class="section level2"> +<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a> +</h2> +<p>The purpose of this document is to test demonstrate how nonlinear +hierarchical models (NLHM) based on the parent degradation models SFO, +FOMC, DFOP and HS, with parallel formation of two or more metabolites +can be fitted with the mkin package.</p> +<p>It was assembled in the course of work package 1.2 of Project Number +173340 (Application of nonlinear hierarchical models to the kinetic +evaluation of chemical degradation data) of the German Environment +Agency carried out in 2022 and 2023.</p> +<p>The mkin package is used in version 1.2.2, which is currently under +development. It contains the test data, and the functions used in the +evaluations. The <code>saemix</code> package is used as a backend for +fitting the NLHM, but is also loaded to make the convergence plot +function available.</p> +<p>This document is processed with the <code>knitr</code> package, which +also provides the <code>kable</code> function that is used to improve +the display of tabular data in R markdown documents. For parallel +processing, the <code>parallel</code> package is used.</p> +<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span> +<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://yihui.org/knitr/" class="external-link">knitr</a></span><span class="op">)</span></span> +<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span> +<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">parallel</span><span class="op">)</span></span> +<span><span class="va">n_cores</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span></span> +<span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="op">{</span></span> +<span> <span class="va">cl</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span> +<span><span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span> +<span> <span class="va">cl</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makeForkCluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span> +<span><span class="op">}</span></span></code></pre></div> +</div> +<div class="section level2"> +<h2 id="data">Data<a class="anchor" aria-label="anchor" href="#data"></a> +</h2> +<p>The test data are available in the mkin package as an object of class +<code>mkindsg</code> (mkin dataset group) under the identifier +<code>dimethenamid_2018</code>. The following preprocessing steps are +done in this document.</p> +<ul> +<li>The data available for the enantiomer dimethenamid-P (DMTAP) are +renamed to have the same substance name as the data for the racemic +mixture dimethenamid (DMTA). The reason for this is that no difference +between their degradation behaviour was identified in the EU risk +assessment.</li> +<li>Unnecessary columns are discarded</li> +<li>The observation times of each dataset are multiplied with the +corresponding normalisation factor also available in the dataset, in +order to make it possible to describe all datasets with a single set of +parameters that are independent of temperature</li> +<li>Finally, datasets observed in the same soil (<code>Elliot 1</code> +and <code>Elliot 2</code>) are combined, resulting in dimethenamid +(DMTA) data from six soils.</li> +</ul> +<p>The following commented R code performs this preprocessing.</p> +<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="co"># Apply a function to each of the seven datasets in the mkindsg object to create a list</span></span> +<span><span class="va">dmta_ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span> +<span> <span class="va">ds_i</span> <span class="op"><-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span> <span class="co"># Get a dataset</span></span> +<span> <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op"><-</span> <span class="st">"DMTA"</span> <span class="co"># Rename DMTAP to DMTA</span></span> +<span> <span class="va">ds_i</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">ds_i</span>, select <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">)</span> <span class="co"># Select data</span></span> +<span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op"><-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span> <span class="co"># Normalise time</span></span> +<span> <span class="va">ds_i</span> <span class="co"># Return the dataset</span></span> +<span><span class="op">}</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># Use dataset titles as names for the list elements</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># Combine data for Elliot soil to obtain a named list with six elements</span></span> +<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span> <span class="co">#</span></span> +<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span></span> +<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span></span></code></pre></div> +<p>The following tables show the 6 datasets.</p> +<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="kw">for</span> <span class="op">(</span><span class="va">ds_name</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span> +<span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span></span> +<span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mkin_long_to_wide.html">mkin_long_to_wide</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="va">ds_name</span><span class="op">]</span><span class="op">]</span><span class="op">)</span>,</span> +<span> caption <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="va">ds_name</span><span class="op">)</span>,</span> +<span> booktabs <span class="op">=</span> <span class="cn">TRUE</span>, row.names <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">)</span></span> +<span> <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"\n\\clearpage\n"</span><span class="op">)</span></span> +<span><span class="op">}</span></span></code></pre></div> +<table class="table"> +<caption>Dataset Calke</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +<th align="right">M23</th> +<th align="right">M27</th> +<th align="right">M31</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0</td> +<td align="right">95.8</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">0</td> +<td align="right">98.7</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">14</td> +<td align="right">60.5</td> +<td align="right">4.1</td> +<td align="right">1.5</td> +<td align="right">2.0</td> +</tr> +<tr class="even"> +<td align="right">30</td> +<td align="right">39.1</td> +<td align="right">5.3</td> +<td align="right">2.4</td> +<td align="right">2.1</td> +</tr> +<tr class="odd"> +<td align="right">59</td> +<td align="right">15.2</td> +<td align="right">6.0</td> +<td align="right">3.2</td> +<td align="right">2.2</td> +</tr> +<tr class="even"> +<td align="right">120</td> +<td align="right">4.8</td> +<td align="right">4.3</td> +<td align="right">3.8</td> +<td align="right">1.8</td> +</tr> +<tr class="odd"> +<td align="right">120</td> +<td align="right">4.6</td> +<td align="right">4.1</td> +<td align="right">3.7</td> +<td align="right">2.1</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Borstel</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +<th align="right">M23</th> +<th align="right">M27</th> +<th align="right">M31</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">100.5</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">99.6</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">1.941295</td> +<td align="right">91.9</td> +<td align="right">0.4</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">1.941295</td> +<td align="right">91.3</td> +<td align="right">0.5</td> +<td align="right">0.3</td> +<td align="right">0.1</td> +</tr> +<tr class="odd"> +<td align="right">6.794534</td> +<td align="right">81.8</td> +<td align="right">1.2</td> +<td align="right">0.8</td> +<td align="right">1.0</td> +</tr> +<tr class="even"> +<td align="right">6.794534</td> +<td align="right">82.1</td> +<td align="right">1.3</td> +<td align="right">0.9</td> +<td align="right">0.9</td> +</tr> +<tr class="odd"> +<td align="right">13.589067</td> +<td align="right">69.1</td> +<td align="right">2.8</td> +<td align="right">1.4</td> +<td align="right">2.0</td> +</tr> +<tr class="even"> +<td align="right">13.589067</td> +<td align="right">68.0</td> +<td align="right">2.0</td> +<td align="right">1.4</td> +<td align="right">2.5</td> +</tr> +<tr class="odd"> +<td align="right">27.178135</td> +<td align="right">51.4</td> +<td align="right">2.9</td> +<td align="right">2.7</td> +<td align="right">4.3</td> +</tr> +<tr class="even"> +<td align="right">27.178135</td> +<td align="right">51.4</td> +<td align="right">4.9</td> +<td align="right">2.6</td> +<td align="right">3.2</td> +</tr> +<tr class="odd"> +<td align="right">56.297565</td> +<td align="right">27.6</td> +<td align="right">12.2</td> +<td align="right">4.4</td> +<td align="right">4.3</td> +</tr> +<tr class="even"> +<td align="right">56.297565</td> +<td align="right">26.8</td> +<td align="right">12.2</td> +<td align="right">4.7</td> +<td align="right">4.8</td> +</tr> +<tr class="odd"> +<td align="right">86.387643</td> +<td align="right">15.7</td> +<td align="right">12.2</td> +<td align="right">5.4</td> +<td align="right">5.0</td> +</tr> +<tr class="even"> +<td align="right">86.387643</td> +<td align="right">15.3</td> +<td align="right">12.0</td> +<td align="right">5.2</td> +<td align="right">5.1</td> +</tr> +<tr class="odd"> +<td align="right">115.507073</td> +<td align="right">7.9</td> +<td align="right">10.4</td> +<td align="right">5.4</td> +<td align="right">4.3</td> +</tr> +<tr class="even"> +<td align="right">115.507073</td> +<td align="right">8.1</td> +<td align="right">11.6</td> +<td align="right">5.4</td> +<td align="right">4.4</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Flaach</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +<th align="right">M23</th> +<th align="right">M27</th> +<th align="right">M31</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">96.5</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">0.0000000</td> +<td align="right">96.8</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">97.0</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">0.6233856</td> +<td align="right">82.9</td> +<td align="right">0.7</td> +<td align="right">1.1</td> +<td align="right">0.3</td> +</tr> +<tr class="odd"> +<td align="right">0.6233856</td> +<td align="right">86.7</td> +<td align="right">0.7</td> +<td align="right">1.1</td> +<td align="right">0.3</td> +</tr> +<tr class="even"> +<td align="right">0.6233856</td> +<td align="right">87.4</td> +<td align="right">0.2</td> +<td align="right">0.3</td> +<td align="right">0.1</td> +</tr> +<tr class="odd"> +<td align="right">1.8701567</td> +<td align="right">72.8</td> +<td align="right">2.2</td> +<td align="right">2.6</td> +<td align="right">0.7</td> +</tr> +<tr class="even"> +<td align="right">1.8701567</td> +<td align="right">69.9</td> +<td align="right">1.8</td> +<td align="right">2.4</td> +<td align="right">0.6</td> +</tr> +<tr class="odd"> +<td align="right">1.8701567</td> +<td align="right">71.9</td> +<td align="right">1.6</td> +<td align="right">2.3</td> +<td align="right">0.7</td> +</tr> +<tr class="even"> +<td align="right">4.3636989</td> +<td align="right">51.4</td> +<td align="right">4.1</td> +<td align="right">5.0</td> +<td align="right">1.3</td> +</tr> +<tr class="odd"> +<td align="right">4.3636989</td> +<td align="right">52.9</td> +<td align="right">4.2</td> +<td align="right">5.9</td> +<td align="right">1.2</td> +</tr> +<tr class="even"> +<td align="right">4.3636989</td> +<td align="right">48.6</td> +<td align="right">4.2</td> +<td align="right">4.8</td> +<td align="right">1.4</td> +</tr> +<tr class="odd"> +<td align="right">8.7273979</td> +<td align="right">28.5</td> +<td align="right">7.5</td> +<td align="right">8.5</td> +<td align="right">2.4</td> +</tr> +<tr class="even"> +<td align="right">8.7273979</td> +<td align="right">27.3</td> +<td align="right">7.1</td> +<td align="right">8.5</td> +<td align="right">2.1</td> +</tr> +<tr class="odd"> +<td align="right">8.7273979</td> +<td align="right">27.5</td> +<td align="right">7.5</td> +<td align="right">8.3</td> +<td align="right">2.3</td> +</tr> +<tr class="even"> +<td align="right">13.0910968</td> +<td align="right">14.8</td> +<td align="right">8.4</td> +<td align="right">9.3</td> +<td align="right">3.3</td> +</tr> +<tr class="odd"> +<td align="right">13.0910968</td> +<td align="right">13.4</td> +<td align="right">6.8</td> +<td align="right">8.7</td> +<td align="right">2.4</td> +</tr> +<tr class="even"> +<td align="right">13.0910968</td> +<td align="right">14.4</td> +<td align="right">8.0</td> +<td align="right">9.1</td> +<td align="right">2.6</td> +</tr> +<tr class="odd"> +<td align="right">17.4547957</td> +<td align="right">7.7</td> +<td align="right">7.2</td> +<td align="right">8.6</td> +<td align="right">4.0</td> +</tr> +<tr class="even"> +<td align="right">17.4547957</td> +<td align="right">7.3</td> +<td align="right">7.2</td> +<td align="right">8.5</td> +<td align="right">3.6</td> +</tr> +<tr class="odd"> +<td align="right">17.4547957</td> +<td align="right">8.1</td> +<td align="right">6.9</td> +<td align="right">8.9</td> +<td align="right">3.3</td> +</tr> +<tr class="even"> +<td align="right">26.1821936</td> +<td align="right">2.0</td> +<td align="right">4.9</td> +<td align="right">8.1</td> +<td align="right">2.1</td> +</tr> +<tr class="odd"> +<td align="right">26.1821936</td> +<td align="right">1.5</td> +<td align="right">4.3</td> +<td align="right">7.7</td> +<td align="right">1.7</td> +</tr> +<tr class="even"> +<td align="right">26.1821936</td> +<td align="right">1.9</td> +<td align="right">4.5</td> +<td align="right">7.4</td> +<td align="right">1.8</td> +</tr> +<tr class="odd"> +<td align="right">34.9095915</td> +<td align="right">1.3</td> +<td align="right">3.8</td> +<td align="right">5.9</td> +<td align="right">1.6</td> +</tr> +<tr class="even"> +<td align="right">34.9095915</td> +<td align="right">1.0</td> +<td align="right">3.1</td> +<td align="right">6.0</td> +<td align="right">1.6</td> +</tr> +<tr class="odd"> +<td align="right">34.9095915</td> +<td align="right">1.1</td> +<td align="right">3.1</td> +<td align="right">5.9</td> +<td align="right">1.4</td> +</tr> +<tr class="even"> +<td align="right">43.6369893</td> +<td align="right">0.9</td> +<td align="right">2.7</td> +<td align="right">5.6</td> +<td align="right">1.8</td> +</tr> +<tr class="odd"> +<td align="right">43.6369893</td> +<td align="right">0.7</td> +<td align="right">2.3</td> +<td align="right">5.2</td> +<td align="right">1.5</td> +</tr> +<tr class="even"> +<td align="right">43.6369893</td> +<td align="right">0.7</td> +<td align="right">2.1</td> +<td align="right">5.6</td> +<td align="right">1.3</td> +</tr> +<tr class="odd"> +<td align="right">52.3643872</td> +<td align="right">0.6</td> +<td align="right">1.6</td> +<td align="right">4.3</td> +<td align="right">1.2</td> +</tr> +<tr class="even"> +<td align="right">52.3643872</td> +<td align="right">0.4</td> +<td align="right">1.1</td> +<td align="right">3.7</td> +<td align="right">0.9</td> +</tr> +<tr class="odd"> +<td align="right">52.3643872</td> +<td align="right">0.5</td> +<td align="right">1.3</td> +<td align="right">3.9</td> +<td align="right">1.1</td> +</tr> +<tr class="even"> +<td align="right">74.8062674</td> +<td align="right">0.4</td> +<td align="right">0.4</td> +<td align="right">2.5</td> +<td align="right">0.5</td> +</tr> +<tr class="odd"> +<td align="right">74.8062674</td> +<td align="right">0.3</td> +<td align="right">0.4</td> +<td align="right">2.4</td> +<td align="right">0.5</td> +</tr> +<tr class="even"> +<td align="right">74.8062674</td> +<td align="right">0.3</td> +<td align="right">0.3</td> +<td align="right">2.2</td> +<td align="right">0.3</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset BBA 2.2</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +<th align="right">M23</th> +<th align="right">M27</th> +<th align="right">M31</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">98.09</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">0.0000000</td> +<td align="right">98.77</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">0.7678922</td> +<td align="right">93.52</td> +<td align="right">0.36</td> +<td align="right">0.42</td> +<td align="right">0.36</td> +</tr> +<tr class="even"> +<td align="right">0.7678922</td> +<td align="right">92.03</td> +<td align="right">0.40</td> +<td align="right">0.47</td> +<td align="right">0.33</td> +</tr> +<tr class="odd"> +<td align="right">2.3036765</td> +<td align="right">88.39</td> +<td align="right">1.03</td> +<td align="right">0.71</td> +<td align="right">0.55</td> +</tr> +<tr class="even"> +<td align="right">2.3036765</td> +<td align="right">87.18</td> +<td align="right">1.07</td> +<td align="right">0.82</td> +<td align="right">0.64</td> +</tr> +<tr class="odd"> +<td align="right">5.3752452</td> +<td align="right">69.38</td> +<td align="right">3.60</td> +<td align="right">2.19</td> +<td align="right">1.94</td> +</tr> +<tr class="even"> +<td align="right">5.3752452</td> +<td align="right">71.06</td> +<td align="right">3.66</td> +<td align="right">2.28</td> +<td align="right">1.62</td> +</tr> +<tr class="odd"> +<td align="right">10.7504904</td> +<td align="right">45.21</td> +<td align="right">6.97</td> +<td align="right">5.45</td> +<td align="right">4.22</td> +</tr> +<tr class="even"> +<td align="right">10.7504904</td> +<td align="right">46.81</td> +<td align="right">7.22</td> +<td align="right">5.19</td> +<td align="right">4.37</td> +</tr> +<tr class="odd"> +<td align="right">16.1257355</td> +<td align="right">30.54</td> +<td align="right">8.65</td> +<td align="right">8.81</td> +<td align="right">6.31</td> +</tr> +<tr class="even"> +<td align="right">16.1257355</td> +<td align="right">30.07</td> +<td align="right">8.38</td> +<td align="right">7.93</td> +<td align="right">6.85</td> +</tr> +<tr class="odd"> +<td align="right">21.5009807</td> +<td align="right">21.60</td> +<td align="right">9.10</td> +<td align="right">10.25</td> +<td align="right">7.05</td> +</tr> +<tr class="even"> +<td align="right">21.5009807</td> +<td align="right">20.41</td> +<td align="right">8.63</td> +<td align="right">10.77</td> +<td align="right">6.84</td> +</tr> +<tr class="odd"> +<td align="right">32.2514711</td> +<td align="right">9.10</td> +<td align="right">7.63</td> +<td align="right">10.89</td> +<td align="right">6.53</td> +</tr> +<tr class="even"> +<td align="right">32.2514711</td> +<td align="right">9.70</td> +<td align="right">8.01</td> +<td align="right">10.85</td> +<td align="right">7.11</td> +</tr> +<tr class="odd"> +<td align="right">43.0019614</td> +<td align="right">6.58</td> +<td align="right">6.40</td> +<td align="right">10.41</td> +<td align="right">6.06</td> +</tr> +<tr class="even"> +<td align="right">43.0019614</td> +<td align="right">6.31</td> +<td align="right">6.35</td> +<td align="right">10.35</td> +<td align="right">6.05</td> +</tr> +<tr class="odd"> +<td align="right">53.7524518</td> +<td align="right">3.47</td> +<td align="right">5.35</td> +<td align="right">9.92</td> +<td align="right">5.50</td> +</tr> +<tr class="even"> +<td align="right">53.7524518</td> +<td align="right">3.52</td> +<td align="right">5.06</td> +<td align="right">9.42</td> +<td align="right">5.07</td> +</tr> +<tr class="odd"> +<td align="right">64.5029421</td> +<td align="right">3.40</td> +<td align="right">5.14</td> +<td align="right">9.15</td> +<td align="right">4.94</td> +</tr> +<tr class="even"> +<td align="right">64.5029421</td> +<td align="right">3.67</td> +<td align="right">5.91</td> +<td align="right">9.25</td> +<td align="right">4.39</td> +</tr> +<tr class="odd"> +<td align="right">91.3791680</td> +<td align="right">1.62</td> +<td align="right">3.35</td> +<td align="right">7.14</td> +<td align="right">3.64</td> +</tr> +<tr class="even"> +<td align="right">91.3791680</td> +<td align="right">1.62</td> +<td align="right">2.87</td> +<td align="right">7.13</td> +<td align="right">3.55</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset BBA 2.3</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +<th align="right">M23</th> +<th align="right">M27</th> +<th align="right">M31</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">99.33</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">0.0000000</td> +<td align="right">97.44</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">0.6733938</td> +<td align="right">93.73</td> +<td align="right">0.18</td> +<td align="right">0.50</td> +<td align="right">0.47</td> +</tr> +<tr class="even"> +<td align="right">0.6733938</td> +<td align="right">93.77</td> +<td align="right">0.18</td> +<td align="right">0.83</td> +<td align="right">0.34</td> +</tr> +<tr class="odd"> +<td align="right">2.0201814</td> +<td align="right">87.84</td> +<td align="right">0.52</td> +<td align="right">1.25</td> +<td align="right">1.00</td> +</tr> +<tr class="even"> +<td align="right">2.0201814</td> +<td align="right">89.82</td> +<td align="right">0.43</td> +<td align="right">1.09</td> +<td align="right">0.89</td> +</tr> +<tr class="odd"> +<td align="right">4.7137565</td> +<td align="right">71.61</td> +<td align="right">1.19</td> +<td align="right">3.28</td> +<td align="right">3.58</td> +</tr> +<tr class="even"> +<td align="right">4.7137565</td> +<td align="right">71.42</td> +<td align="right">1.11</td> +<td align="right">3.24</td> +<td align="right">3.41</td> +</tr> +<tr class="odd"> +<td align="right">9.4275131</td> +<td align="right">45.60</td> +<td align="right">2.26</td> +<td align="right">7.17</td> +<td align="right">8.74</td> +</tr> +<tr class="even"> +<td align="right">9.4275131</td> +<td align="right">45.42</td> +<td align="right">1.99</td> +<td align="right">7.91</td> +<td align="right">8.28</td> +</tr> +<tr class="odd"> +<td align="right">14.1412696</td> +<td align="right">31.12</td> +<td align="right">2.81</td> +<td align="right">10.15</td> +<td align="right">9.67</td> +</tr> +<tr class="even"> +<td align="right">14.1412696</td> +<td align="right">31.68</td> +<td align="right">2.83</td> +<td align="right">9.55</td> +<td align="right">8.95</td> +</tr> +<tr class="odd"> +<td align="right">18.8550262</td> +<td align="right">23.20</td> +<td align="right">3.39</td> +<td align="right">12.09</td> +<td align="right">10.34</td> +</tr> +<tr class="even"> +<td align="right">18.8550262</td> +<td align="right">24.13</td> +<td align="right">3.56</td> +<td align="right">11.89</td> +<td align="right">10.00</td> +</tr> +<tr class="odd"> +<td align="right">28.2825393</td> +<td align="right">9.43</td> +<td align="right">3.49</td> +<td align="right">13.32</td> +<td align="right">7.89</td> +</tr> +<tr class="even"> +<td align="right">28.2825393</td> +<td align="right">9.82</td> +<td align="right">3.28</td> +<td align="right">12.05</td> +<td align="right">8.13</td> +</tr> +<tr class="odd"> +<td align="right">37.7100523</td> +<td align="right">7.08</td> +<td align="right">2.80</td> +<td align="right">10.04</td> +<td align="right">5.06</td> +</tr> +<tr class="even"> +<td align="right">37.7100523</td> +<td align="right">8.64</td> +<td align="right">2.97</td> +<td align="right">10.78</td> +<td align="right">5.54</td> +</tr> +<tr class="odd"> +<td align="right">47.1375654</td> +<td align="right">4.41</td> +<td align="right">2.42</td> +<td align="right">9.32</td> +<td align="right">3.79</td> +</tr> +<tr class="even"> +<td align="right">47.1375654</td> +<td align="right">4.78</td> +<td align="right">2.51</td> +<td align="right">9.62</td> +<td align="right">4.11</td> +</tr> +<tr class="odd"> +<td align="right">56.5650785</td> +<td align="right">4.92</td> +<td align="right">2.22</td> +<td align="right">8.00</td> +<td align="right">3.11</td> +</tr> +<tr class="even"> +<td align="right">56.5650785</td> +<td align="right">5.08</td> +<td align="right">1.95</td> +<td align="right">8.45</td> +<td align="right">2.98</td> +</tr> +<tr class="odd"> +<td align="right">80.1338612</td> +<td align="right">2.13</td> +<td align="right">1.28</td> +<td align="right">5.71</td> +<td align="right">1.78</td> +</tr> +<tr class="even"> +<td align="right">80.1338612</td> +<td align="right">2.23</td> +<td align="right">0.99</td> +<td align="right">3.33</td> +<td align="right">1.55</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Elliot</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +<th align="right">M23</th> +<th align="right">M27</th> +<th align="right">M31</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">97.5</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">100.7</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">1.228478</td> +<td align="right">86.4</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">1.228478</td> +<td align="right">88.5</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">1.5</td> +</tr> +<tr class="odd"> +<td align="right">3.685435</td> +<td align="right">69.8</td> +<td align="right">2.8</td> +<td align="right">2.3</td> +<td align="right">5.0</td> +</tr> +<tr class="even"> +<td align="right">3.685435</td> +<td align="right">77.1</td> +<td align="right">1.7</td> +<td align="right">2.1</td> +<td align="right">2.4</td> +</tr> +<tr class="odd"> +<td align="right">8.599349</td> +<td align="right">59.0</td> +<td align="right">4.3</td> +<td align="right">4.0</td> +<td align="right">4.3</td> +</tr> +<tr class="even"> +<td align="right">8.599349</td> +<td align="right">54.2</td> +<td align="right">5.8</td> +<td align="right">3.4</td> +<td align="right">5.0</td> +</tr> +<tr class="odd"> +<td align="right">17.198697</td> +<td align="right">31.3</td> +<td align="right">8.2</td> +<td align="right">6.6</td> +<td align="right">8.0</td> +</tr> +<tr class="even"> +<td align="right">17.198697</td> +<td align="right">33.5</td> +<td align="right">5.2</td> +<td align="right">6.9</td> +<td align="right">7.7</td> +</tr> +<tr class="odd"> +<td align="right">25.798046</td> +<td align="right">19.6</td> +<td align="right">5.1</td> +<td align="right">8.2</td> +<td align="right">7.8</td> +</tr> +<tr class="even"> +<td align="right">25.798046</td> +<td align="right">20.9</td> +<td align="right">6.1</td> +<td align="right">8.8</td> +<td align="right">6.5</td> +</tr> +<tr class="odd"> +<td align="right">34.397395</td> +<td align="right">13.3</td> +<td align="right">6.0</td> +<td align="right">9.7</td> +<td align="right">8.0</td> +</tr> +<tr class="even"> +<td align="right">34.397395</td> +<td align="right">15.8</td> +<td align="right">6.0</td> +<td align="right">8.8</td> +<td align="right">7.4</td> +</tr> +<tr class="odd"> +<td align="right">51.596092</td> +<td align="right">6.7</td> +<td align="right">5.0</td> +<td align="right">8.3</td> +<td align="right">6.9</td> +</tr> +<tr class="even"> +<td align="right">51.596092</td> +<td align="right">8.7</td> +<td align="right">4.2</td> +<td align="right">9.2</td> +<td align="right">9.0</td> +</tr> +<tr class="odd"> +<td align="right">68.794789</td> +<td align="right">8.8</td> +<td align="right">3.9</td> +<td align="right">9.3</td> +<td align="right">5.5</td> +</tr> +<tr class="even"> +<td align="right">68.794789</td> +<td align="right">8.7</td> +<td align="right">2.9</td> +<td align="right">8.5</td> +<td align="right">6.1</td> +</tr> +<tr class="odd"> +<td align="right">103.192184</td> +<td align="right">6.0</td> +<td align="right">1.9</td> +<td align="right">8.6</td> +<td align="right">6.1</td> +</tr> +<tr class="even"> +<td align="right">103.192184</td> +<td align="right">4.4</td> +<td align="right">1.5</td> +<td align="right">6.0</td> +<td align="right">4.0</td> +</tr> +<tr class="odd"> +<td align="right">146.188928</td> +<td align="right">3.3</td> +<td align="right">2.0</td> +<td align="right">5.6</td> +<td align="right">3.1</td> +</tr> +<tr class="even"> +<td align="right">146.188928</td> +<td align="right">2.8</td> +<td align="right">2.3</td> +<td align="right">4.5</td> +<td align="right">2.9</td> +</tr> +<tr class="odd"> +<td align="right">223.583066</td> +<td align="right">1.4</td> +<td align="right">1.2</td> +<td align="right">4.1</td> +<td align="right">1.8</td> +</tr> +<tr class="even"> +<td align="right">223.583066</td> +<td align="right">1.8</td> +<td align="right">1.9</td> +<td align="right">3.9</td> +<td align="right">2.6</td> +</tr> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">93.4</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">103.2</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">1.228478</td> +<td align="right">89.2</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">1.3</td> +</tr> +<tr class="even"> +<td align="right">1.228478</td> +<td align="right">86.6</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">3.685435</td> +<td align="right">78.2</td> +<td align="right">2.6</td> +<td align="right">1.0</td> +<td align="right">3.1</td> +</tr> +<tr class="even"> +<td align="right">3.685435</td> +<td align="right">78.1</td> +<td align="right">2.4</td> +<td align="right">2.6</td> +<td align="right">2.3</td> +</tr> +<tr class="odd"> +<td align="right">8.599349</td> +<td align="right">55.6</td> +<td align="right">5.5</td> +<td align="right">4.5</td> +<td align="right">3.4</td> +</tr> +<tr class="even"> +<td align="right">8.599349</td> +<td align="right">53.0</td> +<td align="right">5.6</td> +<td align="right">4.6</td> +<td align="right">4.3</td> +</tr> +<tr class="odd"> +<td align="right">17.198697</td> +<td align="right">33.7</td> +<td align="right">7.3</td> +<td align="right">7.6</td> +<td align="right">7.8</td> +</tr> +<tr class="even"> +<td align="right">17.198697</td> +<td align="right">33.2</td> +<td align="right">6.5</td> +<td align="right">6.7</td> +<td align="right">8.7</td> +</tr> +<tr class="odd"> +<td align="right">25.798046</td> +<td align="right">20.9</td> +<td align="right">5.8</td> +<td align="right">8.7</td> +<td align="right">7.7</td> +</tr> +<tr class="even"> +<td align="right">25.798046</td> +<td align="right">19.9</td> +<td align="right">7.7</td> +<td align="right">7.6</td> +<td align="right">6.5</td> +</tr> +<tr class="odd"> +<td align="right">34.397395</td> +<td align="right">18.2</td> +<td align="right">7.8</td> +<td align="right">8.0</td> +<td align="right">6.3</td> +</tr> +<tr class="even"> +<td align="right">34.397395</td> +<td align="right">12.7</td> +<td align="right">7.3</td> +<td align="right">8.6</td> +<td align="right">8.7</td> +</tr> +<tr class="odd"> +<td align="right">51.596092</td> +<td align="right">7.8</td> +<td align="right">7.0</td> +<td align="right">7.4</td> +<td align="right">5.7</td> +</tr> +<tr class="even"> +<td align="right">51.596092</td> +<td align="right">9.0</td> +<td align="right">6.3</td> +<td align="right">7.2</td> +<td align="right">4.2</td> +</tr> +<tr class="odd"> +<td align="right">68.794789</td> +<td align="right">11.4</td> +<td align="right">4.3</td> +<td align="right">10.3</td> +<td align="right">3.2</td> +</tr> +<tr class="even"> +<td align="right">68.794789</td> +<td align="right">9.0</td> +<td align="right">3.8</td> +<td align="right">9.4</td> +<td align="right">4.2</td> +</tr> +<tr class="odd"> +<td align="right">103.192184</td> +<td align="right">3.9</td> +<td align="right">2.6</td> +<td align="right">6.5</td> +<td align="right">3.8</td> +</tr> +<tr class="even"> +<td align="right">103.192184</td> +<td align="right">4.4</td> +<td align="right">2.8</td> +<td align="right">6.9</td> +<td align="right">4.0</td> +</tr> +<tr class="odd"> +<td align="right">146.188928</td> +<td align="right">2.6</td> +<td align="right">1.6</td> +<td align="right">4.6</td> +<td align="right">4.5</td> +</tr> +<tr class="even"> +<td align="right">146.188928</td> +<td align="right">3.4</td> +<td align="right">1.1</td> +<td align="right">4.5</td> +<td align="right">4.5</td> +</tr> +<tr class="odd"> +<td align="right">223.583066</td> +<td align="right">2.0</td> +<td align="right">1.4</td> +<td align="right">4.3</td> +<td align="right">3.8</td> +</tr> +<tr class="even"> +<td align="right">223.583066</td> +<td align="right">1.7</td> +<td align="right">1.3</td> +<td align="right">4.2</td> +<td align="right">2.3</td> +</tr> +</tbody> +</table> +</div> +<div class="section level2"> +<h2 id="separate-evaluations">Separate evaluations<a class="anchor" aria-label="anchor" href="#separate-evaluations"></a> +</h2> +<p>As a first step to obtain suitable starting parameters for the NLHM +fits, we do separate fits of several variants of the pathway model used +previously <span class="citation">(Ranke et al. 2021)</span>, varying +the kinetic model for the parent compound. Because the SFORB model often +provides faster convergence than the DFOP model, and can sometimes be +fitted where the DFOP model results in errors, it is included in the set +of parent models tested here.</p> +<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="kw">if</span> <span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.exists</a></span><span class="op">(</span><span class="st">"dmta_dlls"</span><span class="op">)</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.create</a></span><span class="op">(</span><span class="st">"dmta_dlls"</span><span class="op">)</span></span> +<span><span class="va">m_sfo_path_1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> M27 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> M31 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> name <span class="op">=</span> <span class="st">"m_sfo_path"</span>, dll_dir <span class="op">=</span> <span class="st">"dmta_dlls"</span>,</span> +<span> unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span></span> +<span><span class="op">)</span></span> +<span><span class="va">m_fomc_path_1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> M27 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> M31 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> name <span class="op">=</span> <span class="st">"m_fomc_path"</span>, dll_dir <span class="op">=</span> <span class="st">"dmta_dlls"</span>,</span> +<span> unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span></span> +<span><span class="op">)</span></span> +<span><span class="va">m_dfop_path_1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> M27 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> M31 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> name <span class="op">=</span> <span class="st">"m_dfop_path"</span>, dll_dir <span class="op">=</span> <span class="st">"dmta_dlls"</span>,</span> +<span> unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span></span> +<span><span class="op">)</span></span> +<span><span class="va">m_sforb_path_1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> M27 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> M31 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> name <span class="op">=</span> <span class="st">"m_sforb_path"</span>, dll_dir <span class="op">=</span> <span class="st">"dmta_dlls"</span>,</span> +<span> unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span></span> +<span><span class="op">)</span></span> +<span><span class="va">m_hs_path_1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"HS"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> M27 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> M31 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> name <span class="op">=</span> <span class="st">"m_hs_path"</span>, dll_dir <span class="op">=</span> <span class="st">"dmta_dlls"</span>,</span> +<span> unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span></span> +<span><span class="op">)</span></span> +<span><span class="va">deg_mods_1</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span> +<span> sfo_path_1 <span class="op">=</span> <span class="va">m_sfo_path_1</span>,</span> +<span> fomc_path_1 <span class="op">=</span> <span class="va">m_fomc_path_1</span>,</span> +<span> dfop_path_1 <span class="op">=</span> <span class="va">m_dfop_path_1</span>,</span> +<span> sforb_path_1 <span class="op">=</span> <span class="va">m_sforb_path_1</span>,</span> +<span> hs_path_1 <span class="op">=</span> <span class="va">m_hs_path_1</span><span class="op">)</span></span> +<span></span> +<span><span class="va">sep_1_const</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span></span> +<span> <span class="va">deg_mods_1</span>,</span> +<span> <span class="va">dmta_ds</span>,</span> +<span> error_model <span class="op">=</span> <span class="st">"const"</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span></span> +<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">sep_1_const</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">Calke</th> +<th align="left">Borstel</th> +<th align="left">Flaach</th> +<th align="left">BBA 2.2</th> +<th align="left">BBA 2.3</th> +<th align="left">Elliot</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">sfo_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">hs_path_1</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">C</td> +</tr> +</tbody> +</table> +<p>All separate pathway fits with SFO or FOMC for the parent and +constant variance converged (status OK). Most fits with DFOP or SFORB +for the parent converged as well. The fits with HS for the parent did +not converge with default settings.</p> +<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">sep_1_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">sep_1_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span> +<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">sep_1_tc</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">Calke</th> +<th align="left">Borstel</th> +<th align="left">Flaach</th> +<th align="left">BBA 2.2</th> +<th align="left">BBA 2.3</th> +<th align="left">Elliot</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">sfo_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_1</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">hs_path_1</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>With the two-component error model, the set of fits with convergence +problems is slightly different, with convergence problems appearing for +different data sets when applying the DFOP and SFORB model and some +additional convergence problems when using the FOMC model for the +parent.</p> +</div> +<div class="section level2"> +<h2 id="hierarchichal-model-fits">Hierarchichal model fits<a class="anchor" aria-label="anchor" href="#hierarchichal-model-fits"></a> +</h2> +<p>The following code fits two sets of the corresponding hierarchical +models to the data, one assuming constant variance, and one assuming +two-component error.</p> +<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">saem_1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">sep_1_const</span>, <span class="va">sep_1_tc</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<p>The run time for these fits was around two hours on five year old +hardware. After a recent hardware upgrade these fits complete in less +than twenty minutes.</p> +<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">sfo_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">hs_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>According to the <code>status</code> function, all fits terminated +successfully.</p> +<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div> +<pre><code>Warning in FUN(X[[i]], ...): Could not obtain log likelihood with 'is' method +for sforb_path_1 const</code></pre> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="right">npar</th> +<th align="right">AIC</th> +<th align="right">BIC</th> +<th align="right">Lik</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">sfo_path_1 const</td> +<td align="right">17</td> +<td align="right">2291.8</td> +<td align="right">2288.3</td> +<td align="right">-1128.9</td> +</tr> +<tr class="even"> +<td align="left">sfo_path_1 tc</td> +<td align="right">18</td> +<td align="right">2276.3</td> +<td align="right">2272.5</td> +<td align="right">-1120.1</td> +</tr> +<tr class="odd"> +<td align="left">fomc_path_1 const</td> +<td align="right">19</td> +<td align="right">2099.0</td> +<td align="right">2095.0</td> +<td align="right">-1030.5</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_1 tc</td> +<td align="right">20</td> +<td align="right">1939.6</td> +<td align="right">1935.5</td> +<td align="right">-949.8</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1 const</td> +<td align="right">21</td> +<td align="right">2038.8</td> +<td align="right">2034.4</td> +<td align="right">-998.4</td> +</tr> +<tr class="even"> +<td align="left">hs_path_1 const</td> +<td align="right">21</td> +<td align="right">2024.2</td> +<td align="right">2019.8</td> +<td align="right">-991.1</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1 tc</td> +<td align="right">22</td> +<td align="right">1879.8</td> +<td align="right">1875.2</td> +<td align="right">-917.9</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_1 tc</td> +<td align="right">22</td> +<td align="right">1832.9</td> +<td align="right">1828.3</td> +<td align="right">-894.4</td> +</tr> +<tr class="odd"> +<td align="left">hs_path_1 tc</td> +<td align="right">22</td> +<td align="right">1831.4</td> +<td align="right">1826.8</td> +<td align="right">-893.7</td> +</tr> +</tbody> +</table> +<p>When the goodness-of-fit of the models is compared, a warning is +obtained, indicating that the likelihood of the pathway fit with SFORB +for the parent compound and constant variance could not be calculated +with importance sampling (method ‘is’). As this is the default method on +which all AIC and BIC comparisons are based, this variant is not +included in the model comparison table. Comparing the goodness-of-fit of +the remaining models, HS model model with two-component error provides +the best fit. However, for batch experiments performed with constant +conditions such as the experiments evaluated here, there is no reason to +assume a discontinuity, so the SFORB model is preferable from a +mechanistic viewpoint. In addition, the information criteria AIC and BIC +are very similar for HS and SFORB. Therefore, the SFORB model is +selected here for further refinements.</p> +<div class="section level3"> +<h3 id="parameter-identifiability-based-on-the-fisher-information-matrix">Parameter identifiability based on the Fisher Information +Matrix<a class="anchor" aria-label="anchor" href="#parameter-identifiability-based-on-the-fisher-information-matrix"></a> +</h3> +<p>Using the <code>illparms</code> function, ill-defined statistical +model parameters such as standard deviations of the degradation +parameters in the population and error model parameters can be +found.</p> +<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">sfo_path_1</td> +<td align="left"></td> +<td align="left">sd(DMTA_0)</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_1</td> +<td align="left"></td> +<td align="left">sd(DMTA_0)</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1</td> +<td align="left"></td> +<td align="left"></td> +</tr> +<tr class="even"> +<td align="left">sforb_path_1</td> +<td align="left"></td> +<td align="left">sd(log_k_DMTA_bound_free)</td> +</tr> +<tr class="odd"> +<td align="left">hs_path_1</td> +<td align="left"></td> +<td align="left">sd(log_tb)</td> +</tr> +</tbody> +</table> +<p>When using constant variance, no ill-defined variance parameters are +identified with the <code>illparms</code> function in any of the +degradation models. When using the two-component error model, there is +one ill-defined variance parameter in all variants except for the +variant using DFOP for the parent compound.</p> +<p>For the selected combination of the SFORB pathway model with +two-component error, the random effect for the rate constant from +reversibly bound DMTA to the free DMTA (<code>k_DMTA_bound_free</code>) +is not well-defined. Therefore, the fit is updated without assuming a +random effect for this parameter.</p> +<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">saem_sforb_path_1_tc_reduced</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>,</span> +<span> no_random_effect <span class="op">=</span> <span class="st">"log_k_DMTA_bound_free"</span><span class="op">)</span></span> +<span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced</span><span class="op">)</span></span></code></pre></div> +<p>As expected, no ill-defined parameters remain. The model comparison +below shows that the reduced model is preferable.</p> +<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>, <span class="va">saem_sforb_path_1_tc_reduced</span><span class="op">)</span> <span class="op">|></span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="right">npar</th> +<th align="right">AIC</th> +<th align="right">BIC</th> +<th align="right">Lik</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">saem_sforb_path_1_tc_reduced</td> +<td align="right">21</td> +<td align="right">1830.3</td> +<td align="right">1825.9</td> +<td align="right">-894.2</td> +</tr> +<tr class="even"> +<td align="left">saem_1[[“sforb_path_1”, “tc”]]</td> +<td align="right">22</td> +<td align="right">1832.9</td> +<td align="right">1828.3</td> +<td align="right">-894.4</td> +</tr> +</tbody> +</table> +<p>The convergence plot of the refined fit is shown below.</p> +<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></code></pre></div> +<p><img src="2022_dmta_pathway_files/figure-html/saem-sforb-path-1-tc-reduced-convergence-1.png" width="700" style="display: block; margin: auto;"></p> +<p>For some parameters, for example for <code>f_DMTA_ilr_1</code> and +<code>f_DMTA_ilr_2</code>, i.e. for two of the parameters determining +the formation fractions of the parallel formation of the three +metabolites, some movement of the parameters is still visible in the +second phase of the algorithm. However, the amplitude of this movement +is in the range of the amplitude towards the end of the first phase. +Therefore, it is likely that an increase in iterations would not improve +the parameter estimates very much, and it is proposed that the fit is +acceptable. No numeric convergence criterion is implemented in +saemix.</p> +</div> +<div class="section level3"> +<h3 id="alternative-check-of-parameter-identifiability">Alternative check of parameter identifiability<a class="anchor" aria-label="anchor" href="#alternative-check-of-parameter-identifiability"></a> +</h3> +<p>As an alternative check of parameter identifiability <span class="citation">(Duchesne et al. 2021)</span>, multistart runs were +performed on the basis of the refined fit shown above.</p> +<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">saem_sforb_path_1_tc_reduced_multi</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced</span>,</span> +<span> n <span class="op">=</span> <span class="fl">32</span>, cores <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></code></pre></div> +<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced_multi</span><span class="op">)</span></span></code></pre></div> +<pre><code><multistart> object with 32 fits: + E OK +15 17 +OK: Fit terminated successfully +E: Error</code></pre> +<p>Out of the 32 fits that were initiated, only 17 terminated without an +error. The reason for this is that the wide variation of starting +parameters in combination with the parameter variation that is used in +the SAEM algorithm leads to parameter combinations for the degradation +model that the numerical integration routine cannot cope with. Because +of this variation of initial parameters, some of the model fits take up +to two times more time than the original fit.</p> +<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">12.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span> +<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced_multi</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-2-1.png" alt="Parameter boxplots for the multistart runs that succeeded" width="960"><p class="caption"> +Parameter boxplots for the multistart runs that succeeded +</p> +</div> +<p>However, visual analysis of the boxplot of the parameters obtained in +the successful fits confirms that the results are sufficiently +independent of the starting parameters, and there are no remaining +ill-defined parameters.</p> +</div> +</div> +<div class="section level2"> +<h2 id="plots-of-selected-fits">Plots of selected fits<a class="anchor" aria-label="anchor" href="#plots-of-selected-fits"></a> +</h2> +<p>The SFORB pathway fits with full and reduced parameter distribution +model are shown below.</p> +<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-3-1.png" alt="SFORB pathway fit with two-component error" width="700"><p class="caption"> +SFORB pathway fit with two-component error +</p> +</div> +<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-4-1.png" alt="SFORB pathway fit with two-component error, reduced parameter model" width="700"><p class="caption"> +SFORB pathway fit with two-component error, reduced parameter model +</p> +</div> +<p>Plots of the remaining fits and listings for all successful fits are +shown in the Appendix.</p> +</div> +<div class="section level2"> +<h2 id="conclusions">Conclusions<a class="anchor" aria-label="anchor" href="#conclusions"></a> +</h2> +<p>Pathway fits with SFO, FOMC, DFOP, SFORB and HS models for the parent +compound could be successfully performed.</p> +</div> +<div class="section level2"> +<h2 id="acknowledgements">Acknowledgements<a class="anchor" aria-label="anchor" href="#acknowledgements"></a> +</h2> +<p>The helpful comments by Janina Wöltjen of the German Environment +Agency on earlier versions of this document are gratefully +acknowledged.</p> +</div> +<div class="section level2"> +<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a> +</h2> +<div id="refs" class="references csl-bib-body hanging-indent"> +<div id="ref-duchesne_2021" class="csl-entry"> +Duchesne, Ronan, Anissa Guillemin, Olivier Gandrillon, and Fabien +Crauste. 2021. <span>“Practical Identifiability in the Frame of +Nonlinear Mixed Effects Models: The Example of the in Vitro +Erythropoiesis.”</span> <em>BMC Bioinformatics</em> 22 (478). <a href="https://doi.org/10.1186/s12859-021-04373-4" class="external-link">https://doi.org/10.1186/s12859-021-04373-4</a>. +</div> +<div id="ref-ranke2021" class="csl-entry"> +Ranke, Johannes, Janina Wöltjen, Jana Schmidt, and Emmanuelle Comets. +2021. <span>“Taking Kinetic Evaluations of Degradation Data to the Next +Level with Nonlinear Mixed-Effects Models.”</span> <em>Environments</em> +8 (8). <a href="https://doi.org/10.3390/environments8080071" class="external-link">https://doi.org/10.3390/environments8080071</a>. +</div> +</div> +</div> +<div class="section level2"> +<h2 id="appendix">Appendix<a class="anchor" aria-label="anchor" href="#appendix"></a> +</h2> +<div class="section level3"> +<h3 id="plots-of-hierarchical-fits-not-selected-for-refinement">Plots of hierarchical fits not selected for refinement<a class="anchor" aria-label="anchor" href="#plots-of-hierarchical-fits-not-selected-for-refinement"></a> +</h3> +<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"sfo_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-5-1.png" alt="SFO pathway fit with two-component error" width="700"><p class="caption"> +SFO pathway fit with two-component error +</p> +</div> +<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"fomc_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-6-1.png" alt="FOMC pathway fit with two-component error" width="700"><p class="caption"> +FOMC pathway fit with two-component error +</p> +</div> +<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-7-1.png" alt="HS pathway fit with two-component error" width="700"><p class="caption"> +HS pathway fit with two-component error +</p> +</div> +</div> +<div class="section level3"> +<h3 id="hierarchical-model-fit-listings">Hierarchical model fit listings<a class="anchor" aria-label="anchor" href="#hierarchical-model-fit-listings"></a> +</h3> +<div class="section level4"> +<h4 id="fits-with-random-effects-for-all-degradation-parameters">Fits with random effects for all degradation parameters<a class="anchor" aria-label="anchor" href="#fits-with-random-effects-for-all-degradation-parameters"></a> +</h4> + +</div> +<div class="section level4"> +<h4 id="improved-fit-of-the-sforb-pathway-model-with-two-component-error">Improved fit of the SFORB pathway model with two-component +error<a class="anchor" aria-label="anchor" href="#improved-fit-of-the-sforb-pathway-model-with-two-component-error"></a> +</h4> + +</div> +</div> +<div class="section level3"> +<h3 id="session-info">Session info<a class="anchor" aria-label="anchor" href="#session-info"></a> +</h3> +<pre><code>R version 4.2.2 Patched (2022-11-10 r83330) +Platform: x86_64-pc-linux-gnu (64-bit) +Running under: Debian GNU/Linux bookworm/sid + +Matrix products: default +BLAS: /usr/lib/x86_64-linux-gnu/openblas-serial/libblas.so.3 +LAPACK: /usr/lib/x86_64-linux-gnu/openblas-serial/libopenblas-r0.3.21.so + +locale: + [1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C + [3] LC_TIME=de_DE.UTF-8 LC_COLLATE=de_DE.UTF-8 + [5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=de_DE.UTF-8 + [7] LC_PAPER=de_DE.UTF-8 LC_NAME=C + [9] LC_ADDRESS=C LC_TELEPHONE=C +[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C + +attached base packages: +[1] parallel stats graphics grDevices utils datasets methods +[8] base + +other attached packages: +[1] saemix_3.2 npde_3.3 knitr_1.41 mkin_1.2.2 + +loaded via a namespace (and not attached): + [1] deSolve_1.34 zoo_1.8-11 tidyselect_1.2.0 xfun_0.35 + [5] bslib_0.4.2 purrr_1.0.0 lattice_0.20-45 colorspace_2.0-3 + [9] vctrs_0.5.1 generics_0.1.3 htmltools_0.5.4 yaml_2.3.6 +[13] pkgbuild_1.4.0 utf8_1.2.2 rlang_1.0.6 pkgdown_2.0.7 +[17] jquerylib_0.1.4 pillar_1.8.1 glue_1.6.2 DBI_1.1.3 +[21] lifecycle_1.0.3 stringr_1.5.0 munsell_0.5.0 gtable_0.3.1 +[25] ragg_1.2.4 codetools_0.2-18 memoise_2.0.1 evaluate_0.19 +[29] inline_0.3.19 callr_3.7.3 fastmap_1.1.0 ps_1.7.2 +[33] lmtest_0.9-40 fansi_1.0.3 highr_0.9 scales_1.2.1 +[37] cachem_1.0.6 desc_1.4.2 jsonlite_1.8.4 systemfonts_1.0.4 +[41] fs_1.5.2 textshaping_0.3.6 gridExtra_2.3 ggplot2_3.4.0 +[45] digest_0.6.31 stringi_1.7.8 processx_3.8.0 dplyr_1.0.10 +[49] grid_4.2.2 rprojroot_2.0.3 cli_3.5.0 tools_4.2.2 +[53] magrittr_2.0.3 sass_0.4.4 tibble_3.1.8 crayon_1.5.2 +[57] pkgconfig_2.0.3 prettyunits_1.1.1 assertthat_0.2.1 rmarkdown_2.19 +[61] R6_2.5.1 mclust_6.0.0 nlme_3.1-161 compiler_4.2.2 </code></pre> +</div> +<div class="section level3"> +<h3 id="hardware-info">Hardware info<a class="anchor" aria-label="anchor" href="#hardware-info"></a> +</h3> +<pre><code>CPU model: AMD Ryzen 9 7950X 16-Core Processor</code></pre> +<pre><code>MemTotal: 64940452 kB</code></pre> +</div> +</div> + </div> + + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + + <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2> + </nav> +</div> + +</div> + + + + <footer><div class="copyright"> + <p></p> +<p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> 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<header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> @@ -32,7 +34,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -42,7 +44,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -58,19 +60,28 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul> </li> @@ -80,7 +91,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> <li> - <a href="https://github.com/jranke/mkin/"> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> @@ -95,15 +106,15 @@ - </header><script src="twa_files/header-attrs-2.6/header-attrs.js"></script><script src="twa_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><script src="twa_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Calculation of time weighted average concentrations with mkin</h1> - <h4 class="author">Johannes Ranke</h4> + <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 class="date">Last change 18 September 2019 (rebuilt 2021-02-15)</h4> + <h4 data-toc-skip class="date">Last change 18 September 2019 (rebuilt 2022-11-24)</h4> - <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/twa.rmd"><code>vignettes/twa.rmd</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/twa.rmd" class="external-link"><code>vignettes/twa.rmd</code></a></small> <div class="hidden name"><code>twa.rmd</code></div> </div> @@ -141,10 +152,10 @@ <p><span class="math display">\[f_\textrm{twa} = \frac{1}{t} \left( \frac{1}{k_1} \left( 1 - e^{- k_1 t_b} \right) + \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)} \right) \right) \]</span></p> -<p>Note that a method for calculating maximum moving window time weighted average concentrations for a model fitted by ‘mkinfit’ or from parent decline model parameters is included in the <code><a href="../reference/max_twa_parent.html">max_twa_parent()</a></code> function. If the same is needed for metabolites, the function <code><a href="https://pkgdown.jrwb.de/pfm/reference/max_twa.html">pfm::max_twa()</a></code> from the ‘pfm’ package can be used.</p> +<p>Note that a method for calculating maximum moving window time weighted average concentrations for a model fitted by ‘mkinfit’ or from parent decline model parameters is included in the <code><a href="../reference/max_twa_parent.html">max_twa_parent()</a></code> function. If the same is needed for metabolites, the function <code><a href="https://pkgdown.jrwb.de/pfm/reference/max_twa.html" class="external-link">pfm::max_twa()</a></code> from the ‘pfm’ package can be used.</p> <div id="refs" class="references hanging-indent"> <div id="ref-FOCUSkinetics2014"> -<p>FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> +<p>FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> </div> </div> </div> @@ -158,11 +169,13 @@ <footer><div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <p></p> +<p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer> @@ -171,5 +184,7 @@ + + </body> </html> diff --git a/docs/dev/articles/web_only/FOCUS_Z.html b/docs/dev/articles/web_only/FOCUS_Z.html index 694b33ca..eec1ba66 100644 --- a/docs/dev/articles/web_only/FOCUS_Z.html +++ b/docs/dev/articles/web_only/FOCUS_Z.html @@ -20,6 +20,8 @@ <![endif]--> </head> <body data-spy="scroll" data-target="#toc"> + + <div class="container template-article"> <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> @@ -32,7 +34,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -42,7 +44,7 @@ <a href="../../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -58,19 +60,28 @@ <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul> </li> @@ -80,7 +91,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> <li> - <a href="https://github.com/jranke/mkin/"> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> @@ -95,280 +106,283 @@ - </header><script src="FOCUS_Z_files/header-attrs-2.6/header-attrs.js"></script><script src="FOCUS_Z_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><script src="FOCUS_Z_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Example evaluation of FOCUS dataset Z</h1> - <h4 class="author">Johannes Ranke</h4> + <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 class="date">Last change 16 January 2018 (rebuilt 2021-02-15)</h4> + <h4 data-toc-skip class="date">Last change 16 January 2018 (rebuilt 2022-11-24)</h4> - <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/web_only/FOCUS_Z.rmd"><code>vignettes/web_only/FOCUS_Z.rmd</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/FOCUS_Z.rmd" class="external-link"><code>vignettes/web_only/FOCUS_Z.rmd</code></a></small> <div class="hidden name"><code>FOCUS_Z.rmd</code></div> </div> -<p><a href="http://www.jrwb.de">Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany</a><br><a href="http://chem.uft.uni-bremen.de/ranke">Privatdozent at the University of Bremen</a></p> -<div id="the-data" class="section level1"> -<h1 class="hasAnchor"> -<a href="#the-data" class="anchor"></a>The data</h1> +<p><a href="http://www.jrwb.de" class="external-link">Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany</a><br><a href="http://chem.uft.uni-bremen.de/ranke" class="external-link">Privatdozent at the University of Bremen</a></p> +<div class="section level2"> +<h2 id="the-data">The data<a class="anchor" aria-label="anchor" href="#the-data"></a> +</h2> <p>The following code defines the example dataset from Appendix 7 to the FOCUS kinetics report <span class="citation">(FOCUS Work Group on Degradation Kinetics 2014, 354)</span>.</p> <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> -<span class="va">LOD</span> <span class="op">=</span> <span class="fl">0.5</span> -<span class="va">FOCUS_2006_Z</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span> - t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">0.04</span>, <span class="fl">0.125</span>, <span class="fl">0.29</span>, <span class="fl">0.54</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">7</span>, <span class="fl">10</span>, <span class="fl">14</span>, <span class="fl">21</span>, - <span class="fl">42</span>, <span class="fl">61</span>, <span class="fl">96</span>, <span class="fl">124</span><span class="op">)</span>, - Z0 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">100</span>, <span class="fl">81.7</span>, <span class="fl">70.4</span>, <span class="fl">51.1</span>, <span class="fl">41.2</span>, <span class="fl">6.6</span>, <span class="fl">4.6</span>, <span class="fl">3.9</span>, <span class="fl">4.6</span>, <span class="fl">4.3</span>, <span class="fl">6.8</span>, - <span class="fl">2.9</span>, <span class="fl">3.5</span>, <span class="fl">5.3</span>, <span class="fl">4.4</span>, <span class="fl">1.2</span>, <span class="fl">0.7</span><span class="op">)</span>, - Z1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">18.3</span>, <span class="fl">29.6</span>, <span class="fl">46.3</span>, <span class="fl">55.1</span>, <span class="fl">65.7</span>, <span class="fl">39.1</span>, <span class="fl">36</span>, <span class="fl">15.3</span>, <span class="fl">5.6</span>, <span class="fl">1.1</span>, - <span class="fl">1.6</span>, <span class="fl">0.6</span>, <span class="fl">0.5</span> <span class="op">*</span> <span class="va">LOD</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="cn">NA</span><span class="op">)</span>, - Z2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="cn">NA</span>, <span class="fl">0.5</span> <span class="op">*</span> <span class="va">LOD</span>, <span class="fl">2.6</span>, <span class="fl">3.8</span>, <span class="fl">15.3</span>, <span class="fl">37.2</span>, <span class="fl">31.7</span>, <span class="fl">35.6</span>, <span class="fl">14.5</span>, - <span class="fl">0.8</span>, <span class="fl">2.1</span>, <span class="fl">1.9</span>, <span class="fl">0.5</span> <span class="op">*</span> <span class="va">LOD</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="cn">NA</span><span class="op">)</span>, - Z3 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="fl">0.5</span> <span class="op">*</span> <span class="va">LOD</span>, <span class="fl">9.2</span>, <span class="fl">13.1</span>, <span class="fl">22.3</span>, <span class="fl">28.4</span>, <span class="fl">32.5</span>, - <span class="fl">25.2</span>, <span class="fl">17.2</span>, <span class="fl">4.8</span>, <span class="fl">4.5</span>, <span class="fl">2.8</span>, <span class="fl">4.4</span><span class="op">)</span><span class="op">)</span> - -<span class="va">FOCUS_2006_Z_mkin</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">FOCUS_2006_Z</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span><span class="va">LOD</span> <span class="op">=</span> <span class="fl">0.5</span></span> +<span><span class="va">FOCUS_2006_Z</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span> +<span> t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">0.04</span>, <span class="fl">0.125</span>, <span class="fl">0.29</span>, <span class="fl">0.54</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">7</span>, <span class="fl">10</span>, <span class="fl">14</span>, <span class="fl">21</span>,</span> +<span> <span class="fl">42</span>, <span class="fl">61</span>, <span class="fl">96</span>, <span class="fl">124</span><span class="op">)</span>,</span> +<span> Z0 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">100</span>, <span class="fl">81.7</span>, <span class="fl">70.4</span>, <span class="fl">51.1</span>, <span class="fl">41.2</span>, <span class="fl">6.6</span>, <span class="fl">4.6</span>, <span class="fl">3.9</span>, <span class="fl">4.6</span>, <span class="fl">4.3</span>, <span class="fl">6.8</span>,</span> +<span> <span class="fl">2.9</span>, <span class="fl">3.5</span>, <span class="fl">5.3</span>, <span class="fl">4.4</span>, <span class="fl">1.2</span>, <span class="fl">0.7</span><span class="op">)</span>,</span> +<span> Z1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">18.3</span>, <span class="fl">29.6</span>, <span class="fl">46.3</span>, <span class="fl">55.1</span>, <span class="fl">65.7</span>, <span class="fl">39.1</span>, <span class="fl">36</span>, <span class="fl">15.3</span>, <span class="fl">5.6</span>, <span class="fl">1.1</span>,</span> +<span> <span class="fl">1.6</span>, <span class="fl">0.6</span>, <span class="fl">0.5</span> <span class="op">*</span> <span class="va">LOD</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="cn">NA</span><span class="op">)</span>,</span> +<span> Z2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="cn">NA</span>, <span class="fl">0.5</span> <span class="op">*</span> <span class="va">LOD</span>, <span class="fl">2.6</span>, <span class="fl">3.8</span>, <span class="fl">15.3</span>, <span class="fl">37.2</span>, <span class="fl">31.7</span>, <span class="fl">35.6</span>, <span class="fl">14.5</span>,</span> +<span> <span class="fl">0.8</span>, <span class="fl">2.1</span>, <span class="fl">1.9</span>, <span class="fl">0.5</span> <span class="op">*</span> <span class="va">LOD</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="cn">NA</span><span class="op">)</span>,</span> +<span> Z3 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="cn">NA</span>, <span class="fl">0.5</span> <span class="op">*</span> <span class="va">LOD</span>, <span class="fl">9.2</span>, <span class="fl">13.1</span>, <span class="fl">22.3</span>, <span class="fl">28.4</span>, <span class="fl">32.5</span>,</span> +<span> <span class="fl">25.2</span>, <span class="fl">17.2</span>, <span class="fl">4.8</span>, <span class="fl">4.5</span>, <span class="fl">2.8</span>, <span class="fl">4.4</span><span class="op">)</span><span class="op">)</span></span> +<span></span> +<span><span class="va">FOCUS_2006_Z_mkin</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">FOCUS_2006_Z</span><span class="op">)</span></span></code></pre></div> </div> -<div id="parent-and-one-metabolite" class="section level1"> -<h1 class="hasAnchor"> -<a href="#parent-and-one-metabolite" class="anchor"></a>Parent and one metabolite</h1> +<div class="section level2"> +<h2 id="parent-and-one-metabolite">Parent and one metabolite<a class="anchor" aria-label="anchor" href="#parent-and-one-metabolite"></a> +</h2> <p>The next step is to set up the models used for the kinetic analysis. As the simultaneous fit of parent and the first metabolite is usually straightforward, Step 1 (SFO for parent only) is skipped here. We start with the model 2a, with formation and decline of metabolite Z1 and the pathway from parent directly to sink included (default in mkin).</p> <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">Z.2a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span><span class="op">)</span>, - Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></code></pre></div> -<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> +<code class="sourceCode R"><span><span class="va">Z.2a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span><span class="op">)</span>,</span> +<span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">m.Z.2a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.2a</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in mkinfit(Z.2a, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with -## value of zero were removed from the data</code></pre> +<code class="sourceCode R"><span><span class="va">m.Z.2a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.2a</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit(Z.2a, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span></span> +<span><span class="co">## value of zero were removed from the data</span></span></code></pre> <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.2a</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.2a</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png" width="700"></p> <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html">summary</a></span><span class="op">(</span><span class="va">m.Z.2a</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></code></pre></div> -<pre><code>## Estimate se_notrans t value Pr(>t) Lower Upper -## Z0_0 97.01488 3.301084 29.3888 3.2971e-21 91.66556 102.3642 -## k_Z0 2.23601 0.207078 10.7979 3.3309e-11 1.95303 2.5600 -## k_Z1 0.48212 0.063265 7.6207 2.8154e-08 0.40341 0.5762 -## f_Z0_to_Z1 1.00000 0.094764 10.5525 5.3560e-11 0.00000 1.0000 -## sigma 4.80411 0.635638 7.5579 3.2592e-08 3.52677 6.0815</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.2a</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></code></pre></div> +<pre><code><span><span class="co">## Estimate se_notrans t value Pr(>t) Lower Upper</span></span> +<span><span class="co">## Z0_0 97.01488 3.301084 29.3888 3.2971e-21 91.66556 102.3642</span></span> +<span><span class="co">## k_Z0 2.23601 0.207078 10.7979 3.3309e-11 1.95303 2.5600</span></span> +<span><span class="co">## k_Z1 0.48212 0.063265 7.6207 2.8154e-08 0.40341 0.5762</span></span> +<span><span class="co">## f_Z0_to_Z1 1.00000 0.094764 10.5525 5.3560e-11 0.00000 1.0000</span></span> +<span><span class="co">## sigma 4.80411 0.635638 7.5579 3.2592e-08 3.52677 6.0815</span></span></code></pre> <p>As obvious from the parameter summary (the component of the summary), the kinetic rate constant from parent compound Z to sink is very small and the t-test for this parameter suggests that it is not significantly different from zero. This suggests, in agreement with the analysis in the FOCUS kinetics report, to simplify the model by removing the pathway to sink.</p> <p>A similar result can be obtained when formation fractions are used in the model formulation:</p> <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">Z.2a.ff</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span><span class="op">)</span>, - Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, - use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></code></pre></div> -<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> +<code class="sourceCode R"><span><span class="va">Z.2a.ff</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span><span class="op">)</span>,</span> +<span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">m.Z.2a.ff</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.2a.ff</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in mkinfit(Z.2a.ff, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with -## value of zero were removed from the data</code></pre> +<code class="sourceCode R"><span><span class="va">m.Z.2a.ff</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.2a.ff</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit(Z.2a.ff, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span></span> +<span><span class="co">## value of zero were removed from the data</span></span></code></pre> <div class="sourceCode" id="cb13"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.2a.ff</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.2a.ff</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png" width="700"></p> <div class="sourceCode" id="cb14"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html">summary</a></span><span class="op">(</span><span class="va">m.Z.2a.ff</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></code></pre></div> -<pre><code>## Estimate se_notrans t value Pr(>t) Lower Upper -## Z0_0 97.01488 3.301084 29.3888 3.2971e-21 91.66556 102.3642 -## k_Z0 2.23601 0.207078 10.7979 3.3309e-11 1.95303 2.5600 -## k_Z1 0.48212 0.063265 7.6207 2.8154e-08 0.40341 0.5762 -## f_Z0_to_Z1 1.00000 0.094764 10.5525 5.3560e-11 0.00000 1.0000 -## sigma 4.80411 0.635638 7.5579 3.2592e-08 3.52677 6.0815</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.2a.ff</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></code></pre></div> +<pre><code><span><span class="co">## Estimate se_notrans t value Pr(>t) Lower Upper</span></span> +<span><span class="co">## Z0_0 97.01488 3.301084 29.3888 3.2971e-21 91.66556 102.3642</span></span> +<span><span class="co">## k_Z0 2.23601 0.207078 10.7979 3.3309e-11 1.95303 2.5600</span></span> +<span><span class="co">## k_Z1 0.48212 0.063265 7.6207 2.8154e-08 0.40341 0.5762</span></span> +<span><span class="co">## f_Z0_to_Z1 1.00000 0.094764 10.5525 5.3560e-11 0.00000 1.0000</span></span> +<span><span class="co">## sigma 4.80411 0.635638 7.5579 3.2592e-08 3.52677 6.0815</span></span></code></pre> <p>Here, the ilr transformed formation fraction fitted in the model takes a very large value, and the backtransformed formation fraction from parent Z to Z1 is practically unity. Here, the covariance matrix used for the calculation of confidence intervals is not returned as the model is overparameterised.</p> <p>A simplified model is obtained by removing the pathway to the sink. </p> <p>In the following, we use the parameterisation with formation fractions in order to be able to compare with the results in the FOCUS guidance, and as it makes it easier to use parameters obtained in a previous fit when adding a further metabolite.</p> <div class="sourceCode" id="cb16"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">Z.3</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></code></pre></div> -<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> +<code class="sourceCode R"><span><span class="va">Z.3</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <div class="sourceCode" id="cb18"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">m.Z.3</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.3</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in mkinfit(Z.3, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with -## value of zero were removed from the data</code></pre> +<code class="sourceCode R"><span><span class="va">m.Z.3</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.3</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit(Z.3, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span></span> +<span><span class="co">## value of zero were removed from the data</span></span></code></pre> <div class="sourceCode" id="cb20"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.3</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.3</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png" width="700"></p> <div class="sourceCode" id="cb21"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html">summary</a></span><span class="op">(</span><span class="va">m.Z.3</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></code></pre></div> -<pre><code>## Estimate se_notrans t value Pr(>t) Lower Upper -## Z0_0 97.01488 2.597342 37.352 2.0106e-24 91.67597 102.3538 -## k_Z0 2.23601 0.146904 15.221 9.1477e-15 1.95354 2.5593 -## k_Z1 0.48212 0.041727 11.554 4.8268e-12 0.40355 0.5760 -## sigma 4.80411 0.620208 7.746 1.6110e-08 3.52925 6.0790</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.3</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></code></pre></div> +<pre><code><span><span class="co">## Estimate se_notrans t value Pr(>t) Lower Upper</span></span> +<span><span class="co">## Z0_0 97.01488 2.597342 37.352 2.0106e-24 91.67597 102.3538</span></span> +<span><span class="co">## k_Z0 2.23601 0.146904 15.221 9.1477e-15 1.95354 2.5593</span></span> +<span><span class="co">## k_Z1 0.48212 0.041727 11.554 4.8268e-12 0.40355 0.5760</span></span> +<span><span class="co">## sigma 4.80411 0.620208 7.746 1.6110e-08 3.52925 6.0790</span></span></code></pre> <p>As there is only one transformation product for Z0 and no pathway to sink, the formation fraction is internally fixed to unity.</p> </div> -<div id="metabolites-z2-and-z3" class="section level1"> -<h1 class="hasAnchor"> -<a href="#metabolites-z2-and-z3" class="anchor"></a>Metabolites Z2 and Z3</h1> +<div class="section level2"> +<h2 id="metabolites-z2-and-z3">Metabolites Z2 and Z3<a class="anchor" aria-label="anchor" href="#metabolites-z2-and-z3"></a> +</h2> <p>As suggested in the FOCUS report, the pathway to sink was removed for metabolite Z1 as well in the next step. While this step appears questionable on the basis of the above results, it is followed here for the purpose of comparison. Also, in the FOCUS report, it is assumed that there is additional empirical evidence that Z1 quickly and exclusively hydrolyses to Z2.</p> <div class="sourceCode" id="cb23"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">Z.5</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></code></pre></div> -<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> +<code class="sourceCode R"><span><span class="va">Z.5</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <div class="sourceCode" id="cb25"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">m.Z.5</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.5</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in mkinfit(Z.5, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with -## value of zero were removed from the data</code></pre> +<code class="sourceCode R"><span><span class="va">m.Z.5</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.5</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit(Z.5, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span></span> +<span><span class="co">## value of zero were removed from the data</span></span></code></pre> <div class="sourceCode" id="cb27"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.5</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.5</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png" width="700"></p> <p>Finally, metabolite Z3 is added to the model. We use the optimised differential equation parameter values from the previous fit in order to accelerate the optimization.</p> <div class="sourceCode" id="cb28"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">Z.FOCUS</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z3"</span><span class="op">)</span>, - Z3 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, - use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></code></pre></div> -<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> +<code class="sourceCode R"><span><span class="va">Z.FOCUS</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z3"</span><span class="op">)</span>,</span> +<span> Z3 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <div class="sourceCode" id="cb30"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">m.Z.FOCUS</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.FOCUS</span>, <span class="va">FOCUS_2006_Z_mkin</span>, - parms.ini <span class="op">=</span> <span class="va">m.Z.5</span><span class="op">$</span><span class="va">bparms.ode</span>, - quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, parms.ini = m.Z.5$bparms.ode, : -## Observations with value of zero were removed from the data</code></pre> -<pre><code>## Warning in mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, parms.ini = m.Z.5$bparms.ode, : Optimisation did not converge: -## false convergence (8)</code></pre> +<code class="sourceCode R"><span><span class="va">m.Z.FOCUS</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.FOCUS</span>, <span class="va">FOCUS_2006_Z_mkin</span>,</span> +<span> parms.ini <span class="op">=</span> <span class="va">m.Z.5</span><span class="op">$</span><span class="va">bparms.ode</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, parms.ini = m.Z.5$bparms.ode, :</span></span> +<span><span class="co">## Observations with value of zero were removed from the data</span></span></code></pre> +<pre><code><span><span class="co">## Warning in mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, parms.ini = m.Z.5$bparms.ode, : Optimisation did not converge:</span></span> +<span><span class="co">## false convergence (8)</span></span></code></pre> <div class="sourceCode" id="cb33"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.FOCUS</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.FOCUS</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png" width="700"></p> <div class="sourceCode" id="cb34"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html">summary</a></span><span class="op">(</span><span class="va">m.Z.FOCUS</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></code></pre></div> -<pre><code>## Estimate se_notrans t value Pr(>t) Lower Upper -## Z0_0 96.838822 1.994274 48.5584 4.0280e-42 92.826981 100.850664 -## k_Z0 2.215393 0.118458 18.7019 1.0413e-23 1.989456 2.466989 -## k_Z1 0.478305 0.028258 16.9266 6.2418e-22 0.424708 0.538666 -## k_Z2 0.451627 0.042139 10.7176 1.6314e-14 0.374339 0.544872 -## k_Z3 0.058692 0.015245 3.8499 1.7803e-04 0.034808 0.098965 -## f_Z2_to_Z3 0.471502 0.058351 8.0805 9.6608e-11 0.357769 0.588274 -## sigma 3.984431 0.383402 10.3923 4.5575e-14 3.213126 4.755736</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.FOCUS</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></code></pre></div> +<pre><code><span><span class="co">## Estimate se_notrans t value Pr(>t) Lower Upper</span></span> +<span><span class="co">## Z0_0 96.838822 1.994274 48.5584 4.0280e-42 92.826981 100.850664</span></span> +<span><span class="co">## k_Z0 2.215393 0.118458 18.7019 1.0413e-23 1.989456 2.466989</span></span> +<span><span class="co">## k_Z1 0.478305 0.028258 16.9266 6.2418e-22 0.424708 0.538666</span></span> +<span><span class="co">## k_Z2 0.451627 0.042139 10.7176 1.6314e-14 0.374339 0.544872</span></span> +<span><span class="co">## k_Z3 0.058692 0.015245 3.8499 1.7803e-04 0.034808 0.098965</span></span> +<span><span class="co">## f_Z2_to_Z3 0.471502 0.058351 8.0805 9.6608e-11 0.357769 0.588274</span></span> +<span><span class="co">## sigma 3.984431 0.383402 10.3923 4.5575e-14 3.213126 4.755736</span></span></code></pre> <div class="sourceCode" id="cb36"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">m.Z.FOCUS</span><span class="op">)</span></code></pre></div> -<pre><code>## $ff -## Z2_Z3 Z2_sink -## 0.4715 0.5285 -## -## $distimes -## DT50 DT90 -## Z0 0.31288 1.0394 -## Z1 1.44917 4.8141 -## Z2 1.53478 5.0984 -## Z3 11.80986 39.2315</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">m.Z.FOCUS</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## $ff</span></span> +<span><span class="co">## Z2_Z3 Z2_sink </span></span> +<span><span class="co">## 0.4715 0.5285 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $distimes</span></span> +<span><span class="co">## DT50 DT90</span></span> +<span><span class="co">## Z0 0.31288 1.0394</span></span> +<span><span class="co">## Z1 1.44917 4.8141</span></span> +<span><span class="co">## Z2 1.53478 5.0984</span></span> +<span><span class="co">## Z3 11.80986 39.2315</span></span></code></pre> <p>This fit corresponds to the final result chosen in Appendix 7 of the FOCUS report. Confidence intervals returned by mkin are based on internally transformed parameters, however.</p> </div> -<div id="using-the-sforb-model" class="section level1"> -<h1 class="hasAnchor"> -<a href="#using-the-sforb-model" class="anchor"></a>Using the SFORB model</h1> +<div class="section level2"> +<h2 id="using-the-sforb-model">Using the SFORB model<a class="anchor" aria-label="anchor" href="#using-the-sforb-model"></a> +</h2> <p>As the FOCUS report states, there is a certain tailing of the time course of metabolite Z3. Also, the time course of the parent compound is not fitted very well using the SFO model, as residues at a certain low level remain.</p> <p>Therefore, an additional model is offered here, using the single first-order reversible binding (SFORB) model for metabolite Z3. As expected, the <span class="math inline">\(\chi^2\)</span> error level is lower for metabolite Z3 using this model and the graphical fit for Z3 is improved. However, the covariance matrix is not returned.</p> <div class="sourceCode" id="cb38"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">Z.mkin.1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z3"</span><span class="op">)</span>, - Z3 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span><span class="op">)</span><span class="op">)</span></code></pre></div> -<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> +<code class="sourceCode R"><span><span class="va">Z.mkin.1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z3"</span><span class="op">)</span>,</span> +<span> Z3 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <div class="sourceCode" id="cb40"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">m.Z.mkin.1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.1</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with -## value of zero were removed from the data</code></pre> +<code class="sourceCode R"><span><span class="va">m.Z.mkin.1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.1</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span></span> +<span><span class="co">## value of zero were removed from the data</span></span></code></pre> <div class="sourceCode" id="cb42"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.1</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.1</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png" width="700"></p> <div class="sourceCode" id="cb43"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html">summary</a></span><span class="op">(</span><span class="va">m.Z.mkin.1</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">cov.unscaled</span></code></pre></div> -<pre><code>## NULL</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.mkin.1</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">cov.unscaled</span></span></code></pre></div> +<pre><code><span><span class="co">## NULL</span></span></code></pre> <p>Therefore, a further stepwise model building is performed starting from the stage of parent and two metabolites, starting from the assumption that the model fit for the parent compound can be improved by using the SFORB model.</p> <div class="sourceCode" id="cb45"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">Z.mkin.3</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></code></pre></div> -<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> +<code class="sourceCode R"><span><span class="va">Z.mkin.3</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <div class="sourceCode" id="cb47"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">m.Z.mkin.3</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.3</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with -## value of zero were removed from the data</code></pre> +<code class="sourceCode R"><span><span class="va">m.Z.mkin.3</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.3</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span></span> +<span><span class="co">## value of zero were removed from the data</span></span></code></pre> <div class="sourceCode" id="cb49"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.3</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.3</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png" width="700"></p> <p>This results in a much better representation of the behaviour of the parent compound Z0.</p> <p>Finally, Z3 is added as well. These models appear overparameterised (no covariance matrix returned) if the sink for Z1 is left in the models.</p> <div class="sourceCode" id="cb50"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">Z.mkin.4</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z3"</span><span class="op">)</span>, - Z3 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></code></pre></div> -<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> +<code class="sourceCode R"><span><span class="va">Z.mkin.4</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z3"</span><span class="op">)</span>,</span> +<span> Z3 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <div class="sourceCode" id="cb52"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">m.Z.mkin.4</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.4</span>, <span class="va">FOCUS_2006_Z_mkin</span>, - parms.ini <span class="op">=</span> <span class="va">m.Z.mkin.3</span><span class="op">$</span><span class="va">bparms.ode</span>, - quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin, parms.ini = m.Z.mkin. -## 3$bparms.ode, : Observations with value of zero were removed from the data</code></pre> +<code class="sourceCode R"><span><span class="va">m.Z.mkin.4</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.4</span>, <span class="va">FOCUS_2006_Z_mkin</span>,</span> +<span> parms.ini <span class="op">=</span> <span class="va">m.Z.mkin.3</span><span class="op">$</span><span class="va">bparms.ode</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin, parms.ini =</span></span> +<span><span class="co">## m.Z.mkin.3$bparms.ode, : Observations with value of zero were removed from the</span></span> +<span><span class="co">## data</span></span></code></pre> <div class="sourceCode" id="cb54"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.4</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.4</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png" width="700"></p> <p>The error level of the fit, but especially of metabolite Z3, can be improved if the SFORB model is chosen for this metabolite, as this model is capable of representing the tailing of the metabolite decline phase.</p> <div class="sourceCode" id="cb55"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">Z.mkin.5</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>, - Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z3"</span><span class="op">)</span>, - Z3 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span><span class="op">)</span><span class="op">)</span></code></pre></div> -<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> +<code class="sourceCode R"><span><span class="va">Z.mkin.5</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> Z2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z3"</span><span class="op">)</span>,</span> +<span> Z3 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <div class="sourceCode" id="cb57"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">m.Z.mkin.5</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.5</span>, <span class="va">FOCUS_2006_Z_mkin</span>, - parms.ini <span class="op">=</span> <span class="va">m.Z.mkin.4</span><span class="op">$</span><span class="va">bparms.ode</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">4</span><span class="op">]</span>, - quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini = m.Z.mkin. -## 4$bparms.ode[1:4], : Observations with value of zero were removed from the data</code></pre> +<code class="sourceCode R"><span><span class="va">m.Z.mkin.5</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.5</span>, <span class="va">FOCUS_2006_Z_mkin</span>,</span> +<span> parms.ini <span class="op">=</span> <span class="va">m.Z.mkin.4</span><span class="op">$</span><span class="va">bparms.ode</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">4</span><span class="op">]</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini =</span></span> +<span><span class="co">## m.Z.mkin.4$bparms.ode[1:4], : Observations with value of zero were removed from</span></span> +<span><span class="co">## the data</span></span></code></pre> <div class="sourceCode" id="cb59"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.5</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.5</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png" width="700"></p> <p>The summary view of the backtransformed parameters shows that we get no confidence intervals due to overparameterisation. As the optimized is excessively small, it seems reasonable to fix it to zero.</p> <div class="sourceCode" id="cb60"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">m.Z.mkin.5a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.5</span>, <span class="va">FOCUS_2006_Z_mkin</span>, - parms.ini <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">m.Z.mkin.5</span><span class="op">$</span><span class="va">bparms.ode</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span><span class="op">]</span>, - k_Z3_bound_free <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, - fixed_parms <span class="op">=</span> <span class="st">"k_Z3_bound_free"</span>, - quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini = c(m.Z.mkin. -## 5$bparms.ode[1:7], : Observations with value of zero were removed from the data</code></pre> +<code class="sourceCode R"><span><span class="va">m.Z.mkin.5a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.5</span>, <span class="va">FOCUS_2006_Z_mkin</span>,</span> +<span> parms.ini <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">m.Z.mkin.5</span><span class="op">$</span><span class="va">bparms.ode</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span><span class="op">]</span>,</span> +<span> k_Z3_bound_free <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span> +<span> fixed_parms <span class="op">=</span> <span class="st">"k_Z3_bound_free"</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini =</span></span> +<span><span class="co">## c(m.Z.mkin.5$bparms.ode[1:7], : Observations with value of zero were removed</span></span> +<span><span class="co">## from the data</span></span></code></pre> <div class="sourceCode" id="cb62"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.5a</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.5a</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png" width="700"></p> <p>As expected, the residual plots for Z0 and Z3 are more random than in the case of the all SFO model for which they were shown above. In conclusion, the model is proposed as the best-fit model for the dataset from Appendix 7 of the FOCUS report.</p> <p>A graphical representation of the confidence intervals can finally be obtained.</p> <div class="sourceCode" id="cb63"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/mkinparplot.html">mkinparplot</a></span><span class="op">(</span><span class="va">m.Z.mkin.5a</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/mkinparplot.html">mkinparplot</a></span><span class="op">(</span><span class="va">m.Z.mkin.5a</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png" width="700"></p> <p>The endpoints obtained with this model are</p> <div class="sourceCode" id="cb64"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">m.Z.mkin.5a</span><span class="op">)</span></code></pre></div> -<pre><code>## $ff -## Z0_free Z2_Z3 Z2_sink Z3_free -## 1.00000 0.53656 0.46344 1.00000 -## -## $SFORB -## Z0_b1 Z0_b2 Z3_b1 Z3_b2 -## 2.4471322 0.0075125 0.0800069 0.0000000 -## -## $distimes -## DT50 DT90 DT50back DT50_Z0_b1 DT50_Z0_b2 DT50_Z3_b1 DT50_Z3_b2 -## Z0 0.3043 1.1848 0.35666 0.28325 92.266 NA NA -## Z1 1.5148 5.0320 NA NA NA NA NA -## Z2 1.6414 5.4526 NA NA NA NA NA -## Z3 NA NA NA NA NA 8.6636 Inf</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">m.Z.mkin.5a</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## $ff</span></span> +<span><span class="co">## Z0_free Z2_Z3 Z2_sink Z3_free </span></span> +<span><span class="co">## 1.00000 0.53656 0.46344 1.00000 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $SFORB</span></span> +<span><span class="co">## Z0_b1 Z0_b2 Z0_g Z3_b1 Z3_b2 Z3_g </span></span> +<span><span class="co">## 2.4471322 0.0075125 0.9519862 0.0800069 0.0000000 0.9347820 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $distimes</span></span> +<span><span class="co">## DT50 DT90 DT50back DT50_Z0_b1 DT50_Z0_b2 DT50_Z3_b1 DT50_Z3_b2</span></span> +<span><span class="co">## Z0 0.3043 1.1848 0.35666 0.28325 92.266 NA NA</span></span> +<span><span class="co">## Z1 1.5148 5.0320 NA NA NA NA NA</span></span> +<span><span class="co">## Z2 1.6414 5.4526 NA NA NA NA NA</span></span> +<span><span class="co">## Z3 NA NA NA NA NA 8.6636 Inf</span></span></code></pre> <p>It is clear the degradation rate of Z3 towards the end of the experiment is very low as DT50_Z3_b2 (the second Eigenvalue of the system of two differential equations representing the SFORB system for Z3, corresponding to the slower rate constant of the DFOP model) is reported to be infinity. However, this appears to be a feature of the data.</p> </div> -<div id="references" class="section level1"> -<h1 class="hasAnchor"> -<a href="#references" class="anchor"></a>References</h1> +<div class="section level2"> +<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a> +</h2> <!-- vim: set foldmethod=syntax: --> <div id="refs" class="references hanging-indent"> <div id="ref-FOCUSkinetics2014"> -<p>FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> +<p>FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> </div> </div> </div> @@ -385,11 +399,13 @@ <footer><div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <p></p> +<p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer> @@ -398,5 +414,7 @@ + + </body> </html> diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png Binary files differindex 2213c446..be652d31 100644 --- a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png +++ b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png diff 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a/docs/dev/articles/web_only/NAFTA_examples.html b/docs/dev/articles/web_only/NAFTA_examples.html index b9784415..a054d4a1 100644 --- a/docs/dev/articles/web_only/NAFTA_examples.html +++ b/docs/dev/articles/web_only/NAFTA_examples.html @@ -20,6 +20,8 @@ <![endif]--> </head> <body data-spy="scroll" data-target="#toc"> + + <div class="container template-article"> <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> @@ -32,7 +34,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -42,7 +44,7 @@ <a href="../../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -58,19 +60,28 @@ <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul> </li> @@ -80,7 +91,7 @@ </ul> <ul class="nav navbar-nav navbar-right"> <li> - <a href="https://github.com/jranke/mkin/"> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> @@ -95,908 +106,908 @@ - </header><script src="NAFTA_examples_files/header-attrs-2.6/header-attrs.js"></script><script src="NAFTA_examples_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><script src="NAFTA_examples_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance</h1> - <h4 class="author">Johannes Ranke</h4> + <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 class="date">26 February 2019 (rebuilt 2021-02-15)</h4> + <h4 data-toc-skip class="date">26 February 2019 (rebuilt 2022-11-24)</h4> - <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/web_only/NAFTA_examples.rmd"><code>vignettes/web_only/NAFTA_examples.rmd</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/NAFTA_examples.rmd" class="external-link"><code>vignettes/web_only/NAFTA_examples.rmd</code></a></small> <div class="hidden name"><code>NAFTA_examples.rmd</code></div> </div> -<div id="introduction" class="section level1"> -<h1 class="hasAnchor"> -<a href="#introduction" class="anchor"></a>Introduction</h1> +<div class="section level2"> +<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a> +</h2> <p>In this document, the example evaluations provided in Attachment 1 to the SOP of US EPA for using the NAFTA guidance <span class="citation">(US EPA 2015)</span> are repeated using mkin. The original evaluations reported in the attachment were performed using PestDF in version 0.8.4. Note that PestDF 0.8.13 is the version distributed at the US EPA website today (2019-02-26).</p> <p>The datasets are now distributed with the mkin package.</p> </div> -<div id="examples-where-dfop-did-not-converge-with-pestdf-0-8-4" class="section level1"> -<h1 class="hasAnchor"> -<a href="#examples-where-dfop-did-not-converge-with-pestdf-0-8-4" class="anchor"></a>Examples where DFOP did not converge with PestDF 0.8.4</h1> +<div class="section level2"> +<h2 id="examples-where-dfop-did-not-converge-with-pestdf-0-8-4">Examples where DFOP did not converge with PestDF 0.8.4<a class="anchor" aria-label="anchor" href="#examples-where-dfop-did-not-converge-with-pestdf-0-8-4"></a> +</h2> <p>In attachment 1, it is reported that the DFOP model does not converge for these datasets when PestDF 0.8.4 was used. For all four datasets, the DFOP model can be fitted with mkin (see below). The negative half-life given by PestDF 0.8.4 for these fits appears to be the result of a bug. The results for the other two models (SFO and IORE) are the same.</p> -<div id="example-on-page-5-upper-panel" class="section level2"> -<h2 class="hasAnchor"> -<a href="#example-on-page-5-upper-panel" class="anchor"></a>Example on page 5, upper panel</h2> +<div class="section level3"> +<h3 id="example-on-page-5-upper-panel">Example on page 5, upper panel<a class="anchor" aria-label="anchor" href="#example-on-page-5-upper-panel"></a> +</h3> <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p5a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p5a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> -<pre><code>## The half-life obtained from the IORE model may be used</code></pre> +<code class="sourceCode R"><span><span class="va">p5a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p5a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">p5a</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p5a</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p5a-1.png" width="700"></p> <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p5a</span><span class="op">)</span></code></pre></div> -<pre><code>## Sums of squares: -## SFO IORE DFOP -## 465.21753 56.27506 32.06401 -## -## Critical sum of squares for checking the SFO model: -## [1] 64.4304 -## -## Parameters: -## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 95.8401 4.67e-21 92.245 99.4357 -## k_parent 0.0102 3.92e-12 0.009 0.0117 -## sigma 4.8230 3.81e-06 3.214 6.4318 -## -## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 1.01e+02 NA 9.91e+01 1.02e+02 -## k__iore_parent 1.54e-05 NA 4.08e-06 5.84e-05 -## N_parent 2.57e+00 NA 2.25e+00 2.89e+00 -## sigma 1.68e+00 NA 1.12e+00 2.24e+00 -## -## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 9.99e+01 1.41e-26 98.8116 101.0810 -## k1 2.67e-02 5.05e-06 0.0243 0.0295 -## k2 2.26e-12 5.00e-01 0.0000 Inf -## g 6.47e-01 3.67e-06 0.6248 0.6677 -## sigma 1.27e+00 8.91e-06 0.8395 1.6929 -## -## -## DTx values: -## DT50 DT90 DT50_rep -## SFO 67.7 2.25e+02 6.77e+01 -## IORE 58.2 1.07e+03 3.22e+02 -## DFOP 55.5 5.59e+11 3.07e+11 -## -## Representative half-life: -## [1] 321.51</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p5a</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 465.21753 56.27506 32.06401 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 64.4304</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 95.8401 4.67e-21 92.245 99.4357</span></span> +<span><span class="co">## k_parent 0.0102 3.92e-12 0.009 0.0117</span></span> +<span><span class="co">## sigma 4.8230 3.81e-06 3.214 6.4318</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 1.01e+02 NA 9.91e+01 1.02e+02</span></span> +<span><span class="co">## k__iore_parent 1.54e-05 NA 4.08e-06 5.84e-05</span></span> +<span><span class="co">## N_parent 2.57e+00 NA 2.25e+00 2.89e+00</span></span> +<span><span class="co">## sigma 1.68e+00 NA 1.12e+00 2.24e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 9.99e+01 1.41e-26 98.8116 101.0810</span></span> +<span><span class="co">## k1 2.67e-02 5.05e-06 0.0243 0.0295</span></span> +<span><span class="co">## k2 2.26e-12 5.00e-01 0.0000 Inf</span></span> +<span><span class="co">## g 6.47e-01 3.67e-06 0.6248 0.6677</span></span> +<span><span class="co">## sigma 1.27e+00 8.91e-06 0.8395 1.6929</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 67.7 2.25e+02 6.77e+01</span></span> +<span><span class="co">## IORE 58.2 1.07e+03 3.22e+02</span></span> +<span><span class="co">## DFOP 55.5 5.59e+11 3.07e+11</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 321.51</span></span></code></pre> </div> -<div id="example-on-page-5-lower-panel" class="section level2"> -<h2 class="hasAnchor"> -<a href="#example-on-page-5-lower-panel" class="anchor"></a>Example on page 5, lower panel</h2> +<div class="section level3"> +<h3 id="example-on-page-5-lower-panel">Example on page 5, lower panel<a class="anchor" aria-label="anchor" href="#example-on-page-5-lower-panel"></a> +</h3> <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p5b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p5b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> -<pre><code>## The half-life obtained from the IORE model may be used</code></pre> +<code class="sourceCode R"><span><span class="va">p5b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p5b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">p5b</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p5b</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p5b-1.png" width="700"></p> <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p5b</span><span class="op">)</span></code></pre></div> -<pre><code>## Sums of squares: -## SFO IORE DFOP -## 94.81123 10.10936 7.55871 -## -## Critical sum of squares for checking the SFO model: -## [1] 11.77879 -## -## Parameters: -## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 96.497 2.32e-24 94.85271 98.14155 -## k_parent 0.008 3.42e-14 0.00737 0.00869 -## sigma 2.295 1.22e-05 1.47976 3.11036 -## -## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 9.85e+01 1.17e-28 9.79e+01 9.92e+01 -## k__iore_parent 1.53e-04 6.50e-03 7.21e-05 3.26e-04 -## N_parent 1.94e+00 5.88e-13 1.76e+00 2.12e+00 -## sigma 7.49e-01 1.63e-05 4.82e-01 1.02e+00 -## -## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 9.84e+01 1.24e-27 97.8078 98.9187 -## k1 1.55e-02 4.10e-04 0.0143 0.0167 -## k2 8.63e-12 5.00e-01 0.0000 Inf -## g 6.89e-01 2.92e-03 0.6626 0.7142 -## sigma 6.48e-01 2.38e-05 0.4147 0.8813 -## -## -## DTx values: -## DT50 DT90 DT50_rep -## SFO 86.6 2.88e+02 8.66e+01 -## IORE 85.5 7.17e+02 2.16e+02 -## DFOP 83.6 1.32e+11 8.04e+10 -## -## Representative half-life: -## [1] 215.87</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p5b</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 94.81123 10.10936 7.55871 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 11.77879</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 96.497 2.32e-24 94.85271 98.14155</span></span> +<span><span class="co">## k_parent 0.008 3.42e-14 0.00737 0.00869</span></span> +<span><span class="co">## sigma 2.295 1.22e-05 1.47976 3.11036</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 9.85e+01 1.17e-28 9.79e+01 9.92e+01</span></span> +<span><span class="co">## k__iore_parent 1.53e-04 6.50e-03 7.21e-05 3.26e-04</span></span> +<span><span class="co">## N_parent 1.94e+00 5.88e-13 1.76e+00 2.12e+00</span></span> +<span><span class="co">## sigma 7.49e-01 1.63e-05 4.82e-01 1.02e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 9.84e+01 1.24e-27 97.8078 98.9187</span></span> +<span><span class="co">## k1 1.55e-02 4.10e-04 0.0143 0.0167</span></span> +<span><span class="co">## k2 8.63e-12 5.00e-01 0.0000 Inf</span></span> +<span><span class="co">## g 6.89e-01 2.92e-03 0.6626 0.7142</span></span> +<span><span class="co">## sigma 6.48e-01 2.38e-05 0.4147 0.8813</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 86.6 2.88e+02 8.66e+01</span></span> +<span><span class="co">## IORE 85.5 7.17e+02 2.16e+02</span></span> +<span><span class="co">## DFOP 83.6 1.32e+11 8.04e+10</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 215.87</span></span></code></pre> </div> -<div id="example-on-page-6" class="section level2"> -<h2 class="hasAnchor"> -<a href="#example-on-page-6" class="anchor"></a>Example on page 6</h2> +<div class="section level3"> +<h3 id="example-on-page-6">Example on page 6<a class="anchor" aria-label="anchor" href="#example-on-page-6"></a> +</h3> <div class="sourceCode" id="cb13"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p6</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p6"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> -<pre><code>## The half-life obtained from the IORE model may be used</code></pre> +<code class="sourceCode R"><span><span class="va">p6</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p6"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb16"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">p6</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p6</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p6-1.png" width="700"></p> <div class="sourceCode" id="cb17"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p6</span><span class="op">)</span></code></pre></div> -<pre><code>## Sums of squares: -## SFO IORE DFOP -## 188.45361 51.00699 42.46931 -## -## Critical sum of squares for checking the SFO model: -## [1] 58.39888 -## -## Parameters: -## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 94.7759 7.29e-24 92.3478 97.2039 -## k_parent 0.0179 8.02e-16 0.0166 0.0194 -## sigma 3.0696 3.81e-06 2.0456 4.0936 -## -## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 97.12446 2.63e-26 95.62461 98.62431 -## k__iore_parent 0.00252 1.95e-03 0.00134 0.00472 -## N_parent 1.49587 4.07e-13 1.33896 1.65279 -## sigma 1.59698 5.05e-06 1.06169 2.13227 -## -## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 9.66e+01 1.57e-25 95.3476 97.8979 -## k1 2.55e-02 7.33e-06 0.0233 0.0278 -## k2 3.22e-11 5.00e-01 0.0000 Inf -## g 8.61e-01 7.55e-06 0.8314 0.8867 -## sigma 1.46e+00 6.93e-06 0.9661 1.9483 -## -## -## DTx values: -## DT50 DT90 DT50_rep -## SFO 38.6 1.28e+02 3.86e+01 -## IORE 34.0 1.77e+02 5.32e+01 -## DFOP 34.1 1.01e+10 2.15e+10 -## -## Representative half-life: -## [1] 53.17</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p6</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 188.45361 51.00699 42.46931 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 58.39888</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 94.7759 7.29e-24 92.3478 97.2039</span></span> +<span><span class="co">## k_parent 0.0179 8.02e-16 0.0166 0.0194</span></span> +<span><span class="co">## sigma 3.0696 3.81e-06 2.0456 4.0936</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 97.12446 2.63e-26 95.62461 98.62431</span></span> +<span><span class="co">## k__iore_parent 0.00252 1.95e-03 0.00134 0.00472</span></span> +<span><span class="co">## N_parent 1.49587 4.07e-13 1.33896 1.65279</span></span> +<span><span class="co">## sigma 1.59698 5.05e-06 1.06169 2.13227</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 9.66e+01 1.57e-25 95.3476 97.8979</span></span> +<span><span class="co">## k1 2.55e-02 7.33e-06 0.0233 0.0278</span></span> +<span><span class="co">## k2 3.22e-11 5.00e-01 0.0000 Inf</span></span> +<span><span class="co">## g 8.61e-01 7.55e-06 0.8314 0.8867</span></span> +<span><span class="co">## sigma 1.46e+00 6.93e-06 0.9661 1.9483</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 38.6 1.28e+02 3.86e+01</span></span> +<span><span class="co">## IORE 34.0 1.77e+02 5.32e+01</span></span> +<span><span class="co">## DFOP 34.1 1.01e+10 2.15e+10</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 53.17</span></span></code></pre> </div> -<div id="example-on-page-7" class="section level2"> -<h2 class="hasAnchor"> -<a href="#example-on-page-7" class="anchor"></a>Example on page 7</h2> +<div class="section level3"> +<h3 id="example-on-page-7">Example on page 7<a class="anchor" aria-label="anchor" href="#example-on-page-7"></a> +</h3> <div class="sourceCode" id="cb19"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p7</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p7"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> -<pre><code>## The half-life obtained from the IORE model may be used</code></pre> +<code class="sourceCode R"><span><span class="va">p7</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p7"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb22"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">p7</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p7</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p7-1.png" width="700"></p> <div class="sourceCode" id="cb23"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p7</span><span class="op">)</span></code></pre></div> -<pre><code>## Sums of squares: -## SFO IORE DFOP -## 3661.661 3195.030 3174.145 -## -## Critical sum of squares for checking the SFO model: -## [1] 3334.194 -## -## Parameters: -## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 96.41796 4.80e-53 93.32245 99.51347 -## k_parent 0.00735 7.64e-21 0.00641 0.00843 -## sigma 7.94557 1.83e-15 6.46713 9.42401 -## -## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 9.92e+01 NA 9.55e+01 1.03e+02 -## k__iore_parent 1.60e-05 NA 1.45e-07 1.77e-03 -## N_parent 2.45e+00 NA 1.35e+00 3.54e+00 -## sigma 7.42e+00 NA 6.04e+00 8.80e+00 -## -## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 9.89e+01 9.44e-49 95.4640 102.2573 -## k1 1.81e-02 1.75e-01 0.0116 0.0281 -## k2 3.63e-10 5.00e-01 0.0000 Inf -## g 6.06e-01 2.19e-01 0.4826 0.7178 -## sigma 7.40e+00 2.97e-15 6.0201 8.7754 -## -## -## DTx values: -## DT50 DT90 DT50_rep -## SFO 94.3 3.13e+02 9.43e+01 -## IORE 96.7 1.51e+03 4.55e+02 -## DFOP 96.4 3.77e+09 1.91e+09 -## -## Representative half-life: -## [1] 454.55</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p7</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 3661.661 3195.030 3174.145 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 3334.194</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 96.41796 4.80e-53 93.32245 99.51347</span></span> +<span><span class="co">## k_parent 0.00735 7.64e-21 0.00641 0.00843</span></span> +<span><span class="co">## sigma 7.94557 1.83e-15 6.46713 9.42401</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 9.92e+01 NA 9.55e+01 1.03e+02</span></span> +<span><span class="co">## k__iore_parent 1.60e-05 NA 1.45e-07 1.77e-03</span></span> +<span><span class="co">## N_parent 2.45e+00 NA 1.35e+00 3.54e+00</span></span> +<span><span class="co">## sigma 7.42e+00 NA 6.04e+00 8.80e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 9.89e+01 9.44e-49 95.4640 102.2573</span></span> +<span><span class="co">## k1 1.81e-02 1.75e-01 0.0116 0.0281</span></span> +<span><span class="co">## k2 3.63e-10 5.00e-01 0.0000 Inf</span></span> +<span><span class="co">## g 6.06e-01 2.19e-01 0.4826 0.7178</span></span> +<span><span class="co">## sigma 7.40e+00 2.97e-15 6.0201 8.7754</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 94.3 3.13e+02 9.43e+01</span></span> +<span><span class="co">## IORE 96.7 1.51e+03 4.55e+02</span></span> +<span><span class="co">## DFOP 96.4 3.77e+09 1.91e+09</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 454.55</span></span></code></pre> </div> </div> -<div id="examples-where-the-representative-half-life-deviates-from-the-observed-dt50" class="section level1"> -<h1 class="hasAnchor"> -<a href="#examples-where-the-representative-half-life-deviates-from-the-observed-dt50" class="anchor"></a>Examples where the representative half-life deviates from the observed DT50</h1> -<div id="example-on-page-8" class="section level2"> -<h2 class="hasAnchor"> -<a href="#example-on-page-8" class="anchor"></a>Example on page 8</h2> +<div class="section level2"> +<h2 id="examples-where-the-representative-half-life-deviates-from-the-observed-dt50">Examples where the representative half-life deviates from the observed DT50<a class="anchor" aria-label="anchor" href="#examples-where-the-representative-half-life-deviates-from-the-observed-dt50"></a> +</h2> +<div class="section level3"> +<h3 id="example-on-page-8">Example on page 8<a class="anchor" aria-label="anchor" href="#example-on-page-8"></a> +</h3> <p>For this dataset, the IORE fit does not converge when the default starting values used by mkin for the IORE model are used. Therefore, a lower value for the rate constant is used here.</p> <div class="sourceCode" id="cb25"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p8</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p8"</span><span class="op">]</span><span class="op">]</span>, parms.ini <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span>k__iore_parent <span class="op">=</span> <span class="fl">1e-3</span><span class="op">)</span><span class="op">)</span></code></pre></div> -<pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> -<pre><code>## The half-life obtained from the IORE model may be used</code></pre> +<code class="sourceCode R"><span><span class="va">p8</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p8"</span><span class="op">]</span><span class="op">]</span>, parms.ini <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k__iore_parent <span class="op">=</span> <span class="fl">1e-3</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb28"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">p8</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p8</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p8-1.png" width="700"></p> <div class="sourceCode" id="cb29"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p8</span><span class="op">)</span></code></pre></div> -<pre><code>## Sums of squares: -## SFO IORE DFOP -## 1996.9408 444.9237 547.5616 -## -## Critical sum of squares for checking the SFO model: -## [1] 477.4924 -## -## Parameters: -## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 88.16549 6.53e-29 83.37344 92.95754 -## k_parent 0.00803 1.67e-13 0.00674 0.00957 -## sigma 7.44786 4.17e-10 5.66209 9.23363 -## -## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 9.77e+01 7.03e-35 9.44e+01 1.01e+02 -## k__iore_parent 6.14e-05 3.20e-02 2.12e-05 1.78e-04 -## N_parent 2.27e+00 4.23e-18 2.00e+00 2.54e+00 -## sigma 3.52e+00 5.36e-10 2.67e+00 4.36e+00 -## -## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 95.70619 8.99e-32 91.87941 99.53298 -## k1 0.02500 5.25e-04 0.01422 0.04394 -## k2 0.00273 6.84e-03 0.00125 0.00597 -## g 0.58835 2.84e-06 0.36595 0.77970 -## sigma 3.90001 6.94e-10 2.96260 4.83741 -## -## -## DTx values: -## DT50 DT90 DT50_rep -## SFO 86.3 287 86.3 -## IORE 53.4 668 201.0 -## DFOP 55.6 517 253.0 -## -## Representative half-life: -## [1] 201.03</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p8</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 1996.9408 444.9237 547.5616 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 477.4924</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 88.16549 6.53e-29 83.37344 92.95754</span></span> +<span><span class="co">## k_parent 0.00803 1.67e-13 0.00674 0.00957</span></span> +<span><span class="co">## sigma 7.44786 4.17e-10 5.66209 9.23363</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 9.77e+01 7.03e-35 9.44e+01 1.01e+02</span></span> +<span><span class="co">## k__iore_parent 6.14e-05 3.20e-02 2.12e-05 1.78e-04</span></span> +<span><span class="co">## N_parent 2.27e+00 4.23e-18 2.00e+00 2.54e+00</span></span> +<span><span class="co">## sigma 3.52e+00 5.36e-10 2.67e+00 4.36e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 95.70619 8.99e-32 91.87941 99.53298</span></span> +<span><span class="co">## k1 0.02500 5.25e-04 0.01422 0.04394</span></span> +<span><span class="co">## k2 0.00273 6.84e-03 0.00125 0.00597</span></span> +<span><span class="co">## g 0.58835 2.84e-06 0.36595 0.77970</span></span> +<span><span class="co">## sigma 3.90001 6.94e-10 2.96260 4.83741</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 86.3 287 86.3</span></span> +<span><span class="co">## IORE 53.4 668 201.0</span></span> +<span><span class="co">## DFOP 55.6 517 253.0</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 201.03</span></span></code></pre> </div> </div> -<div id="examples-where-sfo-was-not-selected-for-an-abiotic-study" class="section level1"> -<h1 class="hasAnchor"> -<a href="#examples-where-sfo-was-not-selected-for-an-abiotic-study" class="anchor"></a>Examples where SFO was not selected for an abiotic study</h1> -<div id="example-on-page-9-upper-panel" class="section level2"> -<h2 class="hasAnchor"> -<a href="#example-on-page-9-upper-panel" class="anchor"></a>Example on page 9, upper panel</h2> +<div class="section level2"> +<h2 id="examples-where-sfo-was-not-selected-for-an-abiotic-study">Examples where SFO was not selected for an abiotic study<a class="anchor" aria-label="anchor" href="#examples-where-sfo-was-not-selected-for-an-abiotic-study"></a> +</h2> +<div class="section level3"> +<h3 id="example-on-page-9-upper-panel">Example on page 9, upper panel<a class="anchor" aria-label="anchor" href="#example-on-page-9-upper-panel"></a> +</h3> <div class="sourceCode" id="cb31"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p9a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p9a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> -<pre><code>## The half-life obtained from the IORE model may be used</code></pre> +<code class="sourceCode R"><span><span class="va">p9a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p9a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb34"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">p9a</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p9a</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p9a-1.png" width="700"></p> <div class="sourceCode" id="cb35"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p9a</span><span class="op">)</span></code></pre></div> -<pre><code>## Sums of squares: -## SFO IORE DFOP -## 839.35238 88.57064 9.93363 -## -## Critical sum of squares for checking the SFO model: -## [1] 105.5678 -## -## Parameters: -## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 88.1933 3.06e-12 79.9447 96.4419 -## k_parent 0.0409 2.07e-07 0.0324 0.0516 -## sigma 7.2429 3.92e-05 4.4768 10.0090 -## -## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 9.89e+01 1.12e-16 9.54e+01 1.02e+02 -## k__iore_parent 1.93e-05 1.13e-01 3.49e-06 1.06e-04 -## N_parent 2.91e+00 1.45e-09 2.50e+00 3.32e+00 -## sigma 2.35e+00 5.31e-05 1.45e+00 3.26e+00 -## -## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 9.85e+01 2.54e-20 97.390 99.672 -## k1 1.38e-01 3.52e-05 0.131 0.146 -## k2 9.02e-13 5.00e-01 0.000 Inf -## g 6.52e-01 8.13e-06 0.642 0.661 -## sigma 7.88e-01 6.13e-02 0.481 1.095 -## -## -## DTx values: -## DT50 DT90 DT50_rep -## SFO 16.9 5.63e+01 1.69e+01 -## IORE 11.6 3.37e+02 1.01e+02 -## DFOP 10.5 1.38e+12 7.69e+11 -## -## Representative half-life: -## [1] 101.43</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p9a</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 839.35238 88.57064 9.93363 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 105.5678</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 88.1933 3.06e-12 79.9447 96.4419</span></span> +<span><span class="co">## k_parent 0.0409 2.07e-07 0.0324 0.0516</span></span> +<span><span class="co">## sigma 7.2429 3.92e-05 4.4768 10.0090</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 9.89e+01 1.12e-16 9.54e+01 1.02e+02</span></span> +<span><span class="co">## k__iore_parent 1.93e-05 1.13e-01 3.49e-06 1.06e-04</span></span> +<span><span class="co">## N_parent 2.91e+00 1.45e-09 2.50e+00 3.32e+00</span></span> +<span><span class="co">## sigma 2.35e+00 5.31e-05 1.45e+00 3.26e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 9.85e+01 2.54e-20 97.390 99.672</span></span> +<span><span class="co">## k1 1.38e-01 3.52e-05 0.131 0.146</span></span> +<span><span class="co">## k2 9.02e-13 5.00e-01 0.000 Inf</span></span> +<span><span class="co">## g 6.52e-01 8.13e-06 0.642 0.661</span></span> +<span><span class="co">## sigma 7.88e-01 6.13e-02 0.481 1.095</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 16.9 5.63e+01 1.69e+01</span></span> +<span><span class="co">## IORE 11.6 3.37e+02 1.01e+02</span></span> +<span><span class="co">## DFOP 10.5 1.38e+12 7.69e+11</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 101.43</span></span></code></pre> <p>In this example, the residuals of the SFO indicate a lack of fit of this model, so even if it was an abiotic experiment, the data do not suggest a simple exponential decline.</p> </div> -<div id="example-on-page-9-lower-panel" class="section level2"> -<h2 class="hasAnchor"> -<a href="#example-on-page-9-lower-panel" class="anchor"></a>Example on page 9, lower panel</h2> +<div class="section level3"> +<h3 id="example-on-page-9-lower-panel">Example on page 9, lower panel<a class="anchor" aria-label="anchor" href="#example-on-page-9-lower-panel"></a> +</h3> <div class="sourceCode" id="cb37"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p9b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p9b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> -<pre><code>## Warning in sqrt(diag(covar_notrans)): NaNs produced</code></pre> -<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> -<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is -## doubtful</code></pre> -<pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> -<pre><code>## The half-life obtained from the IORE model may be used</code></pre> +<code class="sourceCode R"><span><span class="va">p9b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p9b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in sqrt(diag(covar_notrans)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span></span> +<span><span class="co">## doubtful</span></span></code></pre> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb44"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">p9b</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p9b</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p9b-1.png" width="700"></p> <div class="sourceCode" id="cb45"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p9b</span><span class="op">)</span></code></pre></div> -<pre><code>## Sums of squares: -## SFO IORE DFOP -## 35.64867 23.22334 35.64867 -## -## Critical sum of squares for checking the SFO model: -## [1] 28.54188 -## -## Parameters: -## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 94.7123 2.15e-19 93.178 96.2464 -## k_parent 0.0389 4.47e-14 0.037 0.0408 -## sigma 1.5957 1.28e-04 0.932 2.2595 -## -## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 93.863 2.32e-18 92.4565 95.269 -## k__iore_parent 0.127 1.85e-02 0.0504 0.321 -## N_parent 0.711 1.88e-05 0.4843 0.937 -## sigma 1.288 1.76e-04 0.7456 1.830 -## -## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 94.7123 1.61e-16 93.1355 96.2891 -## k1 0.0389 1.08e-04 0.0266 0.0569 -## k2 0.0389 2.23e-04 0.0255 0.0592 -## g 0.5256 NaN NA NA -## sigma 1.5957 2.50e-04 0.9135 2.2779 -## -## -## DTx values: -## DT50 DT90 DT50_rep -## SFO 17.8 59.2 17.8 -## IORE 18.4 49.2 14.8 -## DFOP 17.8 59.2 17.8 -## -## Representative half-life: -## [1] 14.8</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p9b</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 35.64867 23.22334 35.64867 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 28.54188</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 94.7123 2.15e-19 93.178 96.2464</span></span> +<span><span class="co">## k_parent 0.0389 4.47e-14 0.037 0.0408</span></span> +<span><span class="co">## sigma 1.5957 1.28e-04 0.932 2.2595</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 93.863 2.32e-18 92.4565 95.269</span></span> +<span><span class="co">## k__iore_parent 0.127 1.85e-02 0.0504 0.321</span></span> +<span><span class="co">## N_parent 0.711 1.88e-05 0.4843 0.937</span></span> +<span><span class="co">## sigma 1.288 1.76e-04 0.7456 1.830</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 94.7123 1.61e-16 93.1355 96.2891</span></span> +<span><span class="co">## k1 0.0389 1.08e-04 0.0266 0.0569</span></span> +<span><span class="co">## k2 0.0389 2.23e-04 0.0255 0.0592</span></span> +<span><span class="co">## g 0.5256 NaN NA NA</span></span> +<span><span class="co">## sigma 1.5957 2.50e-04 0.9135 2.2779</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 17.8 59.2 17.8</span></span> +<span><span class="co">## IORE 18.4 49.2 14.8</span></span> +<span><span class="co">## DFOP 17.8 59.2 17.8</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 14.8</span></span></code></pre> <p>Here, mkin gives a longer slow DT50 for the DFOP model (17.8 days) than PestDF (13.5 days). Presumably, this is related to the fact that PestDF gives a negative value for the proportion of the fast degradation which should be between 0 and 1, inclusive. This parameter is called f in PestDF and g in mkin. In mkin, it is restricted to the interval from 0 to 1.</p> </div> -<div id="example-on-page-10" class="section level2"> -<h2 class="hasAnchor"> -<a href="#example-on-page-10" class="anchor"></a>Example on page 10</h2> +<div class="section level3"> +<h3 id="example-on-page-10">Example on page 10<a class="anchor" aria-label="anchor" href="#example-on-page-10"></a> +</h3> <div class="sourceCode" id="cb47"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p10</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p10"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> -<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> -<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is -## doubtful</code></pre> -<pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> -<pre><code>## The half-life obtained from the IORE model may be used</code></pre> +<code class="sourceCode R"><span><span class="va">p10</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p10"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span></span> +<span><span class="co">## doubtful</span></span></code></pre> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb53"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">p10</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p10</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p10-1.png" width="700"></p> <div class="sourceCode" id="cb54"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p10</span><span class="op">)</span></code></pre></div> -<pre><code>## Sums of squares: -## SFO IORE DFOP -## 899.4089 336.4348 899.4089 -## -## Critical sum of squares for checking the SFO model: -## [1] 413.4841 -## -## Parameters: -## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 101.7315 6.42e-11 91.9259 111.5371 -## k_parent 0.0495 1.70e-07 0.0404 0.0607 -## sigma 8.0152 1.28e-04 4.6813 11.3491 -## -## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 96.86 3.32e-12 90.848 102.863 -## k__iore_parent 2.96 7.91e-02 0.687 12.761 -## N_parent 0.00 5.00e-01 -0.372 0.372 -## sigma 4.90 1.77e-04 2.837 6.968 -## -## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 101.7315 1.41e-09 91.6534 111.8097 -## k1 0.0495 6.58e-03 0.0303 0.0809 -## k2 0.0495 2.60e-03 0.0410 0.0598 -## g 0.4487 5.00e-01 NA NA -## sigma 8.0152 2.50e-04 4.5886 11.4418 -## -## -## DTx values: -## DT50 DT90 DT50_rep -## SFO 14.0 46.5 14.00 -## IORE 16.4 29.4 8.86 -## DFOP 14.0 46.5 14.00 -## -## Representative half-life: -## [1] 8.86</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p10</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 899.4089 336.4348 899.4089 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 413.4841</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 101.7315 6.42e-11 91.9259 111.5371</span></span> +<span><span class="co">## k_parent 0.0495 1.70e-07 0.0404 0.0607</span></span> +<span><span class="co">## sigma 8.0152 1.28e-04 4.6813 11.3491</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 96.86 3.32e-12 90.848 102.863</span></span> +<span><span class="co">## k__iore_parent 2.96 7.91e-02 0.687 12.761</span></span> +<span><span class="co">## N_parent 0.00 5.00e-01 -0.372 0.372</span></span> +<span><span class="co">## sigma 4.90 1.77e-04 2.837 6.968</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 101.7315 1.41e-09 91.6534 111.8097</span></span> +<span><span class="co">## k1 0.0495 6.58e-03 0.0303 0.0809</span></span> +<span><span class="co">## k2 0.0495 2.60e-03 0.0410 0.0598</span></span> +<span><span class="co">## g 0.4487 5.00e-01 NA NA</span></span> +<span><span class="co">## sigma 8.0152 2.50e-04 4.5886 11.4418</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 14.0 46.5 14.00</span></span> +<span><span class="co">## IORE 16.4 29.4 8.86</span></span> +<span><span class="co">## DFOP 14.0 46.5 14.00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 8.86</span></span></code></pre> <p>Here, a value below N is given for the IORE model, because the data suggests a faster decline towards the end of the experiment, which appears physically rather unlikely in the case of a photolysis study. It seems PestDF does not constrain N to values above zero, thus the slight difference in IORE model parameters between PestDF and mkin.</p> </div> </div> -<div id="the-dt50-was-not-observed-during-the-study" class="section level1"> -<h1 class="hasAnchor"> -<a href="#the-dt50-was-not-observed-during-the-study" class="anchor"></a>The DT50 was not observed during the study</h1> -<div id="example-on-page-11" class="section level2"> -<h2 class="hasAnchor"> -<a href="#example-on-page-11" class="anchor"></a>Example on page 11</h2> +<div class="section level2"> +<h2 id="the-dt50-was-not-observed-during-the-study">The DT50 was not observed during the study<a class="anchor" aria-label="anchor" href="#the-dt50-was-not-observed-during-the-study"></a> +</h2> +<div class="section level3"> +<h3 id="example-on-page-11">Example on page 11<a class="anchor" aria-label="anchor" href="#example-on-page-11"></a> +</h3> <div class="sourceCode" id="cb56"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p11</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p11"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> -<pre><code>## The half-life obtained from the IORE model may be used</code></pre> +<code class="sourceCode R"><span><span class="va">p11</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p11"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb59"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">p11</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p11</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p11-1.png" width="700"></p> <div class="sourceCode" id="cb60"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p11</span><span class="op">)</span></code></pre></div> -<pre><code>## Sums of squares: -## SFO IORE DFOP -## 579.6805 204.7932 144.7783 -## -## Critical sum of squares for checking the SFO model: -## [1] 251.6944 -## -## Parameters: -## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 96.15820 4.83e-13 90.24934 1.02e+02 -## k_parent 0.00321 4.71e-05 0.00222 4.64e-03 -## sigma 6.43473 1.28e-04 3.75822 9.11e+00 -## -## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 1.05e+02 NA 9.90e+01 1.10e+02 -## k__iore_parent 3.11e-17 NA 1.35e-20 7.18e-14 -## N_parent 8.36e+00 NA 6.62e+00 1.01e+01 -## sigma 3.82e+00 NA 2.21e+00 5.44e+00 -## -## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 1.05e+02 9.47e-13 99.9990 109.1224 -## k1 4.41e-02 5.95e-03 0.0296 0.0658 -## k2 9.94e-13 5.00e-01 0.0000 Inf -## g 3.22e-01 1.45e-03 0.2814 0.3650 -## sigma 3.22e+00 3.52e-04 1.8410 4.5906 -## -## -## DTx values: -## DT50 DT90 DT50_rep -## SFO 2.16e+02 7.18e+02 2.16e+02 -## IORE 9.73e+02 1.37e+08 4.11e+07 -## DFOP 3.07e+11 1.93e+12 6.98e+11 -## -## Representative half-life: -## [1] 41148170</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p11</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 579.6805 204.7932 144.7783 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 251.6944</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 96.15820 4.83e-13 90.24934 1.02e+02</span></span> +<span><span class="co">## k_parent 0.00321 4.71e-05 0.00222 4.64e-03</span></span> +<span><span class="co">## sigma 6.43473 1.28e-04 3.75822 9.11e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 1.05e+02 NA 9.90e+01 1.10e+02</span></span> +<span><span class="co">## k__iore_parent 3.11e-17 NA 1.35e-20 7.18e-14</span></span> +<span><span class="co">## N_parent 8.36e+00 NA 6.62e+00 1.01e+01</span></span> +<span><span class="co">## sigma 3.82e+00 NA 2.21e+00 5.44e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 1.05e+02 9.47e-13 99.9990 109.1224</span></span> +<span><span class="co">## k1 4.41e-02 5.95e-03 0.0296 0.0658</span></span> +<span><span class="co">## k2 9.94e-13 5.00e-01 0.0000 Inf</span></span> +<span><span class="co">## g 3.22e-01 1.45e-03 0.2814 0.3650</span></span> +<span><span class="co">## sigma 3.22e+00 3.52e-04 1.8410 4.5906</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 2.16e+02 7.18e+02 2.16e+02</span></span> +<span><span class="co">## IORE 9.73e+02 1.37e+08 4.11e+07</span></span> +<span><span class="co">## DFOP 3.07e+11 1.93e+12 6.98e+11</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 41148170</span></span></code></pre> <p>In this case, the DFOP fit reported for PestDF resulted in a negative value for the slower rate constant, which is not possible in mkin. The other results are in agreement.</p> </div> </div> -<div id="n-is-less-than-1-and-the-dfop-rate-constants-are-like-the-sfo-rate-constant" class="section level1"> -<h1 class="hasAnchor"> -<a href="#n-is-less-than-1-and-the-dfop-rate-constants-are-like-the-sfo-rate-constant" class="anchor"></a>N is less than 1 and the DFOP rate constants are like the SFO rate constant</h1> +<div class="section level2"> +<h2 id="n-is-less-than-1-and-the-dfop-rate-constants-are-like-the-sfo-rate-constant">N is less than 1 and the DFOP rate constants are like the SFO rate constant<a class="anchor" aria-label="anchor" href="#n-is-less-than-1-and-the-dfop-rate-constants-are-like-the-sfo-rate-constant"></a> +</h2> <p>In the following three examples, the same results are obtained with mkin as reported for PestDF. As in the case on page 10, the N values below 1 are deemed unrealistic and appear to be the result of an overparameterisation.</p> -<div id="example-on-page-12-upper-panel" class="section level2"> -<h2 class="hasAnchor"> -<a href="#example-on-page-12-upper-panel" class="anchor"></a>Example on page 12, upper panel</h2> +<div class="section level3"> +<h3 id="example-on-page-12-upper-panel">Example on page 12, upper panel<a class="anchor" aria-label="anchor" href="#example-on-page-12-upper-panel"></a> +</h3> <div class="sourceCode" id="cb62"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p12a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p12a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in summary.mkinfit(x): Could not calculate correlation; no covariance -## matrix</code></pre> -<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> -<pre><code>## Warning in sqrt(diag(covar_notrans)): NaNs produced</code></pre> -<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> -<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is -## doubtful</code></pre> -<pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> -<pre><code>## The half-life obtained from the IORE model may be used</code></pre> +<code class="sourceCode R"><span><span class="va">p12a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p12a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in summary.mkinfit(x): Could not calculate correlation; no covariance</span></span> +<span><span class="co">## matrix</span></span></code></pre> +<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in sqrt(diag(covar_notrans)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span></span> +<span><span class="co">## doubtful</span></span></code></pre> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb70"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">p12a</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p12a</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p12a-1.png" width="700"></p> <div class="sourceCode" id="cb71"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p12a</span><span class="op">)</span></code></pre></div> -<pre><code>## Sums of squares: -## SFO IORE DFOP -## 695.4440 220.0685 695.4440 -## -## Critical sum of squares for checking the SFO model: -## [1] 270.4679 -## -## Parameters: -## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 100.521 8.75e-12 92.461 108.581 -## k_parent 0.124 3.61e-08 0.104 0.148 -## sigma 7.048 1.28e-04 4.116 9.980 -## -## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 96.823 NA NA NA -## k__iore_parent 2.436 NA NA NA -## N_parent 0.263 NA NA NA -## sigma 3.965 NA NA NA -## -## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 100.521 2.74e-10 92.2366 108.805 -## k1 0.124 2.53e-05 0.0908 0.170 -## k2 0.124 2.52e-02 0.0456 0.339 -## g 0.793 NaN NA NA -## sigma 7.048 2.50e-04 4.0349 10.061 -## -## -## DTx values: -## DT50 DT90 DT50_rep -## SFO 5.58 18.5 5.58 -## IORE 6.49 13.2 3.99 -## DFOP 5.58 18.5 5.58 -## -## Representative half-life: -## [1] 3.99</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p12a</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 695.4440 220.0685 695.4440 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 270.4679</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 100.521 8.75e-12 92.461 108.581</span></span> +<span><span class="co">## k_parent 0.124 3.61e-08 0.104 0.148</span></span> +<span><span class="co">## sigma 7.048 1.28e-04 4.116 9.980</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 96.823 NA NA NA</span></span> +<span><span class="co">## k__iore_parent 2.436 NA NA NA</span></span> +<span><span class="co">## N_parent 0.263 NA NA NA</span></span> +<span><span class="co">## sigma 3.965 NA NA NA</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 100.521 2.74e-10 92.2366 108.805</span></span> +<span><span class="co">## k1 0.124 2.53e-05 0.0908 0.170</span></span> +<span><span class="co">## k2 0.124 2.52e-02 0.0456 0.339</span></span> +<span><span class="co">## g 0.793 NaN NA NA</span></span> +<span><span class="co">## sigma 7.048 2.50e-04 4.0349 10.061</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 5.58 18.5 5.58</span></span> +<span><span class="co">## IORE 6.49 13.2 3.99</span></span> +<span><span class="co">## DFOP 5.58 18.5 5.58</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 3.99</span></span></code></pre> </div> -<div id="example-on-page-12-lower-panel" class="section level2"> -<h2 class="hasAnchor"> -<a href="#example-on-page-12-lower-panel" class="anchor"></a>Example on page 12, lower panel</h2> +<div class="section level3"> +<h3 id="example-on-page-12-lower-panel">Example on page 12, lower panel<a class="anchor" aria-label="anchor" href="#example-on-page-12-lower-panel"></a> +</h3> <div class="sourceCode" id="cb73"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p12b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p12b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in qt(alpha/2, rdf): NaNs produced</code></pre> -<pre><code>## Warning in qt(1 - alpha/2, rdf): NaNs produced</code></pre> -<pre><code>## Warning in sqrt(diag(covar_notrans)): NaNs produced</code></pre> -<pre><code>## Warning in pt(abs(tval), rdf, lower.tail = FALSE): NaNs produced</code></pre> -<pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> -<pre><code>## The half-life obtained from the IORE model may be used</code></pre> +<code class="sourceCode R"><span><span class="va">p12b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p12b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in qt(alpha/2, rdf): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in qt(1 - alpha/2, rdf): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in sqrt(diag(covar_notrans)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in pt(abs(tval), rdf, lower.tail = FALSE): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb80"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">p12b</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p12b</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p12b-1.png" width="700"></p> <div class="sourceCode" id="cb81"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p12b</span><span class="op">)</span></code></pre></div> -<pre><code>## Sums of squares: -## SFO IORE DFOP -## 58.90242 19.06353 58.90242 -## -## Critical sum of squares for checking the SFO model: -## [1] 51.51756 -## -## Parameters: -## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 97.6840 0.00039 85.9388 109.4292 -## k_parent 0.0589 0.00261 0.0431 0.0805 -## sigma 3.4323 0.04356 -1.2377 8.1023 -## -## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 95.523 0.0055 74.539157 116.51 -## k__iore_parent 0.333 0.1433 0.000717 154.57 -## N_parent 0.568 0.0677 -0.989464 2.13 -## sigma 1.953 0.0975 -5.893100 9.80 -## -## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 97.6840 NaN NaN NaN -## k1 0.0589 NaN NA NA -## k2 0.0589 NaN NA NA -## g 0.6473 NaN NA NA -## sigma 3.4323 NaN NaN NaN -## -## -## DTx values: -## DT50 DT90 DT50_rep -## SFO 11.8 39.1 11.80 -## IORE 12.9 31.4 9.46 -## DFOP 11.8 39.1 11.80 -## -## Representative half-life: -## [1] 9.46</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p12b</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 58.90242 19.06353 58.90242 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 51.51756</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 97.6840 0.00039 85.9388 109.4292</span></span> +<span><span class="co">## k_parent 0.0589 0.00261 0.0431 0.0805</span></span> +<span><span class="co">## sigma 3.4323 0.04356 -1.2377 8.1023</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 95.523 0.0055 74.539157 116.51</span></span> +<span><span class="co">## k__iore_parent 0.333 0.1433 0.000717 154.57</span></span> +<span><span class="co">## N_parent 0.568 0.0677 -0.989464 2.13</span></span> +<span><span class="co">## sigma 1.953 0.0975 -5.893100 9.80</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 97.6840 NaN NaN NaN</span></span> +<span><span class="co">## k1 0.0589 NaN NA NA</span></span> +<span><span class="co">## k2 0.0589 NaN NA NA</span></span> +<span><span class="co">## g 0.6473 NaN NA NA</span></span> +<span><span class="co">## sigma 3.4323 NaN NaN NaN</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 11.8 39.1 11.80</span></span> +<span><span class="co">## IORE 12.9 31.4 9.46</span></span> +<span><span class="co">## DFOP 11.8 39.1 11.80</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 9.46</span></span></code></pre> </div> -<div id="example-on-page-13" class="section level2"> -<h2 class="hasAnchor"> -<a href="#example-on-page-13" class="anchor"></a>Example on page 13</h2> +<div class="section level3"> +<h3 id="example-on-page-13">Example on page 13<a class="anchor" aria-label="anchor" href="#example-on-page-13"></a> +</h3> <div class="sourceCode" id="cb83"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p13</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p13"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> -<pre><code>## The half-life obtained from the IORE model may be used</code></pre> +<code class="sourceCode R"><span><span class="va">p13</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p13"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb86"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">p13</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p13</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p13-1.png" width="700"></p> <div class="sourceCode" id="cb87"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p13</span><span class="op">)</span></code></pre></div> -<pre><code>## Sums of squares: -## SFO IORE DFOP -## 174.5971 142.3951 174.5971 -## -## Critical sum of squares for checking the SFO model: -## [1] 172.131 -## -## Parameters: -## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 92.73500 5.99e-17 89.61936 95.85065 -## k_parent 0.00258 2.42e-09 0.00223 0.00299 -## sigma 3.41172 7.07e-05 2.05455 4.76888 -## -## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 91.6016 6.34e-16 88.53086 94.672 -## k__iore_parent 0.0396 2.36e-01 0.00207 0.759 -## N_parent 0.3541 1.46e-01 -0.35153 1.060 -## sigma 3.0811 9.64e-05 1.84296 4.319 -## -## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 92.73500 NA 8.95e+01 95.92118 -## k1 0.00258 NA 4.14e-04 0.01611 -## k2 0.00258 NA 1.74e-03 0.00383 -## g 0.16452 NA 0.00e+00 1.00000 -## sigma 3.41172 NA 2.02e+00 4.79960 -## -## -## DTx values: -## DT50 DT90 DT50_rep -## SFO 269 892 269 -## IORE 261 560 169 -## DFOP 269 892 269 -## -## Representative half-life: -## [1] 168.51</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p13</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 174.5971 142.3951 174.5971 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 172.131</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 92.73500 5.99e-17 89.61936 95.85065</span></span> +<span><span class="co">## k_parent 0.00258 2.42e-09 0.00223 0.00299</span></span> +<span><span class="co">## sigma 3.41172 7.07e-05 2.05455 4.76888</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 91.6016 6.34e-16 88.53086 94.672</span></span> +<span><span class="co">## k__iore_parent 0.0396 2.36e-01 0.00207 0.759</span></span> +<span><span class="co">## N_parent 0.3541 1.46e-01 -0.35153 1.060</span></span> +<span><span class="co">## sigma 3.0811 9.64e-05 1.84296 4.319</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 92.73500 NA 8.95e+01 95.92118</span></span> +<span><span class="co">## k1 0.00258 NA 4.14e-04 0.01611</span></span> +<span><span class="co">## k2 0.00258 NA 1.74e-03 0.00383</span></span> +<span><span class="co">## g 0.16452 NA 0.00e+00 1.00000</span></span> +<span><span class="co">## sigma 3.41172 NA 2.02e+00 4.79960</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 269 892 269</span></span> +<span><span class="co">## IORE 261 560 169</span></span> +<span><span class="co">## DFOP 269 892 269</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 168.51</span></span></code></pre> </div> </div> -<div id="dt50-not-observed-in-the-study-and-dfop-problems-in-pestdf" class="section level1"> -<h1 class="hasAnchor"> -<a href="#dt50-not-observed-in-the-study-and-dfop-problems-in-pestdf" class="anchor"></a>DT50 not observed in the study and DFOP problems in PestDF</h1> +<div class="section level2"> +<h2 id="dt50-not-observed-in-the-study-and-dfop-problems-in-pestdf">DT50 not observed in the study and DFOP problems in PestDF<a class="anchor" aria-label="anchor" href="#dt50-not-observed-in-the-study-and-dfop-problems-in-pestdf"></a> +</h2> <div class="sourceCode" id="cb89"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p14</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p14"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> -<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> -<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is -## doubtful</code></pre> -<pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> -<pre><code>## The half-life obtained from the IORE model may be used</code></pre> +<code class="sourceCode R"><span><span class="va">p14</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p14"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span></span> +<span><span class="co">## doubtful</span></span></code></pre> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb95"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">p14</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p14</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p14-1.png" width="700"></p> <div class="sourceCode" id="cb96"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p14</span><span class="op">)</span></code></pre></div> -<pre><code>## Sums of squares: -## SFO IORE DFOP -## 48.43249 28.67746 27.26248 -## -## Critical sum of squares for checking the SFO model: -## [1] 32.83337 -## -## Parameters: -## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 99.47124 2.06e-30 98.42254 1.01e+02 -## k_parent 0.00279 3.75e-15 0.00256 3.04e-03 -## sigma 1.55616 3.81e-06 1.03704 2.08e+00 -## -## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 1.00e+02 NA NaN NaN -## k__iore_parent 9.44e-08 NA NaN NaN -## N_parent 3.31e+00 NA NaN NaN -## sigma 1.20e+00 NA 0.796 1.6 -## -## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 1.00e+02 2.96e-28 99.40280 101.2768 -## k1 9.53e-03 1.20e-01 0.00638 0.0143 -## k2 6.08e-12 5.00e-01 0.00000 Inf -## g 3.98e-01 2.19e-01 0.30481 0.4998 -## sigma 1.17e+00 7.68e-06 0.77406 1.5610 -## -## -## DTx values: -## DT50 DT90 DT50_rep -## SFO 2.48e+02 8.25e+02 2.48e+02 -## IORE 4.34e+02 2.22e+04 6.70e+03 -## DFOP 3.05e+10 2.95e+11 1.14e+11 -## -## Representative half-life: -## [1] 6697.44</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p14</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 48.43249 28.67746 27.26248 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 32.83337</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 99.47124 2.06e-30 98.42254 1.01e+02</span></span> +<span><span class="co">## k_parent 0.00279 3.75e-15 0.00256 3.04e-03</span></span> +<span><span class="co">## sigma 1.55616 3.81e-06 1.03704 2.08e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 1.00e+02 NA NaN NaN</span></span> +<span><span class="co">## k__iore_parent 9.44e-08 NA NaN NaN</span></span> +<span><span class="co">## N_parent 3.31e+00 NA NaN NaN</span></span> +<span><span class="co">## sigma 1.20e+00 NA 0.796 1.6</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 1.00e+02 2.96e-28 99.40280 101.2768</span></span> +<span><span class="co">## k1 9.53e-03 1.20e-01 0.00638 0.0143</span></span> +<span><span class="co">## k2 6.08e-12 5.00e-01 0.00000 Inf</span></span> +<span><span class="co">## g 3.98e-01 2.19e-01 0.30481 0.4998</span></span> +<span><span class="co">## sigma 1.17e+00 7.68e-06 0.77406 1.5610</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 2.48e+02 8.25e+02 2.48e+02</span></span> +<span><span class="co">## IORE 4.34e+02 2.22e+04 6.70e+03</span></span> +<span><span class="co">## DFOP 3.05e+10 2.95e+11 1.14e+11</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 6697.44</span></span></code></pre> <p>The slower rate constant reported by PestDF is negative, which is not physically realistic, and not possible in mkin. The other fits give the same results in mkin and PestDF.</p> </div> -<div id="n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero" class="section level1"> -<h1 class="hasAnchor"> -<a href="#n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero" class="anchor"></a>N is less than 1 and DFOP fraction parameter is below zero</h1> +<div class="section level2"> +<h2 id="n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero">N is less than 1 and DFOP fraction parameter is below zero<a class="anchor" aria-label="anchor" href="#n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero"></a> +</h2> <div class="sourceCode" id="cb98"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p15a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p15a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> -<pre><code>## The half-life obtained from the IORE model may be used</code></pre> +<code class="sourceCode R"><span><span class="va">p15a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p15a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb101"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">p15a</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p15a</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p15a-1.png" width="700"></p> <div class="sourceCode" id="cb102"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p15a</span><span class="op">)</span></code></pre></div> -<pre><code>## Sums of squares: -## SFO IORE DFOP -## 245.5248 135.0132 245.5248 -## -## Critical sum of squares for checking the SFO model: -## [1] 165.9335 -## -## Parameters: -## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 97.96751 2.00e-15 94.32049 101.615 -## k_parent 0.00952 4.93e-09 0.00824 0.011 -## sigma 4.18778 1.28e-04 2.44588 5.930 -## -## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 95.874 2.94e-15 92.937 98.811 -## k__iore_parent 0.629 2.11e-01 0.044 8.982 -## N_parent 0.000 5.00e-01 -0.642 0.642 -## sigma 3.105 1.78e-04 1.795 4.416 -## -## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 97.96751 2.85e-13 94.21913 101.7159 -## k1 0.00952 6.28e-02 0.00250 0.0363 -## k2 0.00952 1.27e-04 0.00646 0.0140 -## g 0.21241 5.00e-01 0.00000 1.0000 -## sigma 4.18778 2.50e-04 2.39747 5.9781 -## -## -## DTx values: -## DT50 DT90 DT50_rep -## SFO 72.8 242 72.8 -## IORE 76.3 137 41.3 -## DFOP 72.8 242 72.8 -## -## Representative half-life: -## [1] 41.33</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p15a</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 245.5248 135.0132 245.5248 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 165.9335</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 97.96751 2.00e-15 94.32049 101.615</span></span> +<span><span class="co">## k_parent 0.00952 4.93e-09 0.00824 0.011</span></span> +<span><span class="co">## sigma 4.18778 1.28e-04 2.44588 5.930</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 95.874 2.94e-15 92.937 98.811</span></span> +<span><span class="co">## k__iore_parent 0.629 2.11e-01 0.044 8.982</span></span> +<span><span class="co">## N_parent 0.000 5.00e-01 -0.642 0.642</span></span> +<span><span class="co">## sigma 3.105 1.78e-04 1.795 4.416</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 97.96751 2.85e-13 94.21913 101.7159</span></span> +<span><span class="co">## k1 0.00952 6.28e-02 0.00250 0.0363</span></span> +<span><span class="co">## k2 0.00952 1.27e-04 0.00646 0.0140</span></span> +<span><span class="co">## g 0.21241 5.00e-01 0.00000 1.0000</span></span> +<span><span class="co">## sigma 4.18778 2.50e-04 2.39747 5.9781</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 72.8 242 72.8</span></span> +<span><span class="co">## IORE 76.3 137 41.3</span></span> +<span><span class="co">## DFOP 72.8 242 72.8</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 41.33</span></span></code></pre> <div class="sourceCode" id="cb104"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p15b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p15b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> -<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> -<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is -## doubtful</code></pre> -<pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> -<pre><code>## The half-life obtained from the IORE model may be used</code></pre> +<code class="sourceCode R"><span><span class="va">p15b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p15b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span></span> +<span><span class="co">## doubtful</span></span></code></pre> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb110"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">p15b</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p15b</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p15b-1.png" width="700"></p> <div class="sourceCode" id="cb111"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p15b</span><span class="op">)</span></code></pre></div> -<pre><code>## Sums of squares: -## SFO IORE DFOP -## 106.91629 68.55574 106.91629 -## -## Critical sum of squares for checking the SFO model: -## [1] 84.25618 -## -## Parameters: -## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 1.01e+02 3.06e-17 98.31594 1.03e+02 -## k_parent 4.86e-03 2.48e-10 0.00435 5.42e-03 -## sigma 2.76e+00 1.28e-04 1.61402 3.91e+00 -## -## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 99.83 1.81e-16 97.51349 102.14 -## k__iore_parent 0.38 3.22e-01 0.00352 41.05 -## N_parent 0.00 5.00e-01 -1.07696 1.08 -## sigma 2.21 2.57e-04 1.23245 3.19 -## -## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 1.01e+02 NA 9.82e+01 1.04e+02 -## k1 4.86e-03 NA 8.63e-04 2.73e-02 -## k2 4.86e-03 NA 3.21e-03 7.35e-03 -## g 1.88e-01 NA NA NA -## sigma 2.76e+00 NA 1.58e+00 3.94e+00 -## -## -## DTx values: -## DT50 DT90 DT50_rep -## SFO 143 474 143.0 -## IORE 131 236 71.2 -## DFOP 143 474 143.0 -## -## Representative half-life: -## [1] 71.18</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p15b</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 106.91629 68.55574 106.91629 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 84.25618</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 1.01e+02 3.06e-17 98.31594 1.03e+02</span></span> +<span><span class="co">## k_parent 4.86e-03 2.48e-10 0.00435 5.42e-03</span></span> +<span><span class="co">## sigma 2.76e+00 1.28e-04 1.61402 3.91e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 99.83 1.81e-16 97.51349 102.14</span></span> +<span><span class="co">## k__iore_parent 0.38 3.22e-01 0.00352 41.05</span></span> +<span><span class="co">## N_parent 0.00 5.00e-01 -1.07696 1.08</span></span> +<span><span class="co">## sigma 2.21 2.57e-04 1.23245 3.19</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 1.01e+02 NA 9.82e+01 1.04e+02</span></span> +<span><span class="co">## k1 4.86e-03 NA 8.63e-04 2.73e-02</span></span> +<span><span class="co">## k2 4.86e-03 NA 3.21e-03 7.35e-03</span></span> +<span><span class="co">## g 1.88e-01 NA NA NA</span></span> +<span><span class="co">## sigma 2.76e+00 NA 1.58e+00 3.94e+00</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 143 474 143.0</span></span> +<span><span class="co">## IORE 131 236 71.2</span></span> +<span><span class="co">## DFOP 143 474 143.0</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 71.18</span></span></code></pre> <p>In mkin, only the IORE fit is affected (deemed unrealistic), as the fraction parameter of the DFOP model is restricted to the interval between 0 and 1 in mkin. The SFO fits give the same results for both mkin and PestDF.</p> </div> -<div id="the-dfop-fraction-parameter-is-greater-than-1" class="section level1"> -<h1 class="hasAnchor"> -<a href="#the-dfop-fraction-parameter-is-greater-than-1" class="anchor"></a>The DFOP fraction parameter is greater than 1</h1> +<div class="section level2"> +<h2 id="the-dfop-fraction-parameter-is-greater-than-1">The DFOP fraction parameter is greater than 1<a class="anchor" aria-label="anchor" href="#the-dfop-fraction-parameter-is-greater-than-1"></a> +</h2> <div class="sourceCode" id="cb113"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">p16</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p16"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> -<pre><code>## The representative half-life of the IORE model is longer than the one corresponding</code></pre> -<pre><code>## to the terminal degradation rate found with the DFOP model.</code></pre> -<pre><code>## The representative half-life obtained from the DFOP model may be used</code></pre> +<code class="sourceCode R"><span><span class="va">p16</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p16"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> +<pre><code><span><span class="co">## The representative half-life of the IORE model is longer than the one corresponding</span></span></code></pre> +<pre><code><span><span class="co">## to the terminal degradation rate found with the DFOP model.</span></span></code></pre> +<pre><code><span><span class="co">## The representative half-life obtained from the DFOP model may be used</span></span></code></pre> <div class="sourceCode" id="cb118"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">p16</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p16</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p16-1.png" width="700"></p> <div class="sourceCode" id="cb119"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">p16</span><span class="op">)</span></code></pre></div> -<pre><code>## Sums of squares: -## SFO IORE DFOP -## 3831.804 2062.008 1550.980 -## -## Critical sum of squares for checking the SFO model: -## [1] 2247.348 -## -## Parameters: -## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 71.953 2.33e-13 60.509 83.40 -## k_parent 0.159 4.86e-05 0.102 0.25 -## sigma 11.302 1.25e-08 8.308 14.30 -## -## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 8.74e+01 2.48e-16 7.72e+01 97.52972 -## k__iore_parent 4.55e-04 2.16e-01 3.48e-05 0.00595 -## N_parent 2.70e+00 1.21e-08 1.99e+00 3.40046 -## sigma 8.29e+00 1.61e-08 6.09e+00 10.49062 -## -## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 88.5333 7.40e-18 79.9836 97.083 -## k1 18.8461 5.00e-01 0.0000 Inf -## k2 0.0776 1.41e-05 0.0518 0.116 -## g 0.4733 1.41e-09 0.3674 0.582 -## sigma 7.1902 2.11e-08 5.2785 9.102 -## -## -## DTx values: -## DT50 DT90 DT50_rep -## SFO 4.35 14.4 4.35 -## IORE 1.48 32.1 9.67 -## DFOP 0.67 21.4 8.93 -## -## Representative half-life: -## [1] 8.93</code></pre> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p16</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Sums of squares:</span></span> +<span><span class="co">## SFO IORE DFOP </span></span> +<span><span class="co">## 3831.804 2062.008 1550.980 </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Critical sum of squares for checking the SFO model:</span></span> +<span><span class="co">## [1] 2247.348</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Parameters:</span></span> +<span><span class="co">## $SFO</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 71.953 2.33e-13 60.509 83.40</span></span> +<span><span class="co">## k_parent 0.159 4.86e-05 0.102 0.25</span></span> +<span><span class="co">## sigma 11.302 1.25e-08 8.308 14.30</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $IORE</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 8.74e+01 2.48e-16 7.72e+01 97.52972</span></span> +<span><span class="co">## k__iore_parent 4.55e-04 2.16e-01 3.48e-05 0.00595</span></span> +<span><span class="co">## N_parent 2.70e+00 1.21e-08 1.99e+00 3.40046</span></span> +<span><span class="co">## sigma 8.29e+00 1.61e-08 6.09e+00 10.49062</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## $DFOP</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 88.5333 7.40e-18 79.9836 97.083</span></span> +<span><span class="co">## k1 18.8461 5.00e-01 0.0000 Inf</span></span> +<span><span class="co">## k2 0.0776 1.41e-05 0.0518 0.116</span></span> +<span><span class="co">## g 0.4733 1.41e-09 0.3674 0.582</span></span> +<span><span class="co">## sigma 7.1902 2.11e-08 5.2785 9.102</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## </span></span> +<span><span class="co">## DTx values:</span></span> +<span><span class="co">## DT50 DT90 DT50_rep</span></span> +<span><span class="co">## SFO 4.35 14.4 4.35</span></span> +<span><span class="co">## IORE 1.48 32.1 9.67</span></span> +<span><span class="co">## DFOP 0.67 21.4 8.93</span></span> +<span><span class="co">## </span></span> +<span><span class="co">## Representative half-life:</span></span> +<span><span class="co">## [1] 8.93</span></span></code></pre> <p>In PestDF, the DFOP fit seems to have stuck in a local minimum, as mkin finds a solution with a much lower <span class="math inline">\(\chi^2\)</span> error level. As the half-life from the slower rate constant of the DFOP model is larger than the IORE derived half-life, the NAFTA recommendation obtained with mkin is to use the DFOP representative half-life of 8.9 days.</p> </div> -<div id="conclusions" class="section level1"> -<h1 class="hasAnchor"> -<a href="#conclusions" class="anchor"></a>Conclusions</h1> +<div class="section level2"> +<h2 id="conclusions">Conclusions<a class="anchor" aria-label="anchor" href="#conclusions"></a> +</h2> <p>The results obtained with mkin deviate from the results obtained with PestDF either in cases where one of the interpretive rules would apply, i.e. the IORE parameter N is less than one or the DFOP k values obtained with PestDF are equal to the SFO k values, or in cases where the DFOP model did not converge, which often lead to negative rate constants returned by PestDF.</p> <p>Therefore, mkin appears to suitable for kinetic evaluations according to the NAFTA guidance.</p> </div> -<div id="references" class="section level1 unnumbered"> -<h1 class="hasAnchor"> -<a href="#references" class="anchor"></a>References</h1> +<div class="section level2"> +<h2 class="unnumbered" id="references">References<a class="anchor" aria-label="anchor" href="#references"></a> +</h2> <div id="refs" class="references hanging-indent"> <div id="ref-usepa2015"> <p>US EPA. 2015. “Standard Operating Procedure for Using the NAFTA Guidance to Calculate Representative Half-Life Values and Characterizing Pesticide Degradation.”</p> @@ -1016,11 +1027,13 @@ <footer><div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <p></p> +<p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer> @@ -1029,5 +1042,7 @@ + + </body> </html> diff --git 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b/docs/dev/articles/web_only/benchmarks.html @@ -34,7 +34,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -112,7 +112,7 @@ <h1 data-toc-skip>Benchmark timings for mkin</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">Last change 14 July 2022 (rebuilt 2022-11-15)</h4> + <h4 data-toc-skip class="date">Last change 14 July 2022 (rebuilt 2022-11-24)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/benchmarks.rmd" class="external-link"><code>vignettes/web_only/benchmarks.rmd</code></a></small> <div class="hidden name"><code>benchmarks.rmd</code></div> @@ -351,8 +351,16 @@ <td align="left">Ryzen 7 1700</td> <td align="left">4.2.2</td> <td align="left">1.2.0</td> -<td align="right">2.129</td> -<td align="right">3.784</td> +<td align="right">2.140</td> +<td align="right">3.774</td> +</tr> +<tr class="odd"> +<td align="left">Linux</td> +<td align="left">Ryzen 7 1700</td> +<td align="left">4.2.2</td> +<td align="left">1.2.2</td> +<td align="right">2.187</td> +<td align="right">3.851</td> </tr> </tbody> </table> @@ -530,9 +538,18 @@ <td align="left">Ryzen 7 1700</td> <td align="left">4.2.2</td> <td align="left">1.2.0</td> -<td align="right">1.559</td> -<td align="right">6.097</td> -<td align="right">2.841</td> +<td align="right">1.554</td> +<td align="right">6.193</td> +<td align="right">2.843</td> +</tr> +<tr class="odd"> +<td align="left">Linux</td> +<td align="left">Ryzen 7 1700</td> +<td align="left">4.2.2</td> +<td align="left">1.2.2</td> +<td align="right">1.585</td> +<td align="right">6.335</td> +<td align="right">3.003</td> </tr> </tbody> </table> @@ -764,12 +781,24 @@ <td align="left">Ryzen 7 1700</td> <td align="left">4.2.2</td> <td align="left">1.2.0</td> -<td align="right">0.911</td> -<td align="right">1.328</td> -<td align="right">1.519</td> -<td align="right">2.986</td> -<td align="right">1.957</td> -<td align="right">2.769</td> +<td align="right">0.913</td> +<td align="right">1.345</td> +<td align="right">1.539</td> +<td align="right">3.011</td> +<td align="right">1.987</td> +<td align="right">2.802</td> +</tr> +<tr class="odd"> +<td align="left">Linux</td> +<td align="left">Ryzen 7 1700</td> +<td align="left">4.2.2</td> +<td align="left">1.2.2</td> +<td align="right">0.935</td> +<td align="right">1.381</td> +<td align="right">1.551</td> +<td align="right">3.209</td> +<td align="right">1.976</td> +<td align="right">3.013</td> </tr> </tbody> </table> diff --git a/docs/dev/articles/web_only/compiled_models.html b/docs/dev/articles/web_only/compiled_models.html index ade86bc5..e9d80420 100644 --- a/docs/dev/articles/web_only/compiled_models.html +++ b/docs/dev/articles/web_only/compiled_models.html @@ -34,7 +34,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -78,7 +78,10 @@ <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul> </li> @@ -109,7 +112,7 @@ <h1 data-toc-skip>Performance benefit by using compiled model definitions in mkin</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">2022-11-01</h4> + <h4 data-toc-skip class="date">2022-11-24</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/compiled_models.rmd" class="external-link"><code>vignettes/web_only/compiled_models.rmd</code></a></small> <div class="hidden name"><code>compiled_models.rmd</code></div> @@ -167,10 +170,10 @@ <span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="st">"R package rbenchmark is not available"</span><span class="op">)</span></span> <span><span class="op">}</span></span></code></pre></div> <pre><code><span><span class="co">## test replications relative elapsed</span></span> -<span><span class="co">## 4 analytical 1 1.000 0.186</span></span> -<span><span class="co">## 3 deSolve, compiled 1 1.656 0.308</span></span> -<span><span class="co">## 2 Eigenvalue based 1 2.102 0.391</span></span> -<span><span class="co">## 1 deSolve, not compiled 1 38.968 7.248</span></span></code></pre> +<span><span class="co">## 4 analytical 1 1.000 0.221</span></span> +<span><span class="co">## 3 deSolve, compiled 1 1.561 0.345</span></span> +<span><span class="co">## 2 Eigenvalue based 1 1.932 0.427</span></span> +<span><span class="co">## 1 deSolve, not compiled 1 33.629 7.432</span></span></code></pre> <p>We see that using the compiled model is by more than a factor of 10 faster than using deSolve without compiled code.</p> </div> <div class="section level2"> @@ -197,10 +200,10 @@ <span><span class="op">}</span></span></code></pre></div> <pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <pre><code><span><span class="co">## test replications relative elapsed</span></span> -<span><span class="co">## 2 deSolve, compiled 1 1.000 0.452</span></span> -<span><span class="co">## 1 deSolve, not compiled 1 29.431 13.303</span></span></code></pre> -<p>Here we get a performance benefit of a factor of 29 using the version of the differential equation model compiled from C code!</p> -<p>This vignette was built with mkin 1.2.0 on</p> +<span><span class="co">## 2 deSolve, compiled 1 1.000 0.482</span></span> +<span><span class="co">## 1 deSolve, not compiled 1 27.865 13.431</span></span></code></pre> +<p>Here we get a performance benefit of a factor of 28 using the version of the differential equation model compiled from C code!</p> +<p>This vignette was built with mkin 1.2.2 on</p> <pre><code><span><span class="co">## R version 4.2.2 (2022-10-31)</span></span> <span><span class="co">## Platform: x86_64-pc-linux-gnu (64-bit)</span></span> <span><span class="co">## Running under: Debian GNU/Linux 11 (bullseye)</span></span></code></pre> diff --git a/docs/dev/articles/web_only/dimethenamid_2018.html b/docs/dev/articles/web_only/dimethenamid_2018.html index 60f1ab5a..ec7f54d8 100644 --- a/docs/dev/articles/web_only/dimethenamid_2018.html +++ b/docs/dev/articles/web_only/dimethenamid_2018.html @@ -34,7 +34,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.2</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -63,19 +63,25 @@ <a href="../../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul> </li> @@ -106,7 +112,7 @@ <h1 data-toc-skip>Example evaluations of the dimethenamid data from 2018</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">Last change 1 July 2022, built on 16 Sep 2022</h4> + <h4 data-toc-skip class="date">Last change 1 July 2022, built on 24 Nov 2022</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/dimethenamid_2018.rmd" class="external-link"><code>vignettes/web_only/dimethenamid_2018.rmd</code></a></small> <div class="hidden name"><code>dimethenamid_2018.rmd</code></div> @@ -366,7 +372,7 @@ DFOP tc more iterations 665.88 663.80</code></pre> <span><span class="op">)</span></span> <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">AIC_parent_saemix_methods_defaults</span><span class="op">)</span></span></code></pre></div> <pre><code> is gq lin -668.27 718.36 666.49 </code></pre> +669.77 669.36 670.95 </code></pre> </div> </div> <div class="section level3"> @@ -437,7 +443,7 @@ DFOP tc more iterations 665.88 663.80</code></pre> </h2> <div class="sourceCode" id="cb32"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/sessionInfo.html" class="external-link">sessionInfo</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> -<pre><code>R version 4.2.1 (2022-06-23) +<pre><code>R version 4.2.2 (2022-10-31) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Debian GNU/Linux 11 (bullseye) @@ -457,24 +463,24 @@ attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: -[1] nlme_3.1-158 mkin_1.1.2 knitr_1.39 +[1] saemix_3.2 npde_3.2 nlme_3.1-160 mkin_1.2.2 knitr_1.41 loaded via a namespace (and not attached): - [1] deSolve_1.33 zoo_1.8-10 tidyselect_1.1.2 xfun_0.31 - [5] bslib_0.4.0 purrr_0.3.4 lattice_0.20-45 colorspace_2.0-3 - [9] vctrs_0.4.1 generics_0.1.3 htmltools_0.5.3 yaml_2.3.5 -[13] utf8_1.2.2 rlang_1.0.4 pkgdown_2.0.6 saemix_3.1 -[17] jquerylib_0.1.4 pillar_1.8.0 glue_1.6.2 DBI_1.1.3 -[21] lifecycle_1.0.1 stringr_1.4.0 munsell_0.5.0 gtable_0.3.0 -[25] ragg_1.2.2 memoise_2.0.1 evaluate_0.15 npde_3.2 -[29] fastmap_1.1.0 lmtest_0.9-40 parallel_4.2.1 fansi_1.0.3 -[33] highr_0.9 KernSmooth_2.23-20 scales_1.2.0 cachem_1.0.6 -[37] desc_1.4.1 jsonlite_1.8.0 systemfonts_1.0.4 fs_1.5.2 -[41] textshaping_0.3.6 gridExtra_2.3 ggplot2_3.3.6 digest_0.6.29 -[45] stringi_1.7.8 dplyr_1.0.9 grid_4.2.1 rprojroot_2.0.3 -[49] cli_3.3.0 tools_4.2.1 magrittr_2.0.3 sass_0.4.2 -[53] tibble_3.1.8 pkgconfig_2.0.3 assertthat_0.2.1 rmarkdown_2.14.3 -[57] mclust_5.4.10 R6_2.5.1 compiler_4.2.1 </code></pre> + [1] deSolve_1.34 zoo_1.8-11 tidyselect_1.2.0 xfun_0.35 + [5] bslib_0.4.1 purrr_0.3.5 lattice_0.20-45 colorspace_2.0-3 + [9] vctrs_0.5.1 generics_0.1.3 htmltools_0.5.3 yaml_2.3.6 +[13] utf8_1.2.2 rlang_1.0.6 pkgdown_2.0.6 jquerylib_0.1.4 +[17] pillar_1.8.1 glue_1.6.2 DBI_1.1.3 lifecycle_1.0.3 +[21] stringr_1.4.1 munsell_0.5.0 gtable_0.3.1 ragg_1.2.4 +[25] codetools_0.2-18 memoise_2.0.1 evaluate_0.18 fastmap_1.1.0 +[29] lmtest_0.9-40 parallel_4.2.2 fansi_1.0.3 highr_0.9 +[33] scales_1.2.1 cachem_1.0.6 desc_1.4.2 jsonlite_1.8.3 +[37] systemfonts_1.0.4 fs_1.5.2 textshaping_0.3.6 gridExtra_2.3 +[41] ggplot2_3.4.0 digest_0.6.30 stringi_1.7.8 dplyr_1.0.10 +[45] grid_4.2.2 rprojroot_2.0.3 cli_3.4.1 tools_4.2.2 +[49] magrittr_2.0.3 sass_0.4.3 tibble_3.1.8 pkgconfig_2.0.3 +[53] assertthat_0.2.1 rmarkdown_2.18 R6_2.5.1 mclust_6.0.0 +[57] compiler_4.2.2 </code></pre> </div> <div class="section level2"> <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a> diff --git a/docs/dev/articles/web_only/multistart.html b/docs/dev/articles/web_only/multistart.html index 50a57d1b..fd05f340 100644 --- a/docs/dev/articles/web_only/multistart.html +++ b/docs/dev/articles/web_only/multistart.html @@ -34,7 +34,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -78,7 +78,10 @@ <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul> </li> @@ -109,7 +112,7 @@ <h1 data-toc-skip>Short demo of the multistart method</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">Last change 26 September 2022 (rebuilt 2022-11-01)</h4> + <h4 data-toc-skip class="date">Last change 26 September 2022 (rebuilt 2022-11-24)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/multistart.rmd" class="external-link"><code>vignettes/web_only/multistart.rmd</code></a></small> <div class="hidden name"><code>multistart.rmd</code></div> @@ -163,8 +166,8 @@ <pre><code><span><span class="co">## Data: 155 observations of 1 variable(s) grouped in 6 datasets</span></span> <span><span class="co">## </span></span> <span><span class="co">## npar AIC BIC Lik Chisq Df Pr(>Chisq)</span></span> -<span><span class="co">## best(f_saem_reduced_multi) 9 663.64 661.77 -322.82 </span></span> -<span><span class="co">## f_saem_full 10 668.27 666.19 -324.13 0 1 1</span></span></code></pre> +<span><span class="co">## best(f_saem_reduced_multi) 9 663.69 661.82 -322.85 </span></span> +<span><span class="co">## f_saem_full 10 669.77 667.69 -324.89 0 1 1</span></span></code></pre> <p>While AIC and BIC are lower for the reduced model, the likelihood ratio test does not indicate a significant difference between the fits.</p> </div> diff --git a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png b/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png Binary files differindex 79543765..13bdb94b 100644 --- a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png +++ b/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png diff --git a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png b/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png Binary files differindex 4466d437..56147ae2 100644 --- a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png +++ b/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png diff --git a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png b/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png Binary files differindex 3dd36f91..f0b89dba 100644 --- a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png +++ b/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png diff --git a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-6-1.png b/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-6-1.png Binary files differindex 3963e993..c57c247f 100644 --- a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-6-1.png +++ b/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-6-1.png diff --git a/docs/dev/articles/web_only/saem_benchmarks.html b/docs/dev/articles/web_only/saem_benchmarks.html index e54bc38c..4fe648c6 100644 --- a/docs/dev/articles/web_only/saem_benchmarks.html +++ b/docs/dev/articles/web_only/saem_benchmarks.html @@ -34,14 +34,14 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"> <li> - <a href="../../reference/index.html">Functions and data</a> + <a href="../../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -53,6 +53,9 @@ <li> <a href="../../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -60,22 +63,31 @@ <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li class="divider"> </li> +<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Performance</li> + <li> + <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -83,6 +95,15 @@ <li> <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul> </li> <li> @@ -106,13 +127,15 @@ - </header><script src="saem_benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Benchmark timings for saem.mmkin</h1> - <h4 data-toc-skip class="author">Johannes Ranke</h4> + <h4 data-toc-skip class="author">Johannes +Ranke</h4> - <h4 data-toc-skip class="date">Last change 14 November 2022 (rebuilt 2022-11-15)</h4> + <h4 data-toc-skip class="date">Last change 14 November 2022 +(rebuilt 2023-01-28)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/saem_benchmarks.rmd" class="external-link"><code>vignettes/web_only/saem_benchmarks.rmd</code></a></small> <div class="hidden name"><code>saem_benchmarks.rmd</code></div> @@ -121,15 +144,19 @@ -<p>Each system is characterized by operating system type, CPU type, mkin version, saemix version and R version. A compiler was available, so if no analytical solution was available, compiled ODE models are used.</p> -<p>Every fit is only performed once, so the accuracy of the benchmarks is limited.</p> +<p>Each system is characterized by operating system type, CPU type, mkin +version, saemix version and R version. A compiler was available, so if +no analytical solution was available, compiled ODE models are used.</p> +<p>Every fit is only performed once, so the accuracy of the benchmarks +is limited.</p> <p>For the initial mmkin fits, we use all available cores.</p> <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">n_cores</span> <span class="op"><-</span> <span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> <div class="section level2"> <h2 id="test-data">Test data<a class="anchor" aria-label="anchor" href="#test-data"></a> </h2> -<p>Please refer to the vignette <code>dimethenamid_2018</code> for an explanation of the following preprocessing.</p> +<p>Please refer to the vignette <code>dimethenamid_2018</code> for an +explanation of the following preprocessing.</p> <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">dmta_ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span> <span> <span class="va">ds_i</span> <span class="op"><-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span> @@ -232,19 +259,24 @@ </tr> </tbody> </table> -<p>The above model comparison suggests to use the SFORB model with two-component error. For comparison, we keep the DFOP model with two-component error, as it competes with SFORB for biphasic curves.</p> +<p>The above model comparison suggests to use the SFORB model with +two-component error. For comparison, we keep the DFOP model with +two-component error, as it competes with SFORB for biphasic curves.</p> <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">dfop_tc</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## [1] "sd(log_k2)"</span></span></code></pre> <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">sforb_tc</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## [1] "sd(log_k_DMTA_bound_free)"</span></span></code></pre> -<p>For these two models, random effects for the transformed parameters <code>k2</code> and <code>k_DMTA_bound_free</code> could not be quantified.</p> +<p>For these two models, random effects for the transformed parameters +<code>k2</code> and <code>k_DMTA_bound_free</code> could not be +quantified.</p> </div> <div class="section level3"> <h3 id="one-metabolite">One metabolite<a class="anchor" aria-label="anchor" href="#one-metabolite"></a> </h3> -<p>We remove parameters that were found to be ill-defined in the parent only fits.</p> +<p>We remove parameters that were found to be ill-defined in the parent +only fits.</p> <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">one_met_mods</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span> <span> DFOP_SFO <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> @@ -267,7 +299,11 @@ <div class="section level3"> <h3 id="three-metabolites">Three metabolites<a class="anchor" aria-label="anchor" href="#three-metabolites"></a> </h3> -<p>For the case of three metabolites, we only keep the SFORB model in order to limit the time for compiling this vignette, and as fitting in parallel may disturb the benchmark. Again, we do not include random effects that were ill-defined in previous fits of subsets of the degradation model.</p> +<p>For the case of three metabolites, we only keep the SFORB model in +order to limit the time for compiling this vignette, and as fitting in +parallel may disturb the benchmark. Again, we do not include random +effects that were ill-defined in previous fits of subsets of the +degradation model.</p> <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">sforb_sfo_tc</span><span class="op">)</span></span></code></pre></div> <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r"> @@ -288,12 +324,24 @@ <div class="section level2"> <h2 id="results">Results<a class="anchor" aria-label="anchor" href="#results"></a> </h2> -<p>Benchmarks for all available error models are shown. They are intended for improving mkin, not for comparing CPUs or operating systems. All trademarks belong to their respective owners.</p> +<p>Benchmarks for all available error models are shown. They are +intended for improving mkin, not for comparing CPUs or operating +systems. All trademarks belong to their respective owners.</p> <div class="section level3"> <h3 id="parent-only-1">Parent only<a class="anchor" aria-label="anchor" href="#parent-only-1"></a> </h3> <p>Constant variance for SFO, DFOP, SFORB and HS.</p> -<table class="table"> +<table style="width:100%;" class="table"> +<colgroup> +<col width="42%"> +<col width="8%"> +<col width="8%"> +<col width="9%"> +<col width="8%"> +<col width="8%"> +<col width="8%"> +<col width="8%"> +</colgroup> <thead><tr class="header"> <th align="left">CPU</th> <th align="left">OS</th> @@ -304,19 +352,51 @@ <th align="right">t3</th> <th align="right">t4</th> </tr></thead> -<tbody><tr class="odd"> +<tbody> +<tr class="odd"> <td align="left">Ryzen 7 1700</td> <td align="left">Linux</td> <td align="left">1.2.0</td> <td align="left">3.2</td> -<td align="right">2.11</td> -<td align="right">4.632</td> -<td align="right">4.264</td> -<td align="right">4.93</td> -</tr></tbody> +<td align="right">2.140</td> +<td align="right">4.626</td> +<td align="right">4.328</td> +<td align="right">4.998</td> +</tr> +<tr class="even"> +<td align="left">Ryzen 7 1700</td> +<td align="left">Linux</td> +<td align="left">1.2.2</td> +<td align="left">3.2</td> +<td align="right">2.427</td> +<td align="right">4.550</td> +<td align="right">4.217</td> +<td align="right">4.851</td> +</tr> +<tr class="odd"> +<td align="left">Ryzen 9 7950X 16-Core Processor</td> +<td align="left">Linux</td> +<td align="left">1.2.2</td> +<td align="left">3.2</td> +<td align="right">1.470</td> +<td align="right">2.263</td> +<td align="right">1.840</td> +<td align="right">2.299</td> +</tr> +</tbody> </table> <p>Two-component error fits for SFO, DFOP, SFORB and HS.</p> -<table class="table"> +<table style="width:100%;" class="table"> +<colgroup> +<col width="42%"> +<col width="8%"> +<col width="8%"> +<col width="9%"> +<col width="8%"> +<col width="8%"> +<col width="8%"> +<col width="8%"> +</colgroup> <thead><tr class="header"> <th align="left">CPU</th> <th align="left">OS</th> @@ -327,16 +407,38 @@ <th align="right">t7</th> <th align="right">t8</th> </tr></thead> -<tbody><tr class="odd"> +<tbody> +<tr class="odd"> <td align="left">Ryzen 7 1700</td> <td align="left">Linux</td> <td align="left">1.2.0</td> <td align="left">3.2</td> -<td align="right">5.602</td> -<td align="right">7.373</td> -<td align="right">7.815</td> -<td align="right">7.831</td> -</tr></tbody> +<td align="right">5.678</td> +<td align="right">7.441</td> +<td align="right">8.000</td> +<td align="right">7.980</td> +</tr> +<tr class="even"> +<td align="left">Ryzen 7 1700</td> +<td align="left">Linux</td> +<td align="left">1.2.2</td> +<td align="left">3.2</td> +<td align="right">5.352</td> +<td align="right">7.201</td> +<td align="right">8.174</td> +<td align="right">8.401</td> +</tr> +<tr class="odd"> +<td align="left">Ryzen 9 7950X 16-Core Processor</td> +<td align="left">Linux</td> +<td align="left">1.2.2</td> +<td align="left">3.2</td> +<td align="right">2.118</td> +<td align="right">3.528</td> +<td align="right">3.295</td> +<td align="right">3.157</td> +</tr> +</tbody> </table> </div> <div class="section level3"> @@ -344,6 +446,14 @@ </h3> <p>Two-component error for DFOP-SFO and SFORB-SFO.</p> <table class="table"> +<colgroup> +<col width="48%"> +<col width="9%"> +<col width="9%"> +<col width="10%"> +<col width="10%"> +<col width="12%"> +</colgroup> <thead><tr class="header"> <th align="left">CPU</th> <th align="left">OS</th> @@ -352,14 +462,32 @@ <th align="right">t9</th> <th align="right">t10</th> </tr></thead> -<tbody><tr class="odd"> +<tbody> +<tr class="odd"> <td align="left">Ryzen 7 1700</td> <td align="left">Linux</td> <td align="left">1.2.0</td> <td align="left">3.2</td> -<td align="right">24.014</td> -<td align="right">749.699</td> -</tr></tbody> +<td align="right">24.465</td> +<td align="right">800.266</td> +</tr> +<tr class="even"> +<td align="left">Ryzen 7 1700</td> +<td align="left">Linux</td> +<td align="left">1.2.2</td> +<td align="left">3.2</td> +<td align="right">25.193</td> +<td align="right">798.580</td> +</tr> +<tr class="odd"> +<td align="left">Ryzen 9 7950X 16-Core Processor</td> +<td align="left">Linux</td> +<td align="left">1.2.2</td> +<td align="left">3.2</td> +<td align="right">12.336</td> +<td align="right">277.666</td> +</tr> +</tbody> </table> </div> <div class="section level3"> @@ -374,13 +502,29 @@ <th align="left">saemix</th> <th align="right">t11</th> </tr></thead> -<tbody><tr class="odd"> +<tbody> +<tr class="odd"> <td align="left">Ryzen 7 1700</td> <td align="left">Linux</td> <td align="left">1.2.0</td> <td align="left">3.2</td> -<td align="right">1249.834</td> -</tr></tbody> +<td align="right">1289.198</td> +</tr> +<tr class="even"> +<td align="left">Ryzen 7 1700</td> +<td align="left">Linux</td> +<td align="left">1.2.2</td> +<td align="left">3.2</td> +<td align="right">1312.445</td> +</tr> +<tr class="odd"> +<td align="left">Ryzen 9 7950X 16-Core Processor</td> +<td align="left">Linux</td> +<td align="left">1.2.2</td> +<td align="left">3.2</td> +<td align="right">459.051</td> +</tr> +</tbody> </table> </div> </div> @@ -403,7 +547,7 @@ <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer> diff --git a/docs/dev/authors.html b/docs/dev/authors.html index 2f95f092..4146fdf2 100644 --- a/docs/dev/authors.html +++ b/docs/dev/authors.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="reference/index.html">Functions and data</a> + <a href="reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="news/index.html">News</a> @@ -113,15 +130,15 @@ </div> - <p>Ranke J (2022). + <p>Ranke J (2023). <em>mkin: Kinetic Evaluation of Chemical Degradation Data</em>. -R package version 1.2.0, <a href="https://pkgdown.jrwb.de/mkin/">https://pkgdown.jrwb.de/mkin/</a>. +R package version 1.2.2, <a href="https://pkgdown.jrwb.de/mkin/">https://pkgdown.jrwb.de/mkin/</a>. </p> <pre>@Manual{, title = {mkin: Kinetic Evaluation of Chemical Degradation Data}, author = {Johannes Ranke}, - year = {2022}, - note = {R package version 1.2.0}, + year = {2023}, + note = {R package version 1.2.2}, url = {https://pkgdown.jrwb.de/mkin/}, }</pre> @@ -136,7 +153,7 @@ R package version 1.2.0, <a href="https://pkgdown.jrwb.de/mkin/">https://pkgdown </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/dev/index.html b/docs/dev/index.html index 4709fe29..36a005ad 100644 --- a/docs/dev/index.html +++ b/docs/dev/index.html @@ -19,11 +19,11 @@ equation models are solved using automatically generated C functions. Heteroscedasticity can be taken into account using variance by variable or two-component error models as described by Ranke and Meinecke (2018) - <doi:10.3390/environments6120124>. Interfaces to several nonlinear - mixed-effects model packages are available, some of which are described by - Ranke et al. (2021) <doi:10.3390/environments8080071>. Please note that no - warranty is implied for correctness of results or fitness for a particular - purpose."> + <doi:10.3390/environments6120124>. Hierarchical degradation models can + be fitted using nonlinear mixed-effects model packages as a backend as + described by Ranke et al. (2021) <doi:10.3390/environments8080071>. Please + note that no warranty is implied for correctness of results or fitness for a + particular purpose."> <meta name="robots" content="noindex"> <!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> @@ -45,14 +45,14 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"> <li> - <a href="reference/index.html">Functions and data</a> + <a href="reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -64,6 +64,9 @@ <li> <a href="articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -71,22 +74,31 @@ <a href="articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Performance</li> + <li> + <a href="articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -94,6 +106,15 @@ <li> <a href="articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Miscellaneous</li> + <li> + <a href="articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul> </li> <li> @@ -122,8 +143,8 @@ <div class="section level1"> <div class="page-header"><h1 id="mkin">mkin<a class="anchor" aria-label="anchor" href="#mkin"></a> </h1></div> - -<p>The R package <strong>mkin</strong> provides calculation routines for the analysis of chemical degradation data, including <b>m</b>ulticompartment <b>kin</b>etics as needed for modelling the formation and decline of transformation products, or if several degradation compartments are involved.</p> +<p><a href="https://cran.r-project.org/package=mkin" class="external-link"><img src="https://www.r-pkg.org/badges/version/mkin"></a> <a href="https://jranke.r-universe.dev/ui#package:mkin" class="external-link"><img src="https://jranke.r-universe.dev/badges/mkin" alt="mkin status badge"></a> <a href="https://app.travis-ci.com/github/jranke/mkin" class="external-link"><img src="https://travis-ci.com/jranke/mkin.svg?branch=main" alt="Build Status"></a> <a href="https://codecov.io/github/jranke/mkin" class="external-link"><img src="https://codecov.io/github/jranke/mkin/branch/main/graphs/badge.svg" alt="codecov"></a></p> +<p>The <a href="https://r-project.org" class="external-link">R</a> package <strong>mkin</strong> provides calculation routines for the analysis of chemical degradation data, including <b>m</b>ulticompartment <b>kin</b>etics as needed for modelling the formation and decline of transformation products, or if several degradation compartments are involved. It provides stable functionality for kinetic evaluations according to the FOCUS guidance (see below for details). In addition, it provides functionality to do hierarchical kinetics based on nonlinear mixed-effects models.</p> <div class="section level2"> <h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a> </h2> @@ -134,7 +155,7 @@ <div class="section level2"> <h2 id="background">Background<a class="anchor" aria-label="anchor" href="#background"></a> </h2> -<p>In the regulatory evaluation of chemical substances like plant protection products (pesticides), biocides and other chemicals, degradation data play an important role. For the evaluation of pesticide degradation experiments, detailed guidance and helpful tools have been developed as detailed in ‘Credits and historical remarks’ below.</p> +<p>In the regulatory evaluation of chemical substances like plant protection products (pesticides), biocides and other chemicals, degradation data play an important role. For the evaluation of pesticide degradation experiments, detailed guidance and various helpful tools have been developed as detailed in ‘Credits and historical remarks’ below. This package aims to provide a one stop solution for degradation kinetics, addressing modellers that are willing to, or even prefer to work with R.</p> </div> <div class="section level2"> <h2 id="usage">Usage<a class="anchor" aria-label="anchor" href="#usage"></a> @@ -153,9 +174,9 @@ <h3 id="general">General<a class="anchor" aria-label="anchor" href="#general"></a> </h3> <ul> -<li>Highly flexible model specification using <a href="https://pkgdown.jrwb.de/mkin/reference/mkinmod.html"><code>mkinmod</code></a>, including equilibrium reactions and using the single first-order reversible binding (SFORB) model, which will automatically create two latent state variables for the observed variable.</li> -<li>Model solution (forward modelling) in the function <a href="https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html"><code>mkinpredict</code></a> is performed either using the analytical solution for the case of parent only degradation, an eigenvalue based solution if only simple first-order (SFO) or SFORB kinetics are used in the model, or using a numeric solver from the <code>deSolve</code> package (default is <code>lsoda</code>).</li> -<li>The usual one-sided t-test for significant difference from zero is nevertheless shown based on estimators for the untransformed parameters.</li> +<li>Highly flexible model specification using <a href="https://pkgdown.jrwb.de/mkin/reference/mkinmod.html"><code>mkinmod</code></a>, including equilibrium reactions and using the single first-order reversible binding (SFORB) model, which will automatically create two state variables for the observed variable.</li> +<li>Model solution (forward modelling) in the function <a href="https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html"><code>mkinpredict</code></a> is performed either using the analytical solution for the case of parent only degradation or some simple models involving a single transformation product, , an eigenvalue based solution if only simple first-order (SFO) or SFORB kinetics are used in the model, or using a numeric solver from the <code>deSolve</code> package (default is <code>lsoda</code>).</li> +<li>The usual one-sided t-test for significant difference from zero is shown based on estimators for the untransformed parameters.</li> <li>Summary and plotting functions. The <code>summary</code> of an <code>mkinfit</code> object is in fact a full report that should give enough information to be able to approximately reproduce the fit with other tools.</li> <li>The chi-squared error level as defined in the FOCUS kinetics guidance (see below) is calculated for each observed variable.</li> <li>The ‘variance by variable’ error model which is often fitted using Iteratively Reweighted Least Squares (IRLS) can be specified as <code>error_model = "obs"</code>.</li> @@ -169,8 +190,8 @@ <li>Model comparisons using the Akaike Information Criterion (AIC) are supported which can also be used for non-constant variance. In such cases the FOCUS chi-squared error level is not meaningful.</li> <li>By default, kinetic rate constants and kinetic formation fractions are transformed internally using <a href="https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html"><code>transform_odeparms</code></a> so their estimators can more reasonably be expected to follow a normal distribution.</li> <li>When parameter estimates are backtransformed to match the model definition, confidence intervals calculated from standard errors are also backtransformed to the correct scale, and will not include meaningless values like negative rate constants or formation fractions adding up to more than 1, which cannot occur in a single experiment with a single defined radiolabel position.</li> -<li>When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite. Mathematically, the SFORB model is equivalent to the DFOP model used by other tools for biphasic metabolite curves. However, the SFORB model has the advantage that there is a mechanistic interpretation of the model parameters.</li> -<li>Nonlinear mixed-effects models can be created from fits of the same degradation model to different datasets for the same compound by using the <a href="https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html">nlme.mmkin</a> and <a href="https://pkgdown.jrwb.de/mkin/reference/saem.html">saem.mmkin</a> and methods. Note that the convergence of the nlme fits depends on the quality of the data. Convergence is better for simple models and data for many groups (e.g. soils). The saem method uses the <code>saemix</code> package as a backend. Analytical solutions suitable for use with this package have been implemented for parent only models and the most important models including one metabolite (SFO-SFO and DFOP-SFO). Fitting other models with <code>saem.mmkin</code>, while it makes use of the compiled ODE models that mkin provides, has longer run times (at least six minutes on my system).</li> +<li>When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite. Mathematically, the SFORB model is equivalent to the DFOP model. However, the SFORB model has the advantage that there is a mechanistic interpretation of the model parameters.</li> +<li>Nonlinear mixed-effects models (hierarchical models) can be created from fits of the same degradation model to different datasets for the same compound by using the <a href="https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html">nlme.mmkin</a> and <a href="https://pkgdown.jrwb.de/mkin/reference/saem.html">saem.mmkin</a> methods. Note that the convergence of the nlme fits depends on the quality of the data. Convergence is better for simple models and data for many groups (e.g. soils). The saem method uses the <code>saemix</code> package as a backend. Analytical solutions suitable for use with this package have been implemented for parent only models and the most important models including one metabolite (SFO-SFO and DFOP-SFO). Fitting other models with <code>saem.mmkin</code>, while it makes use of the compiled ODE models that mkin provides, has longer run times (from a couple of minutes to more than an hour).</li> </ul> </div> <div class="section level3"> @@ -186,7 +207,7 @@ <div class="section level2"> <h2 id="gui">GUI<a class="anchor" aria-label="anchor" href="#gui"></a> </h2> -<p>There is a graphical user interface that may be useful. Please refer to its <a href="https://pkgdown.jrwb.de/gmkin/" class="external-link">documentation page</a> for installation instructions and a manual.</p> +<p>There is a graphical user interface that may be useful. Please refer to its <a href="https://pkgdown.jrwb.de/gmkin/" class="external-link">documentation page</a> for installation instructions and a manual. It only supports evaluations using (generalised) nonlinear regression, but not simultaneous fits using nonlinear mixed-effects models.</p> </div> <div class="section level2"> <h2 id="news">News<a class="anchor" aria-label="anchor" href="#news"></a> @@ -203,8 +224,8 @@ <p>The first <code>mkin</code> code was <a href="https://r-forge.r-project.org/scm/viewvc.php?view=rev&root=kinfit&revision=8" class="external-link">published on 11 May 2010</a> and the <a href="https://cran.r-project.org/src/contrib/Archive/mkin/" class="external-link">first CRAN version</a> on 18 May 2010.</p> <p>In 2011, Bayer Crop Science started to distribute an R based successor to KinGUI named KinGUII whose R code is based on <code>mkin</code>, but which added, among other refinements, a closed source graphical user interface (GUI), iteratively reweighted least squares (IRLS) optimisation of the variance for each of the observed variables, and Markov Chain Monte Carlo (MCMC) simulation functionality, similar to what is available e.g. in the <code>FME</code> package.</p> <p>Somewhat in parallel, Syngenta has sponsored the development of an <code>mkin</code> and KinGUII based GUI application called CAKE, which also adds IRLS and MCMC, is more limited in the model formulation, but puts more weight on usability. CAKE is available for download from the <a href="https://cake-kinetics.org" class="external-link">CAKE website</a>, where you can also find a zip archive of the R scripts derived from <code>mkin</code>, published under the GPL license.</p> -<p>Finally, there is <a href="https://github.com/zhenglei-gao/KineticEval" class="external-link">KineticEval</a>, which contains a further development of the scripts used for KinGUII, so the different tools will hopefully be able to learn from each other in the future as well.</p> -<p>Thanks to René Lehmann, formerly working at the Umweltbundesamt, for the nice cooperation cooperation on parameter transformations, especially the isometric log-ratio transformation that is now used for formation fractions in case there are more than two transformation targets.</p> +<p>Finally, there is <a href="https://github.com/zhenglei-gao/KineticEval" class="external-link">KineticEval</a>, which contains some further development of the scripts used for KinGUII.</p> +<p>Thanks to René Lehmann, formerly working at the Umweltbundesamt, for the nice cooperation on parameter transformations, especially the isometric log-ratio transformation that is now used for formation fractions in case there are more than two transformation targets.</p> <p>Many inspirations for improvements of mkin resulted from doing kinetic evaluations of degradation data for my clients while working at Harlan Laboratories and at Eurofins Regulatory AG, and now as an independent consultant.</p> <p>Funding was received from the Umweltbundesamt in the course of the projects</p> <ul> @@ -214,19 +235,30 @@ <li>Project Number 112407 (Testing the feasibility of using an error model according to Rocke and Lorenzato for more realistic parameter estimates in the kinetic evaluation of degradation data, 2018-2019)</li> <li>Project Number 120667 (Development of objective criteria for the evaluation of the visual fit in the kinetic evaluation of degradation data, 2019-2020)</li> <li>Project Number 146839 (Checking the feasibility of using mixed-effects models for the derivation of kinetic modelling parameters from degradation studies, 2020-2021)</li> +<li>Project Number 173340 (Application of nonlinear hierarchical models to the kinetic evaluation of chemical degradation data)</li> </ul> -<p>Thanks are due also to Emmanuelle Comets, maintainer of the saemix package, for the nice collaboration on using the SAEM algorithm and its implementation in saemix for the evaluation of chemical degradation data.</p> +<p>Thanks to everyone involved for collaboration and support!</p> +<p>Thanks are due also to Emmanuelle Comets, maintainer of the saemix package, for her interest and support for using the SAEM algorithm and its implementation in saemix for the evaluation of chemical degradation data.</p> </div> <div class="section level2"> <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a> </h2> <table class="table"> -<tr><td>Ranke J, Wöltjen J, Schmidt J, and Comets E (2021) Taking kinetic evaluations of degradation data to the next level with nonlinear mixed-effects models. <i>Environments</i> <b>8</b> (8) 71 <a href="https://doi.org/10.3390/environments8080071" class="external-link">doi:10.3390/environments8080071</a> -</td></tr> -<tr><td>Ranke J, Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical Degradation Data <i>Environments</i> <b>6</b> (12) 124 <a href="https://doi.org/10.3390/environments6120124" class="external-link">doi:10.3390/environments6120124</a> -</td></tr> -<tr><td>Ranke J, Wöltjen J, Meinecke S (2018) Comparison of software tools for kinetic evaluation of chemical degradation data <i>Environmental Sciences Europe</i> <b>30</b> 17 <a href="https://doi.org/10.1186/s12302-018-0145-1" class="external-link">doi:10.1186/s12302-018-0145-1</a> -</td></tr> +<tr> +<td> +Ranke J, Wöltjen J, Schmidt J, and Comets E (2021) Taking kinetic evaluations of degradation data to the next level with nonlinear mixed-effects models. <i>Environments</i> <b>8</b> (8) 71 <a href="https://doi.org/10.3390/environments8080071" class="external-link">doi:10.3390/environments8080071</a> +</td> +</tr> +<tr> +<td> +Ranke J, Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical Degradation Data <i>Environments</i> <b>6</b> (12) 124 <a href="https://doi.org/10.3390/environments6120124" class="external-link">doi:10.3390/environments6120124</a> +</td> +</tr> +<tr> +<td> +Ranke J, Wöltjen J, Meinecke S (2018) Comparison of software tools for kinetic evaluation of chemical degradation data <i>Environmental Sciences Europe</i> <b>30</b> 17 <a href="https://doi.org/10.1186/s12302-018-0145-1" class="external-link">doi:10.1186/s12302-018-0145-1</a> +</td> +</tr> </table> </div> <div class="section level2"> @@ -270,15 +302,7 @@ </ul> </div> -<div class="dev-status"> -<h2 data-toc-skip>Dev status</h2> -<ul class="list-unstyled"> -<li><a href="https://cran.r-project.org/package=mkin" class="external-link"><img src="https://www.r-pkg.org/badges/version/mkin"></a></li> -<li><a href="https://jranke.r-universe.dev/ui#package:mkin" class="external-link"><img src="https://jranke.r-universe.dev/badges/mkin" alt="mkin status badge"></a></li> -<li><a href="https://app.travis-ci.com/github/jranke/mkin" class="external-link"><img src="https://travis-ci.com/jranke/mkin.svg?branch=main" alt="Build Status"></a></li> -<li><a href="https://codecov.io/github/jranke/mkin" class="external-link"><img src="https://codecov.io/github/jranke/mkin/branch/main/graphs/badge.svg" alt="codecov"></a></li> -</ul> -</div> + </div> </div> @@ -291,7 +315,7 @@ <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer> diff --git a/docs/dev/news/index.html b/docs/dev/news/index.html index 8448ebc8..d8fcbfe9 100644 --- a/docs/dev/news/index.html +++ b/docs/dev/news/index.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -88,14 +105,34 @@ </div> <div class="section level2"> -<h2 class="page-header" data-toc-text="1.2.0" id="mkin-120-unreleased">mkin 1.2.0 (unreleased)<a class="anchor" aria-label="anchor" href="#mkin-120-unreleased"></a></h2> -<ul><li><p>‘R/mhmkin.R’: New method for performing multiple hierarchical mkin fits in one function call, optionally in parallel.</p></li> +<h2 class="page-header" data-toc-text="1.2.2" id="mkin-122">mkin 1.2.2<a class="anchor" aria-label="anchor" href="#mkin-122"></a></h2> +<ul><li><p>‘inst/rmarkdown/templates/hier’: R markdown template to facilitate the application of hierarchical kinetic models.</p></li> +<li><p>‘inst/testdata/lambda-cyhalothrin_soil_efsa_2014.xlsx’: Example spreadsheet for use with ‘read_spreadsheet()’.</p></li> +<li><p>‘R/mhmkin.R’: Allow an ‘illparms.mhmkin’ object or a list with suitable dimensions as value of the argument ‘no_random_effects’, making it possible to exclude random effects that were ill-defined in simpler variants of the set of degradation models. Remove the possibility to exclude random effects based on separate fits, as it did not work well.</p></li> +<li><p>‘R/summary.saem.mmkin.R’: List all initial parameter values in the summary, including random effects and error model parameters. Avoid redundant warnings that occurred in the calculation of correlations of the fixed effects in the case that the Fisher information matrix could not be inverted. List correlations of random effects if specified by the user in the covariance model.</p></li> +<li><p>‘R/parplot.R’: Possibility to select the top ‘llquant’ fraction of the fits for the parameter plots, and improved legend text.</p></li> +<li><p>‘R/illparms.R’: Also check if confidence intervals for slope parameters in covariate models include zero. Only implemented for fits obtained with the saemix backend.</p></li> +<li><p>‘R/parplot.R’: Make the function work also in the case that some of the multistart runs failed.</p></li> +<li><p>‘R/intervals.R’: Include correlations of random effects in the model in case there are any.</p></li> +</ul></div> + <div class="section level2"> +<h2 class="page-header" data-toc-text="1.2.1" id="mkin-121-2022-11-19">mkin 1.2.1 (2022-11-19)<a class="anchor" aria-label="anchor" href="#mkin-121-2022-11-19"></a></h2> +<ul><li><p>‘{data,R}/ds_mixed.rda’: Include the test data in the package instead of generating it in ‘tests/testthat/setup_script.R’. Refactor the generating code to make it consistent and update tests.</p></li> +<li><p>‘tests/testthat/setup_script.R’: Excluded another ill-defined random effect for the DFOP fit with ‘saem’, in an attempt to avoid a platform dependence that surfaced on Fedora systems on the CRAN check farm</p></li> +<li><p>‘tests/testthat/test_mixed.R’: Round parameters found by saemix to two significant digits before printing, to also help to avoid platform dependence of tests</p></li> +<li><p>‘R/saem.R’: Fix a bug that prevented that ‘error.ini’ is passed to ‘saemix_model’, and set default to c(1, 1) to avoid changing test results</p></li> +<li><p>‘R/parplot.R’: Show initial values for error model parameters</p></li> +<li><p>‘R/loglik.mkinfit.R’: Add ‘nobs’ attribute to the resulting ‘logLik’ object, in order to make test_AIC.R succeed on current R-devel</p></li> +</ul></div> + <div class="section level2"> +<h2 class="page-header" data-toc-text="1.2.0" id="mkin-120-2022-11-17">mkin 1.2.0 (2022-11-17)<a class="anchor" aria-label="anchor" href="#mkin-120-2022-11-17"></a></h2> +<ul><li><p>‘R/saem.R’: ‘logLik’, ‘update’ and ‘anova’ methods for ‘saem.mmkin’ objects.</p></li> +<li><p>‘R/saem.R’: Automatic estimation of start parameters for random effects for the case of mkin transformations, nicely improving convergence and reducing problems with iterative ODE solutions.</p></li> +<li><p>‘R/status.R’: New generic to show status information for fit array objects with methods for ‘mmkin’, ‘mhmkin’ and ‘multistart’ objects.</p></li> +<li><p>‘R/mhmkin.R’: New method for performing multiple hierarchical mkin fits in one function call, optionally in parallel.</p></li> <li><p>‘R/mhmkin.R’: ‘anova.mhmkin’ for conveniently comparing the resulting fits.</p></li> <li><p>‘R/illparms.R’: New generic to show ill-defined parameters with methods for ‘mkinfit’, ‘mmkin’, ‘saem.mmkin’ and ‘mhmkin’ objects.</p></li> <li><p>‘R/multistart.R’: New method for testing multiple start parameters for hierarchical model fits, with function ‘llhist’ and new generic ‘parplot’ for diagnostics, and new generics ‘which.best’ and ‘best’ for extracting the fit with the highest likelihood</p></li> -<li><p>‘R/saem.R’: ‘logLik’, ‘update’ and ‘anova’ methods for ‘saem.mmkin’ objects.</p></li> -<li><p>‘R/saem.R’: Automatic estimation of start parameters for random effects for the case of mkin transformations, nicely improving convergence and reducing problems with iterative ODE solutions.</p></li> -<li><p>‘R/status.R’: New generic to show status information for fit array objects with methods for ‘mmkin’, ‘mhmkin’ and ‘multistart’ objects.</p></li> <li><p>‘R/summary.mmkin.R’: Summary method for mmkin objects.</p></li> <li><p>‘R/saem.R’: Implement and test saemix transformations for FOMC and HS. Also, error out if saemix transformations are requested but not supported.</p></li> <li><p>‘R/read_spreadsheet.R’: Conveniently read in data from a spreadsheet file.</p></li> @@ -126,7 +163,8 @@ </div> <div class="section level2"> <h2 class="page-header" data-toc-text="1.0.5" id="mkin-105-2021-09-15">mkin 1.0.5 (2021-09-15)<a class="anchor" aria-label="anchor" href="#mkin-105-2021-09-15"></a></h2> -<ul><li>‘dimethenamid_2018’: Correct the data for the Borstel soil. The five observations from Staudenmaier (2013) that were previously stored as “Borstel 2” are actually just a subset of the 16 observations in “Borstel 1” which is now simply “Borstel”</li></ul></div> +<ul><li>‘dimethenamid_2018’: Correct the data for the Borstel soil. The five observations from Staudenmaier (2013) that were previously stored as “Borstel 2” are actually just a subset of the 16 observations in “Borstel 1” which is now simply “Borstel”</li> +</ul></div> <div class="section level2"> <h2 class="page-header" data-toc-text="1.0.4" id="mkin-104-2021-04-20">mkin 1.0.4 (2021-04-20)<a class="anchor" aria-label="anchor" href="#mkin-104-2021-04-20"></a></h2> <ul><li><p>All plotting functions setting graphical parameters: Use on.exit() for resetting graphical parameters</p></li> @@ -135,10 +173,12 @@ </ul></div> <div class="section level2"> <h2 class="page-header" data-toc-text="1.0.3" id="mkin-103-2021-02-15">mkin 1.0.3 (2021-02-15)<a class="anchor" aria-label="anchor" href="#mkin-103-2021-02-15"></a></h2> -<ul><li>Review and update README, the ‘Introduction to mkin’ vignette and some of the help pages</li></ul></div> +<ul><li>Review and update README, the ‘Introduction to mkin’ vignette and some of the help pages</li> +</ul></div> <div class="section level2"> <h2 class="page-header" data-toc-text="1.0.2" id="mkin-102-unreleased">mkin 1.0.2 (Unreleased)<a class="anchor" aria-label="anchor" href="#mkin-102-unreleased"></a></h2> -<ul><li>‘mkinfit’: Keep model names stored in ‘mkinmod’ objects, avoiding their loss in ‘gmkin’</li></ul></div> +<ul><li>‘mkinfit’: Keep model names stored in ‘mkinmod’ objects, avoiding their loss in ‘gmkin’</li> +</ul></div> <div class="section level2"> <h2 class="page-header" data-toc-text="1.0.1" id="mkin-101-2021-02-10">mkin 1.0.1 (2021-02-10)<a class="anchor" aria-label="anchor" href="#mkin-101-2021-02-10"></a></h2> <ul><li><p>‘confint.mmkin’, ‘nlme.mmkin’, ‘transform_odeparms’: Fix example code in dontrun sections that failed with current defaults</p></li> @@ -193,7 +233,8 @@ </ul></div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9.49.11" id="mkin-094911-2020-04-20">mkin 0.9.49.11 (2020-04-20)<a class="anchor" aria-label="anchor" href="#mkin-094911-2020-04-20"></a></h2> -<ul><li>Increase a test tolerance to make it pass on all CRAN check machines</li></ul></div> +<ul><li>Increase a test tolerance to make it pass on all CRAN check machines</li> +</ul></div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9.49.10" id="mkin-094910-2020-04-18">mkin 0.9.49.10 (2020-04-18)<a class="anchor" aria-label="anchor" href="#mkin-094910-2020-04-18"></a></h2> <ul><li><p>‘nlme.mmkin’: An nlme method for mmkin row objects and an associated S3 class with print, plot, anova and endpoint methods</p></li> @@ -308,7 +349,8 @@ </ul></div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9.46" id="mkin-0946-2017-07-24">mkin 0.9.46 (2017-07-24)<a class="anchor" aria-label="anchor" href="#mkin-0946-2017-07-24"></a></h2> -<ul><li>Remove <code>test_FOMC_ill-defined.R</code> as it is too platform dependent</li></ul></div> +<ul><li>Remove <code>test_FOMC_ill-defined.R</code> as it is too platform dependent</li> +</ul></div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9.45.2" id="mkin-09452-2017-07-24">mkin 0.9.45.2 (2017-07-24)<a class="anchor" aria-label="anchor" href="#mkin-09452-2017-07-24"></a></h2> <ul><li><p>Rename <code>twa</code> to <code>max_twa_parent</code> to avoid conflict with <code>twa</code> from my <code>pfm</code> package</p></li> @@ -320,7 +362,8 @@ <h2 class="page-header" data-toc-text="0.9.45.1" id="mkin-09451-2016-12-20">mkin 0.9.45.1 (2016-12-20)<a class="anchor" aria-label="anchor" href="#mkin-09451-2016-12-20"></a></h2> <div class="section level3"> <h3 id="new-features-0-9-45-1">New features<a class="anchor" aria-label="anchor" href="#new-features-0-9-45-1"></a></h3> -<ul><li>A <code>twa</code> function, calculating maximum time weighted average concentrations for the parent (SFO, FOMC and DFOP).</li></ul></div> +<ul><li>A <code>twa</code> function, calculating maximum time weighted average concentrations for the parent (SFO, FOMC and DFOP).</li> +</ul></div> </div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9.45" id="mkin-0945-2016-12-08">mkin 0.9.45 (2016-12-08)<a class="anchor" aria-label="anchor" href="#mkin-0945-2016-12-08"></a></h2> @@ -335,7 +378,8 @@ <h2 class="page-header" data-toc-text="0.9.44" id="mkin-0944-2016-06-29">mkin 0.9.44 (2016-06-29)<a class="anchor" aria-label="anchor" href="#mkin-0944-2016-06-29"></a></h2> <div class="section level3"> <h3 id="bug-fixes-0-9-44">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-44"></a></h3> -<ul><li>The test <code>test_FOMC_ill-defined</code> failed on several architectures, so the test is now skipped</li></ul></div> +<ul><li>The test <code>test_FOMC_ill-defined</code> failed on several architectures, so the test is now skipped</li> +</ul></div> </div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9.43" id="mkin-0943-2016-06-28">mkin 0.9.43 (2016-06-28)<a class="anchor" aria-label="anchor" href="#mkin-0943-2016-06-28"></a></h2> @@ -369,7 +413,8 @@ <h2 class="page-header" data-toc-text="0.9.42" id="mkin-0942-2016-03-25">mkin 0.9.42 (2016-03-25)<a class="anchor" aria-label="anchor" href="#mkin-0942-2016-03-25"></a></h2> <div class="section level3"> <h3 id="major-changes-0-9-42">Major changes<a class="anchor" aria-label="anchor" href="#major-changes-0-9-42"></a></h3> -<ul><li>Add the argument <code>from_max_mean</code> to <code>mkinfit</code>, for fitting only the decline from the maximum observed value for models with a single observed variable</li></ul></div> +<ul><li>Add the argument <code>from_max_mean</code> to <code>mkinfit</code>, for fitting only the decline from the maximum observed value for models with a single observed variable</li> +</ul></div> <div class="section level3"> <h3 id="minor-changes-0-9-42">Minor changes<a class="anchor" aria-label="anchor" href="#minor-changes-0-9-42"></a></h3> <ul><li><p>Add plots to <code>compiled_models</code> vignette</p></li> @@ -389,18 +434,21 @@ <div class="section level3"> <h3 id="bug-fixes-0-9-41">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-41"></a></h3> <ul><li> -<code><a href="../reference/summary.mkinfit.html">print.summary.mkinfit()</a></code>: Avoid an error that occurred when printing summaries generated with mkin versions before 0.9-36</li></ul></div> +<code><a href="../reference/summary.mkinfit.html">print.summary.mkinfit()</a></code>: Avoid an error that occurred when printing summaries generated with mkin versions before 0.9-36</li> +</ul></div> </div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9-40" id="mkin-09-40-2015-07-21">mkin 0.9-40 (2015-07-21)<a class="anchor" aria-label="anchor" href="#mkin-09-40-2015-07-21"></a></h2> <div class="section level3"> <h3 id="bug-fixes-0-9-40">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-40"></a></h3> <ul><li> -<code><a href="../reference/endpoints.html">endpoints()</a></code>: For DFOP and SFORB models, where <code><a href="https://rdrr.io/r/stats/optimize.html" class="external-link">optimize()</a></code> is used, make use of the fact that the DT50 must be between DT50_k1 and DT50_k2 (DFOP) or DT50_b1 and DT50_b2 (SFORB), as <code><a href="https://rdrr.io/r/stats/optimize.html" class="external-link">optimize()</a></code> sometimes did not find the minimum. Likewise for finding DT90 values. Also fit on the log scale to make the function more efficient.</li></ul></div> +<code><a href="../reference/endpoints.html">endpoints()</a></code>: For DFOP and SFORB models, where <code><a href="https://rdrr.io/r/stats/optimize.html" class="external-link">optimize()</a></code> is used, make use of the fact that the DT50 must be between DT50_k1 and DT50_k2 (DFOP) or DT50_b1 and DT50_b2 (SFORB), as <code><a href="https://rdrr.io/r/stats/optimize.html" class="external-link">optimize()</a></code> sometimes did not find the minimum. Likewise for finding DT90 values. Also fit on the log scale to make the function more efficient.</li> +</ul></div> <div class="section level3"> <h3 id="internal-changes-0-9-40">Internal changes<a class="anchor" aria-label="anchor" href="#internal-changes-0-9-40"></a></h3> <ul><li> -<code>DESCRIPTION</code>, <code>NAMESPACE</code>, <code>R/*.R</code>: Import (from) stats, graphics and methods packages, and qualify some function calls for non-base packages installed with R to avoid NOTES made by R CMD check –as-cran with upcoming R versions.</li></ul></div> +<code>DESCRIPTION</code>, <code>NAMESPACE</code>, <code>R/*.R</code>: Import (from) stats, graphics and methods packages, and qualify some function calls for non-base packages installed with R to avoid NOTES made by R CMD check –as-cran with upcoming R versions.</li> +</ul></div> </div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9-39" id="mkin-09-39-2015-06-26">mkin 0.9-39 (2015-06-26)<a class="anchor" aria-label="anchor" href="#mkin-09-39-2015-06-26"></a></h2> @@ -412,7 +460,8 @@ <div class="section level3"> <h3 id="bug-fixes-0-9-39">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-39"></a></h3> <ul><li> -<code><a href="../reference/mkinparplot.html">mkinparplot()</a></code>: Fix the x axis scaling for rate constants and formation fractions that got confused by the introduction of the t-values of transformed parameters.</li></ul></div> +<code><a href="../reference/mkinparplot.html">mkinparplot()</a></code>: Fix the x axis scaling for rate constants and formation fractions that got confused by the introduction of the t-values of transformed parameters.</li> +</ul></div> </div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9-38" id="mkin-09-38-2015-06-24">mkin 0.9-38 (2015-06-24)<a class="anchor" aria-label="anchor" href="#mkin-09-38-2015-06-24"></a></h2> @@ -424,7 +473,8 @@ <div class="section level3"> <h3 id="bug-fixes-0-9-38">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-38"></a></h3> <ul><li> -<code><a href="../reference/mkinmod.html">mkinmod()</a></code>: When generating the C code for the derivatives, only declare the time variable when it is needed and remove the ‘-W-no-unused-variable’ compiler flag as the C compiler used in the CRAN checks on Solaris does not know it.</li></ul></div> +<code><a href="../reference/mkinmod.html">mkinmod()</a></code>: When generating the C code for the derivatives, only declare the time variable when it is needed and remove the ‘-W-no-unused-variable’ compiler flag as the C compiler used in the CRAN checks on Solaris does not know it.</li> +</ul></div> </div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9-36" id="mkin-09-36-2015-06-21">mkin 0.9-36 (2015-06-21)<a class="anchor" aria-label="anchor" href="#mkin-09-36-2015-06-21"></a></h2> @@ -437,13 +487,15 @@ </ul></div> <div class="section level3"> <h3 id="minor-changes-0-9-36">Minor changes<a class="anchor" aria-label="anchor" href="#minor-changes-0-9-36"></a></h3> -<ul><li>Added a simple showcase vignette with an evaluation of FOCUS example dataset D</li></ul></div> +<ul><li>Added a simple showcase vignette with an evaluation of FOCUS example dataset D</li> +</ul></div> </div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9-35" id="mkin-09-35-2015-05-15">mkin 0.9-35 (2015-05-15)<a class="anchor" aria-label="anchor" href="#mkin-09-35-2015-05-15"></a></h2> <div class="section level3"> <h3 id="major-changes-0-9-35">Major changes<a class="anchor" aria-label="anchor" href="#major-changes-0-9-35"></a></h3> -<ul><li>Switch from RUnit to testthat for testing</li></ul></div> +<ul><li>Switch from RUnit to testthat for testing</li> +</ul></div> <div class="section level3"> <h3 id="bug-fixes-0-9-35">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-35"></a></h3> <ul><li><p><code><a href="../reference/mkinparplot.html">mkinparplot()</a></code>: Avoid warnings that occurred when not all confidence intervals were available in the summary of the fit</p></li> @@ -525,13 +577,15 @@ <h2 class="page-header" data-toc-text="0.9-31" id="mkin-09-31-2014-07-14">mkin 0.9-31 (2014-07-14)<a class="anchor" aria-label="anchor" href="#mkin-09-31-2014-07-14"></a></h2> <div class="section level3"> <h3 id="bug-fixes-0-9-31">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-31"></a></h3> -<ul><li>The internal renaming of optimised parameters in Version 0.9-30 led to errors in the determination of the degrees of freedom for the chi2 error level calulations in <code><a href="../reference/mkinerrmin.html">mkinerrmin()</a></code> used by the summary function.</li></ul></div> +<ul><li>The internal renaming of optimised parameters in Version 0.9-30 led to errors in the determination of the degrees of freedom for the chi2 error level calulations in <code><a href="../reference/mkinerrmin.html">mkinerrmin()</a></code> used by the summary function.</li> +</ul></div> </div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9-30" id="mkin-09-30-2014-07-11">mkin 0.9-30 (2014-07-11)<a class="anchor" aria-label="anchor" href="#mkin-09-30-2014-07-11"></a></h2> <div class="section level3"> <h3 id="new-features-0-9-30">New features<a class="anchor" aria-label="anchor" href="#new-features-0-9-30"></a></h3> -<ul><li>It is now possible to use formation fractions in combination with turning off the sink in <code><a href="../reference/mkinmod.html">mkinmod()</a></code>.</li></ul></div> +<ul><li>It is now possible to use formation fractions in combination with turning off the sink in <code><a href="../reference/mkinmod.html">mkinmod()</a></code>.</li> +</ul></div> <div class="section level3"> <h3 id="major-changes-0-9-30">Major changes<a class="anchor" aria-label="anchor" href="#major-changes-0-9-30"></a></h3> <ul><li><p>The original and the transformed parameters now have different names (e.g. <code>k_parent</code> and <code>log_k_parent</code>. They also differ in how many they are when we have formation fractions but no pathway to sink.</p></li> @@ -614,7 +668,7 @@ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/dev/pkgdown.yml b/docs/dev/pkgdown.yml index e0c11a84..c8fdd89a 100644 --- a/docs/dev/pkgdown.yml +++ b/docs/dev/pkgdown.yml @@ -1,10 +1,13 @@ -pandoc: 2.9.2.1 -pkgdown: 2.0.6 +pandoc: 2.17.1.1 +pkgdown: 2.0.7 pkgdown_sha: ~ articles: FOCUS_D: FOCUS_D.html FOCUS_L: FOCUS_L.html mkin: mkin.html + 2022_cyan_pathway: prebuilt/2022_cyan_pathway.html + 2022_dmta_parent: prebuilt/2022_dmta_parent.html + 2022_dmta_pathway: prebuilt/2022_dmta_pathway.html twa: twa.html FOCUS_Z: web_only/FOCUS_Z.html NAFTA_examples: web_only/NAFTA_examples.html @@ -13,7 +16,7 @@ articles: dimethenamid_2018: web_only/dimethenamid_2018.html multistart: web_only/multistart.html saem_benchmarks: web_only/saem_benchmarks.html -last_built: 2022-11-14T23:45Z +last_built: 2023-01-28T16:59Z urls: reference: https://pkgdown.jrwb.de/mkin/reference article: https://pkgdown.jrwb.de/mkin/articles diff --git a/docs/dev/reference/AIC.mmkin.html b/docs/dev/reference/AIC.mmkin.html index 8c791755..13f870e7 100644 --- a/docs/dev/reference/AIC.mmkin.html +++ b/docs/dev/reference/AIC.mmkin.html @@ -1,68 +1,13 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - <head> - <meta charset="utf-8"> -<meta http-equiv="X-UA-Compatible" content="IE=edge"> -<meta name="viewport" content="width=device-width, initial-scale=1.0"> - -<title>Calculate the AIC for a column of an mmkin object — AIC.mmkin • mkin</title> - - -<!-- jquery --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" 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class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -73,23 +18,21 @@ same dataset." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -99,48 +42,50 @@ same dataset." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Calculate the AIC for a column of an mmkin object</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/AIC.mmkin.R'><code>R/AIC.mmkin.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/AIC.mmkin.R" class="external-link"><code>R/AIC.mmkin.R</code></a></small> <div class="hidden name"><code>AIC.mmkin.Rd</code></div> </div> @@ -149,102 +94,110 @@ same dataset." /> same dataset.</p> </div> - <pre class="usage"><span class='co'># S3 method for mmkin</span> -<span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>object</span>, <span class='va'>...</span>, k <span class='op'>=</span> <span class='fl'>2</span><span class='op'>)</span> - -<span class='co'># S3 method for mmkin</span> -<span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>BIC</a></span><span class='op'>(</span><span class='va'>object</span>, <span class='va'>...</span><span class='op'>)</span></pre> - - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>object</th> - <td><p>An object of class <code><a href='mmkin.html'>mmkin</a></code>, containing only one -column.</p></td> - </tr> - <tr> - <th>...</th> - <td><p>For compatibility with the generic method</p></td> - </tr> - <tr> - <th>k</th> - <td><p>As in the generic method</p></td> - </tr> - </table> - - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> - - <p>As in the generic method (a numeric value for single fits, or a -dataframe if there are several fits in the column).</p> - <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span>, k <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">BIC</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>object</dt> +<dd><p>An object of class <code><a href="mmkin.html">mmkin</a></code>, containing only one +column.</p></dd> + +<dt>...</dt> +<dd><p>For compatibility with the generic method</p></dd> + + +<dt>k</dt> +<dd><p>As in the generic method</p></dd> + +</dl></div> + <div id="value"> + <h2>Value</h2> + + +<p>As in the generic method (a numeric value for single fits, or a +dataframe if there are several fits in the column).</p> + </div> + <div id="author"> + <h2>Author</h2> <p>Johannes Ranke</p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> - <span class='co'># skip, as it takes > 10 s on winbuilder</span> - <span class='va'>f</span> <span class='op'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"FOMC"</span>, <span class='st'>"DFOP"</span><span class='op'>)</span>, - <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='st'>"FOCUS A"</span> <span class='op'>=</span> <span class='va'>FOCUS_2006_A</span>, - <span class='st'>"FOCUS C"</span> <span class='op'>=</span> <span class='va'>FOCUS_2006_C</span><span class='op'>)</span>, cores <span class='op'>=</span> <span class='fl'>1</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> - <span class='co'># We get a warning because the FOMC model does not converge for the</span> - <span class='co'># FOCUS A dataset, as it is well described by SFO</span> - - <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f</span><span class='op'>[</span><span class='st'>"SFO"</span>, <span class='st'>"FOCUS A"</span><span class='op'>]</span><span class='op'>)</span> <span class='co'># We get a single number for a single fit</span> -</div><div class='output co'>#> [1] 55.28197</div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f</span><span class='op'>[[</span><span class='st'>"SFO"</span>, <span class='st'>"FOCUS A"</span><span class='op'>]</span><span class='op'>]</span><span class='op'>)</span> <span class='co'># or when extracting an mkinfit object</span> -</div><div class='output co'>#> [1] 55.28197</div><div class='input'> - <span class='co'># For FOCUS A, the models fit almost equally well, so the higher the number</span> - <span class='co'># of parameters, the higher (worse) the AIC</span> - <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f</span><span class='op'>[</span>, <span class='st'>"FOCUS A"</span><span class='op'>]</span><span class='op'>)</span> -</div><div class='output co'>#> df AIC -#> SFO 3 55.28197 -#> FOMC 4 57.28222 -#> DFOP 5 59.28197</div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f</span><span class='op'>[</span>, <span class='st'>"FOCUS A"</span><span class='op'>]</span>, k <span class='op'>=</span> <span class='fl'>0</span><span class='op'>)</span> <span class='co'># If we do not penalize additional parameters, we get nearly the same</span> -</div><div class='output co'>#> df AIC -#> SFO 3 49.28197 -#> FOMC 4 49.28222 -#> DFOP 5 49.28197</div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>BIC</a></span><span class='op'>(</span><span class='va'>f</span><span class='op'>[</span>, <span class='st'>"FOCUS A"</span><span class='op'>]</span><span class='op'>)</span> <span class='co'># Comparing the BIC gives a very similar picture</span> -</div><div class='output co'>#> df BIC -#> SFO 3 55.52030 -#> FOMC 4 57.59999 -#> DFOP 5 59.67918</div><div class='input'> - <span class='co'># For FOCUS C, the more complex models fit better</span> - <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f</span><span class='op'>[</span>, <span class='st'>"FOCUS C"</span><span class='op'>]</span><span class='op'>)</span> -</div><div class='output co'>#> df AIC -#> SFO 3 59.29336 -#> FOMC 4 44.68652 -#> DFOP 5 29.02372</div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>BIC</a></span><span class='op'>(</span><span class='va'>f</span><span class='op'>[</span>, <span class='st'>"FOCUS C"</span><span class='op'>]</span><span class='op'>)</span> -</div><div class='output co'>#> df BIC -#> SFO 3 59.88504 -#> FOMC 4 45.47542 -#> DFOP 5 30.00984</div><div class='input'> - -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># skip, as it takes > 10 s on winbuilder</span></span></span> +<span class="r-in"><span> <span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS A"</span> <span class="op">=</span> <span class="va">FOCUS_2006_A</span>,</span></span> +<span class="r-in"><span> <span class="st">"FOCUS C"</span> <span class="op">=</span> <span class="va">FOCUS_2006_C</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="co"># We get a warning because the FOMC model does not converge for the</span></span></span> +<span class="r-in"><span> <span class="co"># FOCUS A dataset, as it is well described by SFO</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="st">"FOCUS A"</span><span class="op">]</span><span class="op">)</span> <span class="co"># We get a single number for a single fit</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] 55.28197</span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[[</span><span class="st">"SFO"</span>, <span class="st">"FOCUS A"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span> <span class="co"># or when extracting an mkinfit object</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] 55.28197</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># For FOCUS A, the models fit almost equally well, so the higher the number</span></span></span> +<span class="r-in"><span> <span class="co"># of parameters, the higher (worse) the AIC</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="st">"FOCUS A"</span><span class="op">]</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> df AIC</span> +<span class="r-out co"><span class="r-pr">#></span> SFO 3 55.28197</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC 4 57.28222</span> +<span class="r-out co"><span class="r-pr">#></span> DFOP 5 59.28197</span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="st">"FOCUS A"</span><span class="op">]</span>, k <span class="op">=</span> <span class="fl">0</span><span class="op">)</span> <span class="co"># If we do not penalize additional parameters, we get nearly the same</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> df AIC</span> +<span class="r-out co"><span class="r-pr">#></span> SFO 3 49.28197</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC 4 49.28222</span> +<span class="r-out co"><span class="r-pr">#></span> DFOP 5 49.28197</span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">BIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="st">"FOCUS A"</span><span class="op">]</span><span class="op">)</span> <span class="co"># Comparing the BIC gives a very similar picture</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> df BIC</span> +<span class="r-out co"><span class="r-pr">#></span> SFO 3 55.52030</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC 4 57.59999</span> +<span class="r-out co"><span class="r-pr">#></span> DFOP 5 59.67918</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># For FOCUS C, the more complex models fit better</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="st">"FOCUS C"</span><span class="op">]</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> df AIC</span> +<span class="r-out co"><span class="r-pr">#></span> SFO 3 59.29336</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC 4 44.68652</span> +<span class="r-out co"><span class="r-pr">#></span> DFOP 5 29.02372</span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">BIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="st">"FOCUS C"</span><span class="op">]</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> df BIC</span> +<span class="r-out co"><span class="r-pr">#></span> SFO 3 59.88504</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC 4 45.47542</span> +<span class="r-out co"><span class="r-pr">#></span> DFOP 5 30.00984</span> +<span class="r-in"><span> </span></span> +<span class="r-in"><span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/CAKE_export.html b/docs/dev/reference/CAKE_export.html index 47efd056..33ae3f74 100644 --- a/docs/dev/reference/CAKE_export.html +++ b/docs/dev/reference/CAKE_export.html @@ -18,7 +18,7 @@ specified as well."><meta name="robots" content="noindex"><!-- mathjax --><scrip </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.2</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -45,19 +45,25 @@ specified as well."><meta name="robots" content="noindex"><!-- mathjax --><scrip <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/D24_2014.html b/docs/dev/reference/D24_2014.html index 5cf7604c..14840260 100644 --- a/docs/dev/reference/D24_2014.html +++ b/docs/dev/reference/D24_2014.html @@ -22,7 +22,7 @@ constrained by data protection regulations."><meta name="robots" content="noinde </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.2</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -49,19 +49,25 @@ constrained by data protection regulations."><meta name="robots" content="noinde <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/DFOP.solution.html b/docs/dev/reference/DFOP.solution.html index f41d8e9b..c6746fe7 100644 --- a/docs/dev/reference/DFOP.solution.html +++ b/docs/dev/reference/DFOP.solution.html @@ -18,7 +18,7 @@ two exponential decline functions."><meta name="robots" content="noindex"><!-- m </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -60,7 +60,10 @@ two exponential decline functions."><meta name="robots" content="noindex"><!-- m <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/Extract.mmkin.html b/docs/dev/reference/Extract.mmkin.html index 8381337a..cd863616 100644 --- a/docs/dev/reference/Extract.mmkin.html +++ b/docs/dev/reference/Extract.mmkin.html @@ -1,67 +1,12 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - <head> - <meta charset="utf-8"> -<meta http-equiv="X-UA-Compatible" content="IE=edge"> -<meta name="viewport" content="width=device-width, initial-scale=1.0"> - -<title>Subsetting 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<div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -72,23 +17,21 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -98,48 +41,50 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Subsetting method for mmkin objects</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/mmkin.R'><code>R/mmkin.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mmkin.R" class="external-link"><code>R/mmkin.R</code></a></small> <div class="hidden name"><code>Extract.mmkin.Rd</code></div> </div> @@ -147,122 +92,127 @@ <p>Subsetting method for mmkin objects</p> </div> - <pre class="usage"># S3 method for mmkin -[(x, i, j, ..., drop = FALSE)</pre> + <div id="ref-usage"> + <div class="sourceCode"><pre><code># S3 method for mmkin +[(x, i, j, ..., drop = FALSE)</code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>x</dt> +<dd><p>An <code><a href="mmkin.html">mmkin</a> object</code></p></dd> - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>x</th> - <td><p>An <code><a href='mmkin.html'>mmkin</a> object</code></p></td> - </tr> - <tr> - <th>i</th> - <td><p>Row index selecting the fits for specific models</p></td> - </tr> - <tr> - <th>j</th> - <td><p>Column index selecting the fits to specific datasets</p></td> - </tr> - <tr> - <th>...</th> - <td><p>Not used, only there to satisfy the generic method definition</p></td> - </tr> - <tr> - <th>drop</th> - <td><p>If FALSE, the method always returns an mmkin object, otherwise -either a list of mkinfit objects or a single mkinfit object.</p></td> - </tr> - </table> - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> +<dt>i</dt> +<dd><p>Row index selecting the fits for specific models</p></dd> - <p>An object of class <code><a href='mmkin.html'>mmkin</a></code>.</p> - <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2> +<dt>j</dt> +<dd><p>Column index selecting the fits to specific datasets</p></dd> + + +<dt>...</dt> +<dd><p>Not used, only there to satisfy the generic method definition</p></dd> + + +<dt>drop</dt> +<dd><p>If FALSE, the method always returns an mmkin object, otherwise +either a list of mkinfit objects or a single mkinfit object.</p></dd> + +</dl></div> + <div id="value"> + <h2>Value</h2> + + +<p>An object of class <code><a href="mmkin.html">mmkin</a></code>.</p> + </div> + <div id="author"> + <h2>Author</h2> <p>Johannes Ranke</p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> - <span class='co'># Only use one core, to pass R CMD check --as-cran</span> - <span class='va'>fits</span> <span class='op'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"FOMC"</span><span class='op'>)</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>B <span class='op'>=</span> <span class='va'>FOCUS_2006_B</span>, C <span class='op'>=</span> <span class='va'>FOCUS_2006_C</span><span class='op'>)</span>, - cores <span class='op'>=</span> <span class='fl'>1</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> - <span class='va'>fits</span><span class='op'>[</span><span class='st'>"FOMC"</span>, <span class='op'>]</span> -</div><div class='output co'>#> <mmkin> object -#> Status of individual fits: -#> -#> dataset -#> model B C -#> FOMC OK OK -#> -#> OK: No warnings</div><div class='input'> <span class='va'>fits</span><span class='op'>[</span>, <span class='st'>"B"</span><span class='op'>]</span> -</div><div class='output co'>#> <mmkin> object -#> Status of individual fits: -#> -#> dataset -#> model B -#> SFO OK -#> FOMC OK -#> -#> OK: No warnings</div><div class='input'> <span class='va'>fits</span><span class='op'>[</span><span class='st'>"SFO"</span>, <span class='st'>"B"</span><span class='op'>]</span> -</div><div class='output co'>#> <mmkin> object -#> Status of individual fits: -#> -#> dataset -#> model B -#> SFO OK -#> -#> OK: No warnings</div><div class='input'> - <span class='fu'><a href='https://rdrr.io/r/utils/head.html'>head</a></span><span class='op'>(</span> - <span class='co'># This extracts an mkinfit object with lots of components</span> - <span class='va'>fits</span><span class='op'>[[</span><span class='st'>"FOMC"</span>, <span class='st'>"B"</span><span class='op'>]</span><span class='op'>]</span> - <span class='op'>)</span> -</div><div class='output co'>#> $par -#> parent_0 log_alpha log_beta sigma -#> 99.666192 2.549850 5.050586 1.890202 -#> -#> $objective -#> [1] 28.58291 -#> -#> $convergence -#> [1] 0 -#> -#> $iterations -#> [1] 21 -#> -#> $evaluations -#> function gradient -#> 25 78 -#> -#> $message -#> [1] "both X-convergence and relative convergence (5)" -#> </div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># Only use one core, to pass R CMD check --as-cran</span></span></span> +<span class="r-in"><span> <span class="va">fits</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>B <span class="op">=</span> <span class="va">FOCUS_2006_B</span>, C <span class="op">=</span> <span class="va">FOCUS_2006_C</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">fits</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> <mmkin> object</span> +<span class="r-out co"><span class="r-pr">#></span> Status of individual fits:</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> dataset</span> +<span class="r-out co"><span class="r-pr">#></span> model B C </span> +<span class="r-out co"><span class="r-pr">#></span> FOMC OK OK</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> OK: No warnings</span> +<span class="r-in"><span> <span class="va">fits</span><span class="op">[</span>, <span class="st">"B"</span><span class="op">]</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> <mmkin> object</span> +<span class="r-out co"><span class="r-pr">#></span> Status of individual fits:</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> dataset</span> +<span class="r-out co"><span class="r-pr">#></span> model B </span> +<span class="r-out co"><span class="r-pr">#></span> SFO OK</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC OK</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> OK: No warnings</span> +<span class="r-in"><span> <span class="va">fits</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="st">"B"</span><span class="op">]</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> <mmkin> object</span> +<span class="r-out co"><span class="r-pr">#></span> Status of individual fits:</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> dataset</span> +<span class="r-out co"><span class="r-pr">#></span> model B </span> +<span class="r-out co"><span class="r-pr">#></span> SFO OK</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> OK: No warnings</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> <span class="co"># This extracts an mkinfit object with lots of components</span></span></span> +<span class="r-in"><span> <span class="va">fits</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="st">"B"</span><span class="op">]</span><span class="op">]</span></span></span> +<span class="r-in"><span> <span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> $par</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 log_alpha log_beta sigma </span> +<span class="r-out co"><span class="r-pr">#></span> 99.666192 2.549850 5.050586 1.890202 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> $objective</span> +<span class="r-out co"><span class="r-pr">#></span> [1] 28.58291</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> $convergence</span> +<span class="r-out co"><span class="r-pr">#></span> [1] 0</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> $iterations</span> +<span class="r-out co"><span class="r-pr">#></span> [1] 21</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> $evaluations</span> +<span class="r-out co"><span class="r-pr">#></span> function gradient </span> +<span class="r-out co"><span class="r-pr">#></span> 25 78 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> $message</span> +<span class="r-out co"><span class="r-pr">#></span> [1] "both X-convergence and relative convergence (5)"</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/FOCUS_2006_DFOP_ref_A_to_B.html b/docs/dev/reference/FOCUS_2006_DFOP_ref_A_to_B.html index a188430d..8891567a 100644 --- a/docs/dev/reference/FOCUS_2006_DFOP_ref_A_to_B.html +++ b/docs/dev/reference/FOCUS_2006_DFOP_ref_A_to_B.html @@ -1,71 +1,16 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - <head> - <meta charset="utf-8"> -<meta http-equiv="X-UA-Compatible" content="IE=edge"> -<meta name="viewport" content="width=device-width, initial-scale=1.0"> - -<title>Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) — FOCUS_2006_DFOP_ref_A_to_B • mkin</title> - - -<!-- jquery --> 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Taken directly from FOCUS (2006). The results from fitting the data with the Topfit software was removed, as the initial concentration of the parent compound was fixed to a value of 100 -in this fit." /> - - -<meta name="robots" content="noindex"> - -<!-- mathjax --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script> - -<!--[if lt IE 9]> +in this fit."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> - - - - </head> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -76,23 +21,21 @@ in this fit." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -102,44 +45,46 @@ in this fit." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Results of fitting the DFOP model to Datasets A to B of FOCUS (2006)</h1> @@ -155,59 +100,73 @@ the initial concentration of the parent compound was fixed to a value of 100 in this fit.</p> </div> - <pre class="usage"><span class='va'>FOCUS_2006_DFOP_ref_A_to_B</span></pre> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">FOCUS_2006_DFOP_ref_A_to_B</span></span></code></pre></div> + </div> + <div id="format"> + <h2>Format</h2> + <p>A data frame containing the following variables.</p><dl><dt><code>package</code></dt> +<dd><p>a factor giving the name of the software package</p></dd> - <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2> + <dt><code>M0</code></dt> +<dd><p>The fitted initial concentration of the parent compound</p></dd> - <p>A data frame containing the following variables.</p><dl> - <dt><code>package</code></dt><dd><p>a factor giving the name of the software package</p></dd> - <dt><code>M0</code></dt><dd><p>The fitted initial concentration of the parent compound</p></dd> - <dt><code>f</code></dt><dd><p>The fitted f parameter</p></dd> - <dt><code>k1</code></dt><dd><p>The fitted k1 parameter</p></dd> - <dt><code>k2</code></dt><dd><p>The fitted k2 parameter</p></dd> - <dt><code>DT50</code></dt><dd><p>The resulting half-life of the parent compound</p></dd> - <dt><code>DT90</code></dt><dd><p>The resulting DT90 of the parent compound</p></dd> - <dt><code>dataset</code></dt><dd><p>The FOCUS dataset that was used</p></dd> - -</dl> + <dt><code>f</code></dt> +<dd><p>The fitted f parameter</p></dd> + + <dt><code>k1</code></dt> +<dd><p>The fitted k1 parameter</p></dd> + + <dt><code>k2</code></dt> +<dd><p>The fitted k2 parameter</p></dd> + + <dt><code>DT50</code></dt> +<dd><p>The resulting half-life of the parent compound</p></dd> + + <dt><code>DT90</code></dt> +<dd><p>The resulting DT90 of the parent compound</p></dd> - <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2> + <dt><code>dataset</code></dt> +<dd><p>The FOCUS dataset that was used</p></dd> - <p>FOCUS (2006) “Guidance Document on Estimating Persistence and + +</dl></div> + <div id="source"> + <h2>Source</h2> + <p>FOCUS (2006) “Guidance Document on Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU - Registration” Report of the FOCUS Work Group on Degradation Kinetics, + Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, - <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'><span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>FOCUS_2006_DFOP_ref_A_to_B</span><span class='op'>)</span> -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">FOCUS_2006_DFOP_ref_A_to_B</span><span class="op">)</span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/FOCUS_2006_FOMC_ref_A_to_F.html b/docs/dev/reference/FOCUS_2006_FOMC_ref_A_to_F.html index 0bee1c16..a725ada7 100644 --- a/docs/dev/reference/FOCUS_2006_FOMC_ref_A_to_F.html +++ b/docs/dev/reference/FOCUS_2006_FOMC_ref_A_to_F.html @@ -1,71 +1,16 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - <head> - <meta charset="utf-8"> -<meta http-equiv="X-UA-Compatible" content="IE=edge"> -<meta name="viewport" content="width=device-width, initial-scale=1.0"> - -<title>Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F • mkin</title> - - -<!-- jquery --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script> -<!-- Bootstrap --> - -<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous" /> - -<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script> - -<!-- bootstrap-toc --> -<link rel="stylesheet" href="../bootstrap-toc.css"> -<script src="../bootstrap-toc.js"></script> - 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-<!-- pkgdown --> -<link href="../pkgdown.css" rel="stylesheet"> -<script src="../pkgdown.js"></script> - - - - -<meta property="og:title" content="Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F" /> -<meta property="og:description" content="A table with the fitted parameters and the resulting DT50 and DT90 values +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) — FOCUS_2006_FOMC_ref_A_to_F"><meta property="og:description" content="A table with the fitted parameters and the resulting DT50 and DT90 values generated with different software packages. Taken directly from FOCUS (2006). The results from fitting the data with the Topfit software was removed, as the initial concentration of the parent compound was fixed to a value of 100 -in this fit." /> - - -<meta name="robots" content="noindex"> - -<!-- mathjax --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script> - -<!--[if lt IE 9]> +in this fit."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> - - - - </head> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -76,23 +21,21 @@ in this fit." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -102,44 +45,46 @@ in this fit." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Results of fitting the FOMC model to Datasets A to F of FOCUS (2006)</h1> @@ -155,58 +100,70 @@ the initial concentration of the parent compound was fixed to a value of 100 in this fit.</p> </div> - <pre class="usage"><span class='va'>FOCUS_2006_FOMC_ref_A_to_F</span></pre> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">FOCUS_2006_FOMC_ref_A_to_F</span></span></code></pre></div> + </div> + <div id="format"> + <h2>Format</h2> + <p>A data frame containing the following variables.</p><dl><dt><code>package</code></dt> +<dd><p>a factor giving the name of the software package</p></dd> - <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2> + <dt><code>M0</code></dt> +<dd><p>The fitted initial concentration of the parent compound</p></dd> - <p>A data frame containing the following variables.</p><dl> - <dt><code>package</code></dt><dd><p>a factor giving the name of the software package</p></dd> - <dt><code>M0</code></dt><dd><p>The fitted initial concentration of the parent compound</p></dd> - <dt><code>alpha</code></dt><dd><p>The fitted alpha parameter</p></dd> - <dt><code>beta</code></dt><dd><p>The fitted beta parameter</p></dd> - <dt><code>DT50</code></dt><dd><p>The resulting half-life of the parent compound</p></dd> - <dt><code>DT90</code></dt><dd><p>The resulting DT90 of the parent compound</p></dd> - <dt><code>dataset</code></dt><dd><p>The FOCUS dataset that was used</p></dd> - -</dl> + <dt><code>alpha</code></dt> +<dd><p>The fitted alpha parameter</p></dd> + + <dt><code>beta</code></dt> +<dd><p>The fitted beta parameter</p></dd> + + <dt><code>DT50</code></dt> +<dd><p>The resulting half-life of the parent compound</p></dd> + + <dt><code>DT90</code></dt> +<dd><p>The resulting DT90 of the parent compound</p></dd> - <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2> + <dt><code>dataset</code></dt> +<dd><p>The FOCUS dataset that was used</p></dd> - <p>FOCUS (2006) “Guidance Document on Estimating Persistence and + +</dl></div> + <div id="source"> + <h2>Source</h2> + <p>FOCUS (2006) “Guidance Document on Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU - Registration” Report of the FOCUS Work Group on Degradation Kinetics, + Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, - <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'><span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>FOCUS_2006_FOMC_ref_A_to_F</span><span class='op'>)</span> -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">FOCUS_2006_FOMC_ref_A_to_F</span><span class="op">)</span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/FOCUS_2006_HS_ref_A_to_F.html b/docs/dev/reference/FOCUS_2006_HS_ref_A_to_F.html index 460fdf0d..d6758f62 100644 --- a/docs/dev/reference/FOCUS_2006_HS_ref_A_to_F.html +++ b/docs/dev/reference/FOCUS_2006_HS_ref_A_to_F.html @@ -1,71 +1,16 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - 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Taken directly from FOCUS (2006). The results from fitting the data with the Topfit software was removed, as the initial concentration of the parent compound was fixed to a value of 100 -in this fit." /> - - -<meta name="robots" content="noindex"> - -<!-- mathjax --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script> - -<!--[if lt IE 9]> +in this fit."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> - - - - </head> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -76,23 +21,21 @@ in this fit." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -102,44 +45,46 @@ in this fit." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Results of fitting the HS model to Datasets A to F of FOCUS (2006)</h1> @@ -155,59 +100,73 @@ the initial concentration of the parent compound was fixed to a value of 100 in this fit.</p> </div> - <pre class="usage"><span class='va'>FOCUS_2006_HS_ref_A_to_F</span></pre> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">FOCUS_2006_HS_ref_A_to_F</span></span></code></pre></div> + </div> + <div id="format"> + <h2>Format</h2> + <p>A data frame containing the following variables.</p><dl><dt><code>package</code></dt> +<dd><p>a factor giving the name of the software package</p></dd> - <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2> + <dt><code>M0</code></dt> +<dd><p>The fitted initial concentration of the parent compound</p></dd> - <p>A data frame containing the following variables.</p><dl> - <dt><code>package</code></dt><dd><p>a factor giving the name of the software package</p></dd> - <dt><code>M0</code></dt><dd><p>The fitted initial concentration of the parent compound</p></dd> - <dt><code>tb</code></dt><dd><p>The fitted tb parameter</p></dd> - <dt><code>k1</code></dt><dd><p>The fitted k1 parameter</p></dd> - <dt><code>k2</code></dt><dd><p>The fitted k2 parameter</p></dd> - <dt><code>DT50</code></dt><dd><p>The resulting half-life of the parent compound</p></dd> - <dt><code>DT90</code></dt><dd><p>The resulting DT90 of the parent compound</p></dd> - <dt><code>dataset</code></dt><dd><p>The FOCUS dataset that was used</p></dd> - -</dl> + <dt><code>tb</code></dt> +<dd><p>The fitted tb parameter</p></dd> + + <dt><code>k1</code></dt> +<dd><p>The fitted k1 parameter</p></dd> + + <dt><code>k2</code></dt> +<dd><p>The fitted k2 parameter</p></dd> + + <dt><code>DT50</code></dt> +<dd><p>The resulting half-life of the parent compound</p></dd> + + <dt><code>DT90</code></dt> +<dd><p>The resulting DT90 of the parent compound</p></dd> - <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2> + <dt><code>dataset</code></dt> +<dd><p>The FOCUS dataset that was used</p></dd> - <p>FOCUS (2006) “Guidance Document on Estimating Persistence and + +</dl></div> + <div id="source"> + <h2>Source</h2> + <p>FOCUS (2006) “Guidance Document on Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU - Registration” Report of the FOCUS Work Group on Degradation Kinetics, + Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, - <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'><span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>FOCUS_2006_HS_ref_A_to_F</span><span class='op'>)</span> -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">FOCUS_2006_HS_ref_A_to_F</span><span class="op">)</span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/FOCUS_2006_SFO_ref_A_to_F.html b/docs/dev/reference/FOCUS_2006_SFO_ref_A_to_F.html index c1a5fdff..445ed2d6 100644 --- a/docs/dev/reference/FOCUS_2006_SFO_ref_A_to_F.html +++ b/docs/dev/reference/FOCUS_2006_SFO_ref_A_to_F.html @@ -1,71 +1,16 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - 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Taken directly from FOCUS (2006). The results from fitting the data with the Topfit software was removed, as the initial concentration of the parent compound was fixed to a value of 100 -in this fit." /> - - -<meta name="robots" content="noindex"> - -<!-- mathjax --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script> - -<!--[if lt IE 9]> +in this fit."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> - - - - </head> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -76,23 +21,21 @@ in this fit." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -102,44 +45,46 @@ in this fit." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Results of fitting the SFO model to Datasets A to F of FOCUS (2006)</h1> @@ -155,57 +100,67 @@ the initial concentration of the parent compound was fixed to a value of 100 in this fit.</p> </div> - <pre class="usage"><span class='va'>FOCUS_2006_SFO_ref_A_to_F</span></pre> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">FOCUS_2006_SFO_ref_A_to_F</span></span></code></pre></div> + </div> + <div id="format"> + <h2>Format</h2> + <p>A data frame containing the following variables.</p><dl><dt><code>package</code></dt> +<dd><p>a factor giving the name of the software package</p></dd> - <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2> + <dt><code>M0</code></dt> +<dd><p>The fitted initial concentration of the parent compound</p></dd> - <p>A data frame containing the following variables.</p><dl> - <dt><code>package</code></dt><dd><p>a factor giving the name of the software package</p></dd> - <dt><code>M0</code></dt><dd><p>The fitted initial concentration of the parent compound</p></dd> - <dt><code>k</code></dt><dd><p>The fitted first-order degradation rate constant</p></dd> - <dt><code>DT50</code></dt><dd><p>The resulting half-life of the parent compound</p></dd> - <dt><code>DT90</code></dt><dd><p>The resulting DT90 of the parent compound</p></dd> - <dt><code>dataset</code></dt><dd><p>The FOCUS dataset that was used</p></dd> - -</dl> + <dt><code>k</code></dt> +<dd><p>The fitted first-order degradation rate constant</p></dd> + + <dt><code>DT50</code></dt> +<dd><p>The resulting half-life of the parent compound</p></dd> + + <dt><code>DT90</code></dt> +<dd><p>The resulting DT90 of the parent compound</p></dd> - <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2> + <dt><code>dataset</code></dt> +<dd><p>The FOCUS dataset that was used</p></dd> - <p>FOCUS (2006) “Guidance Document on Estimating Persistence and + +</dl></div> + <div id="source"> + <h2>Source</h2> + <p>FOCUS (2006) “Guidance Document on Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU - Registration” Report of the FOCUS Work Group on Degradation Kinetics, + Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, - <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'><span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span><span class='op'>(</span><span class='va'>FOCUS_2006_SFO_ref_A_to_F</span><span class='op'>)</span> -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">FOCUS_2006_SFO_ref_A_to_F</span><span class="op">)</span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/FOCUS_2006_datasets.html b/docs/dev/reference/FOCUS_2006_datasets.html index fb3a8f17..955b9188 100644 --- a/docs/dev/reference/FOCUS_2006_datasets.html +++ b/docs/dev/reference/FOCUS_2006_datasets.html @@ -1,67 +1,12 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - 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- - - </head> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -72,23 +17,21 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -98,44 +41,46 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Datasets A to F from the FOCUS Kinetics report from 2006</h1> @@ -147,68 +92,73 @@ <p>Data taken from FOCUS (2006), p. 258.</p> </div> - <pre class="usage"><span class='va'>FOCUS_2006_A</span> - <span class='va'>FOCUS_2006_B</span> - <span class='va'>FOCUS_2006_C</span> - <span class='va'>FOCUS_2006_D</span> - <span class='va'>FOCUS_2006_E</span> - <span class='va'>FOCUS_2006_F</span></pre> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">FOCUS_2006_A</span></span> +<span> <span class="va">FOCUS_2006_B</span></span> +<span> <span class="va">FOCUS_2006_C</span></span> +<span> <span class="va">FOCUS_2006_D</span></span> +<span> <span class="va">FOCUS_2006_E</span></span> +<span> <span class="va">FOCUS_2006_F</span></span></code></pre></div> + </div> + <div id="format"> + <h2>Format</h2> + <p>6 datasets with observations on the following variables.</p><dl><dt><code>name</code></dt> +<dd><p>a factor containing the name of the observed variable</p></dd> - <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2> + <dt><code>time</code></dt> +<dd><p>a numeric vector containing time points</p></dd> - <p>6 datasets with observations on the following variables.</p><dl> - <dt><code>name</code></dt><dd><p>a factor containing the name of the observed variable</p></dd> - <dt><code>time</code></dt><dd><p>a numeric vector containing time points</p></dd> - <dt><code>value</code></dt><dd><p>a numeric vector containing concentrations in percent of applied radioactivity</p></dd> - -</dl> + <dt><code>value</code></dt> +<dd><p>a numeric vector containing concentrations in percent of applied radioactivity</p></dd> - <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2> - - <p>FOCUS (2006) “Guidance Document on Estimating Persistence and + +</dl></div> + <div id="source"> + <h2>Source</h2> + <p>FOCUS (2006) “Guidance Document on Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU - Registration” Report of the FOCUS Work Group on Degradation Kinetics, + Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, - <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> - - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'><span class='va'>FOCUS_2006_C</span> -</div><div class='output co'>#> name time value -#> 1 parent 0 85.1 -#> 2 parent 1 57.9 -#> 3 parent 3 29.9 -#> 4 parent 7 14.6 -#> 5 parent 14 9.7 -#> 6 parent 28 6.6 -#> 7 parent 63 4.0 -#> 8 parent 91 3.9 -#> 9 parent 119 0.6</div></pre> + <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + </div> + + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">FOCUS_2006_C</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> name time value</span> +<span class="r-out co"><span class="r-pr">#></span> 1 parent 0 85.1</span> +<span class="r-out co"><span class="r-pr">#></span> 2 parent 1 57.9</span> +<span class="r-out co"><span class="r-pr">#></span> 3 parent 3 29.9</span> +<span class="r-out co"><span class="r-pr">#></span> 4 parent 7 14.6</span> +<span class="r-out co"><span class="r-pr">#></span> 5 parent 14 9.7</span> +<span class="r-out co"><span class="r-pr">#></span> 6 parent 28 6.6</span> +<span class="r-out co"><span class="r-pr">#></span> 7 parent 63 4.0</span> +<span class="r-out co"><span class="r-pr">#></span> 8 parent 91 3.9</span> +<span class="r-out co"><span class="r-pr">#></span> 9 parent 119 0.6</span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/FOMC.solution.html b/docs/dev/reference/FOMC.solution.html index f64e759c..e1f19dc1 100644 --- a/docs/dev/reference/FOMC.solution.html +++ b/docs/dev/reference/FOMC.solution.html @@ -18,7 +18,7 @@ a decreasing rate constant."><meta name="robots" content="noindex"><!-- mathjax </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -60,7 +60,10 @@ a decreasing rate constant."><meta name="robots" content="noindex"><!-- mathjax <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/HS.solution.html b/docs/dev/reference/HS.solution.html index 5fcef9c0..241d8d69 100644 --- a/docs/dev/reference/HS.solution.html +++ b/docs/dev/reference/HS.solution.html @@ -18,7 +18,7 @@ between them."><meta name="robots" content="noindex"><!-- mathjax --><script src </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -60,7 +60,10 @@ between them."><meta name="robots" content="noindex"><!-- mathjax --><script src <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/IORE.solution.html b/docs/dev/reference/IORE.solution.html index 90eccde9..991fb566 100644 --- a/docs/dev/reference/IORE.solution.html +++ b/docs/dev/reference/IORE.solution.html @@ -18,7 +18,7 @@ a concentration dependent rate constant."><meta name="robots" content="noindex"> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - 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<a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -98,44 +41,46 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Example datasets from the NAFTA SOP published 2015</h1> @@ -147,97 +92,107 @@ <p>Data taken from US EPA (2015), p. 19 and 23.</p> </div> - <pre class="usage"><span class='va'>NAFTA_SOP_Appendix_B</span> - <span class='va'>NAFTA_SOP_Appendix_D</span></pre> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">NAFTA_SOP_Appendix_B</span></span> +<span> <span class="va">NAFTA_SOP_Appendix_D</span></span></code></pre></div> + </div> + <div id="format"> + <h2>Format</h2> + <p>2 datasets with observations on the following variables.</p><dl><dt><code>name</code></dt> +<dd><p>a factor containing the name of the observed variable</p></dd> - <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2> + <dt><code>time</code></dt> +<dd><p>a numeric vector containing time points</p></dd> - <p>2 datasets with observations on the following variables.</p><dl> - <dt><code>name</code></dt><dd><p>a factor containing the name of the observed variable</p></dd> - <dt><code>time</code></dt><dd><p>a numeric vector containing time points</p></dd> - <dt><code>value</code></dt><dd><p>a numeric vector containing concentrations</p></dd> - -</dl> - - <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2> + <dt><code>value</code></dt> +<dd><p>a numeric vector containing concentrations</p></dd> + +</dl></div> + <div id="source"> + <h2>Source</h2> <p>NAFTA (2011) Guidance for evaluating and calculating degradation kinetics in environmental media. NAFTA Technical Working Group on Pesticides - <a href='https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation'>https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation</a> + <a href="https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation" class="external-link">https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation</a> accessed 2019-02-22</p> <p>US EPA (2015) Standard Operating Procedure for Using the NAFTA Guidance to Calculate Representative Half-life Values and Characterizing Pesticide Degradation - <a href='https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance'>https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance</a></p> - - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> <span class='va'>nafta_evaluation</span> <span class='op'><-</span> <span class='fu'><a href='nafta.html'>nafta</a></span><span class='op'>(</span><span class='va'>NAFTA_SOP_Appendix_D</span>, cores <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></div><div class='output co'>#> <span class='message'>The representative half-life of the IORE model is longer than the one corresponding</span></div><div class='output co'>#> <span class='message'>to the terminal degradation rate found with the DFOP model.</span></div><div class='output co'>#> <span class='message'>The representative half-life obtained from the DFOP model may be used</span></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>nafta_evaluation</span><span class='op'>)</span> -</div><div class='output co'>#> Sums of squares: -#> SFO IORE DFOP -#> 1378.6832 615.7730 517.8836 -#> -#> Critical sum of squares for checking the SFO model: -#> [1] 717.4598 -#> -#> Parameters: -#> $SFO -#> Estimate Pr(>t) Lower Upper -#> parent_0 83.7558 1.80e-14 77.18268 90.3288 -#> k_parent 0.0017 7.43e-05 0.00112 0.0026 -#> sigma 8.7518 1.22e-05 5.64278 11.8608 -#> -#> $IORE -#> Estimate Pr(>t) Lower Upper -#> parent_0 9.69e+01 NA 8.88e+01 1.05e+02 -#> k__iore_parent 8.40e-14 NA 1.79e-18 3.94e-09 -#> N_parent 6.68e+00 NA 4.19e+00 9.17e+00 -#> sigma 5.85e+00 NA 3.76e+00 7.94e+00 -#> -#> $DFOP -#> Estimate Pr(>t) Lower Upper -#> parent_0 9.76e+01 1.94e-13 9.02e+01 1.05e+02 -#> k1 4.24e-02 5.92e-03 2.03e-02 8.88e-02 -#> k2 8.24e-04 6.48e-03 3.89e-04 1.75e-03 -#> g 2.88e-01 2.47e-05 1.95e-01 4.03e-01 -#> sigma 5.36e+00 2.22e-05 3.43e+00 7.30e+00 -#> -#> -#> DTx values: -#> DT50 DT90 DT50_rep -#> SFO 407 1350 407 -#> IORE 541 5190000 1560000 -#> DFOP 429 2380 841 -#> -#> Representative half-life: -#> [1] 841.41</div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>nafta_evaluation</span><span class='op'>)</span> -</div><div class='img'><img src='NAFTA_SOP_2015-1.png' alt='' width='700' height='433' /></div></pre> + <a href="https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance" class="external-link">https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance</a></p> + </div> + + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span> <span class="va">nafta_evaluation</span> <span class="op"><-</span> <span class="fu"><a href="nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Appendix_D</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span> +<span class="r-msg co"><span class="r-pr">#></span> The representative half-life of the IORE model is longer than the one corresponding</span> +<span class="r-msg co"><span class="r-pr">#></span> to the terminal degradation rate found with the DFOP model.</span> +<span class="r-msg co"><span class="r-pr">#></span> The representative half-life obtained from the DFOP model may be used</span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">nafta_evaluation</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Sums of squares:</span> +<span class="r-out co"><span class="r-pr">#></span> SFO IORE DFOP </span> +<span class="r-out co"><span class="r-pr">#></span> 1378.6832 615.7730 517.8836 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Critical sum of squares for checking the SFO model:</span> +<span class="r-out co"><span class="r-pr">#></span> [1] 717.4598</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Parameters:</span> +<span class="r-out co"><span class="r-pr">#></span> $SFO</span> +<span class="r-out co"><span class="r-pr">#></span> Estimate Pr(>t) Lower Upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 83.7558 1.80e-14 77.18268 90.3288</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent 0.0017 7.43e-05 0.00112 0.0026</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 8.7518 1.22e-05 5.64278 11.8608</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> $IORE</span> +<span class="r-out co"><span class="r-pr">#></span> Estimate Pr(>t) Lower Upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 9.69e+01 NA 8.88e+01 1.05e+02</span> +<span class="r-out co"><span class="r-pr">#></span> k__iore_parent 8.40e-14 NA 1.79e-18 3.94e-09</span> +<span class="r-out co"><span class="r-pr">#></span> N_parent 6.68e+00 NA 4.19e+00 9.17e+00</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 5.85e+00 NA 3.76e+00 7.94e+00</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> $DFOP</span> +<span class="r-out co"><span class="r-pr">#></span> Estimate Pr(>t) Lower Upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 9.76e+01 1.94e-13 9.02e+01 1.05e+02</span> +<span class="r-out co"><span class="r-pr">#></span> k1 4.24e-02 5.92e-03 2.03e-02 8.88e-02</span> +<span class="r-out co"><span class="r-pr">#></span> k2 8.24e-04 6.48e-03 3.89e-04 1.75e-03</span> +<span class="r-out co"><span class="r-pr">#></span> g 2.88e-01 2.47e-05 1.95e-01 4.03e-01</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 5.36e+00 2.22e-05 3.43e+00 7.30e+00</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> DTx values:</span> +<span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50_rep</span> +<span class="r-out co"><span class="r-pr">#></span> SFO 407 1350 407</span> +<span class="r-out co"><span class="r-pr">#></span> IORE 541 5190000 1560000</span> +<span class="r-out co"><span class="r-pr">#></span> DFOP 429 2380 841</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Representative half-life:</span> +<span class="r-out co"><span class="r-pr">#></span> [1] 841.41</span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">nafta_evaluation</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="NAFTA_SOP_2015-1.png" alt="" width="700" height="433"></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" 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href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -98,44 +41,46 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Example datasets from Attachment 1 to the NAFTA SOP published 2015</h1> @@ -147,91 +92,96 @@ <p>Data taken from from Attachment 1 of the SOP.</p> </div> - <pre class="usage"><span class='va'>NAFTA_SOP_Attachment</span></pre> - - - <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">NAFTA_SOP_Attachment</span></span></code></pre></div> + </div> + <div id="format"> + <h2>Format</h2> <p>A list (NAFTA_SOP_Attachment) containing 16 datasets suitable - for the evaluation with <code><a href='nafta.html'>nafta</a></code></p> - <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2> - + for the evaluation with <code><a href="nafta.html">nafta</a></code></p> + </div> + <div id="source"> + <h2>Source</h2> <p>NAFTA (2011) Guidance for evaluating and calculating degradation kinetics in environmental media. NAFTA Technical Working Group on Pesticides - <a href='https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation'>https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation</a> + <a href="https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation" class="external-link">https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation</a> accessed 2019-02-22</p> <p>US EPA (2015) Standard Operating Procedure for Using the NAFTA Guidance to Calculate Representative Half-life Values and Characterizing Pesticide Degradation - <a href='https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance'>https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance</a></p> - - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> <span class='va'>nafta_att_p5a</span> <span class='op'><-</span> <span class='fu'><a href='nafta.html'>nafta</a></span><span class='op'>(</span><span class='va'>NAFTA_SOP_Attachment</span><span class='op'>[[</span><span class='st'>"p5a"</span><span class='op'>]</span><span class='op'>]</span>, cores <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></div><div class='output co'>#> <span class='message'>The half-life obtained from the IORE model may be used</span></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>nafta_att_p5a</span><span class='op'>)</span> -</div><div class='output co'>#> Sums of squares: -#> SFO IORE DFOP -#> 465.21753 56.27506 32.06401 -#> -#> Critical sum of squares for checking the SFO model: -#> [1] 64.4304 -#> -#> Parameters: -#> $SFO -#> Estimate Pr(>t) Lower Upper -#> parent_0 95.8401 4.67e-21 92.245 99.4357 -#> k_parent 0.0102 3.92e-12 0.009 0.0117 -#> sigma 4.8230 3.81e-06 3.214 6.4318 -#> -#> $IORE -#> Estimate Pr(>t) Lower Upper -#> parent_0 1.01e+02 NA 9.91e+01 1.02e+02 -#> k__iore_parent 1.54e-05 NA 4.08e-06 5.84e-05 -#> N_parent 2.57e+00 NA 2.25e+00 2.89e+00 -#> sigma 1.68e+00 NA 1.12e+00 2.24e+00 -#> -#> $DFOP -#> Estimate Pr(>t) Lower Upper -#> parent_0 9.99e+01 1.41e-26 98.8116 101.0810 -#> k1 2.67e-02 5.05e-06 0.0243 0.0295 -#> k2 2.26e-12 5.00e-01 0.0000 Inf -#> g 6.47e-01 3.67e-06 0.6248 0.6677 -#> sigma 1.27e+00 8.91e-06 0.8395 1.6929 -#> -#> -#> DTx values: -#> DT50 DT90 DT50_rep -#> SFO 67.7 2.25e+02 6.77e+01 -#> IORE 58.2 1.07e+03 3.22e+02 -#> DFOP 55.5 5.59e+11 3.07e+11 -#> -#> Representative half-life: -#> [1] 321.51</div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>nafta_att_p5a</span><span class='op'>)</span> -</div><div class='img'><img src='NAFTA_SOP_Attachment-1.png' alt='' width='700' height='433' /></div></pre> + <a href="https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance" class="external-link">https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance</a></p> + </div> + + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span> <span class="va">nafta_att_p5a</span> <span class="op"><-</span> <span class="fu"><a href="nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p5a"</span><span class="op">]</span><span class="op">]</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span> +<span class="r-msg co"><span class="r-pr">#></span> The half-life obtained from the IORE model may be used</span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">nafta_att_p5a</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Sums of squares:</span> +<span class="r-out co"><span class="r-pr">#></span> SFO IORE DFOP </span> +<span class="r-out co"><span class="r-pr">#></span> 465.21753 56.27506 32.06401 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Critical sum of squares for checking the SFO model:</span> +<span class="r-out co"><span class="r-pr">#></span> [1] 64.4304</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Parameters:</span> +<span class="r-out co"><span class="r-pr">#></span> $SFO</span> +<span class="r-out co"><span class="r-pr">#></span> Estimate Pr(>t) Lower Upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 95.8401 4.67e-21 92.245 99.4357</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent 0.0102 3.92e-12 0.009 0.0117</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 4.8230 3.81e-06 3.214 6.4318</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> $IORE</span> +<span class="r-out co"><span class="r-pr">#></span> Estimate Pr(>t) Lower Upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 1.01e+02 NA 9.91e+01 1.02e+02</span> +<span class="r-out co"><span class="r-pr">#></span> k__iore_parent 1.54e-05 NA 4.08e-06 5.84e-05</span> +<span class="r-out co"><span class="r-pr">#></span> N_parent 2.57e+00 NA 2.25e+00 2.89e+00</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 1.68e+00 NA 1.12e+00 2.24e+00</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> $DFOP</span> +<span class="r-out co"><span class="r-pr">#></span> Estimate Pr(>t) Lower Upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 9.99e+01 1.41e-26 98.8116 101.0810</span> +<span class="r-out co"><span class="r-pr">#></span> k1 2.67e-02 5.05e-06 0.0243 0.0295</span> +<span class="r-out co"><span class="r-pr">#></span> k2 2.26e-12 5.00e-01 0.0000 Inf</span> +<span class="r-out co"><span class="r-pr">#></span> g 6.47e-01 3.67e-06 0.6248 0.6677</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 1.27e+00 8.91e-06 0.8395 1.6929</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> DTx values:</span> +<span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50_rep</span> +<span class="r-out co"><span class="r-pr">#></span> SFO 67.7 2.25e+02 6.77e+01</span> +<span class="r-out co"><span class="r-pr">#></span> IORE 58.2 1.07e+03 3.22e+02</span> +<span class="r-out co"><span class="r-pr">#></span> DFOP 55.5 5.59e+11 3.07e+11</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Representative half-life:</span> +<span class="r-out co"><span class="r-pr">#></span> [1] 321.51</span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">nafta_att_p5a</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="NAFTA_SOP_Attachment-1.png" alt="" width="700" height="433"></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/Rplot001.png b/docs/dev/reference/Rplot001.png Binary files differindex b3448db0..17a35806 100644 --- a/docs/dev/reference/Rplot001.png +++ b/docs/dev/reference/Rplot001.png diff --git a/docs/dev/reference/Rplot005.png b/docs/dev/reference/Rplot005.png Binary files differindex cb419daa..76f25647 100644 --- a/docs/dev/reference/Rplot005.png +++ b/docs/dev/reference/Rplot005.png diff --git a/docs/dev/reference/SFO.solution.html b/docs/dev/reference/SFO.solution.html index 970a62c5..a0bb999c 100644 --- a/docs/dev/reference/SFO.solution.html +++ b/docs/dev/reference/SFO.solution.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -59,7 +59,10 @@ <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/SFORB.solution.html b/docs/dev/reference/SFORB.solution.html index e3e43557..c14d3a32 100644 --- a/docs/dev/reference/SFORB.solution.html +++ b/docs/dev/reference/SFORB.solution.html @@ -21,7 +21,7 @@ and no substance in the bound fraction."><meta name="robots" content="noindex">< </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -63,7 +63,10 @@ and no substance in the bound fraction."><meta name="robots" content="noindex">< <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/add_err-1.png b/docs/dev/reference/add_err-1.png Binary files differindex 9ba106db..4a3b4062 100644 --- a/docs/dev/reference/add_err-1.png +++ b/docs/dev/reference/add_err-1.png diff --git 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The variance of the error -may depend on the predicted value and is specified as a standard deviation." /> - - -<meta name="robots" content="noindex"> - -<!-- mathjax --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script> - -<!--[if lt IE 9]> +may depend on the predicted value and is specified as a standard deviation."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> - +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - - </head> - - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -74,23 +19,21 @@ may depend on the predicted value and is specified as a standard deviation." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -100,195 +43,203 @@ may depend on the predicted value and is specified as a standard deviation." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Add normally distributed errors to simulated kinetic degradation data</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/add_err.R'><code>R/add_err.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/add_err.R" class="external-link"><code>R/add_err.R</code></a></small> <div class="hidden name"><code>add_err.Rd</code></div> </div> <div class="ref-description"> <p>Normally distributed errors are added to data predicted for a specific -degradation model using <code><a href='mkinpredict.html'>mkinpredict</a></code>. The variance of the error +degradation model using <code><a href="mkinpredict.html">mkinpredict</a></code>. The variance of the error may depend on the predicted value and is specified as a standard deviation.</p> </div> - <pre class="usage"><span class='fu'>add_err</span><span class='op'>(</span> - <span class='va'>prediction</span>, - <span class='va'>sdfunc</span>, - secondary <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"M1"</span>, <span class='st'>"M2"</span><span class='op'>)</span>, - n <span class='op'>=</span> <span class='fl'>10</span>, - LOD <span class='op'>=</span> <span class='fl'>0.1</span>, - reps <span class='op'>=</span> <span class='fl'>2</span>, - digits <span class='op'>=</span> <span class='fl'>1</span>, - seed <span class='op'>=</span> <span class='cn'>NA</span> -<span class='op'>)</span></pre> - - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>prediction</th> - <td><p>A prediction from a kinetic model as produced by -<code><a href='mkinpredict.html'>mkinpredict</a></code>.</p></td> - </tr> - <tr> - <th>sdfunc</th> - <td><p>A function taking the predicted value as its only argument and + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">add_err</span><span class="op">(</span></span> +<span> <span class="va">prediction</span>,</span> +<span> <span class="va">sdfunc</span>,</span> +<span> secondary <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span> +<span> n <span class="op">=</span> <span class="fl">10</span>,</span> +<span> LOD <span class="op">=</span> <span class="fl">0.1</span>,</span> +<span> reps <span class="op">=</span> <span class="fl">2</span>,</span> +<span> digits <span class="op">=</span> <span class="fl">1</span>,</span> +<span> seed <span class="op">=</span> <span class="cn">NA</span></span> +<span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>prediction</dt> +<dd><p>A prediction from a kinetic model as produced by +<code><a href="mkinpredict.html">mkinpredict</a></code>.</p></dd> + + +<dt>sdfunc</dt> +<dd><p>A function taking the predicted value as its only argument and returning a standard deviation that should be used for generating the -random error terms for this value.</p></td> - </tr> - <tr> - <th>secondary</th> - <td><p>The names of state variables that should have an initial -value of zero</p></td> - </tr> - <tr> - <th>n</th> - <td><p>The number of datasets to be generated.</p></td> - </tr> - <tr> - <th>LOD</th> - <td><p>The limit of detection (LOD). Values that are below the LOD after -adding the random error will be set to NA.</p></td> - </tr> - <tr> - <th>reps</th> - <td><p>The number of replicates to be generated within the datasets.</p></td> - </tr> - <tr> - <th>digits</th> - <td><p>The number of digits to which the values will be rounded.</p></td> - </tr> - <tr> - <th>seed</th> - <td><p>The seed used for the generation of random numbers. If NA, the -seed is not set.</p></td> - </tr> - </table> - - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> - - <p>A list of datasets compatible with <code><a href='mmkin.html'>mmkin</a></code>, i.e. the -components of the list are datasets compatible with <code><a href='mkinfit.html'>mkinfit</a></code>.</p> - <h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2> +random error terms for this value.</p></dd> + + +<dt>secondary</dt> +<dd><p>The names of state variables that should have an initial +value of zero</p></dd> + + +<dt>n</dt> +<dd><p>The number of datasets to be generated.</p></dd> + + +<dt>LOD</dt> +<dd><p>The limit of detection (LOD). Values that are below the LOD after +adding the random error will be set to NA.</p></dd> + +<dt>reps</dt> +<dd><p>The number of replicates to be generated within the datasets.</p></dd> + + +<dt>digits</dt> +<dd><p>The number of digits to which the values will be rounded.</p></dd> + + +<dt>seed</dt> +<dd><p>The seed used for the generation of random numbers. If NA, the +seed is not set.</p></dd> + +</dl></div> + <div id="value"> + <h2>Value</h2> + + +<p>A list of datasets compatible with <code><a href="mmkin.html">mmkin</a></code>, i.e. the +components of the list are datasets compatible with <code><a href="mkinfit.html">mkinfit</a></code>.</p> + </div> + <div id="references"> + <h2>References</h2> <p>Ranke J and Lehmann R (2015) To t-test or not to t-test, that is the question. XV Symposium on Pesticide Chemistry 2-4 September 2015, Piacenza, Italy https://jrwb.de/posters/piacenza_2015.pdf</p> - <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2> - + </div> + <div id="author"> + <h2>Author</h2> <p>Johannes Ranke</p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> -<span class='co'># The kinetic model</span> -<span class='va'>m_SFO_SFO</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"M1"</span><span class='op'>)</span>, - M1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span>, use_of_ff <span class='op'>=</span> <span class='st'>"max"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'> -<span class='co'># Generate a prediction for a specific set of parameters</span> -<span class='va'>sampling_times</span> <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>3</span>, <span class='fl'>7</span>, <span class='fl'>14</span>, <span class='fl'>28</span>, <span class='fl'>60</span>, <span class='fl'>90</span>, <span class='fl'>120</span><span class='op'>)</span> - -<span class='co'># This is the prediction used for the "Type 2 datasets" on the Piacenza poster</span> -<span class='co'># from 2015</span> -<span class='va'>d_SFO_SFO</span> <span class='op'><-</span> <span class='fu'><a href='mkinpredict.html'>mkinpredict</a></span><span class='op'>(</span><span class='va'>m_SFO_SFO</span>, - <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>k_parent <span class='op'>=</span> <span class='fl'>0.1</span>, f_parent_to_M1 <span class='op'>=</span> <span class='fl'>0.5</span>, - k_M1 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span><span class='op'>/</span><span class='fl'>1000</span><span class='op'>)</span>, - <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fl'>100</span>, M1 <span class='op'>=</span> <span class='fl'>0</span><span class='op'>)</span>, - <span class='va'>sampling_times</span><span class='op'>)</span> - -<span class='co'># Add an error term with a constant (independent of the value) standard deviation</span> -<span class='co'># of 10, and generate three datasets</span> -<span class='va'>d_SFO_SFO_err</span> <span class='op'><-</span> <span class='fu'>add_err</span><span class='op'>(</span><span class='va'>d_SFO_SFO</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span> <span class='fl'>10</span>, n <span class='op'>=</span> <span class='fl'>3</span>, seed <span class='op'>=</span> <span class='fl'>123456789</span> <span class='op'>)</span> - -<span class='co'># Name the datasets for nicer plotting</span> -<span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span><span class='op'>(</span><span class='va'>d_SFO_SFO_err</span><span class='op'>)</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span><span class='op'>(</span><span class='st'>"Dataset"</span>, <span class='fl'>1</span><span class='op'>:</span><span class='fl'>3</span><span class='op'>)</span> - -<span class='co'># Name the model in the list of models (with only one member in this case) for</span> -<span class='co'># nicer plotting later on. Be quiet and use only one core not to offend CRAN</span> -<span class='co'># checks</span> -<span class='co'># \dontrun{</span> -<span class='va'>f_SFO_SFO</span> <span class='op'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='st'>"SFO-SFO"</span> <span class='op'>=</span> <span class='va'>m_SFO_SFO</span><span class='op'>)</span>, - <span class='va'>d_SFO_SFO_err</span>, cores <span class='op'>=</span> <span class='fl'>1</span>, - quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> - -<span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_SFO_SFO</span><span class='op'>)</span> -</div><div class='img'><img src='add_err-1.png' alt='' width='700' height='433' /></div><div class='input'> -<span class='co'># We would like to inspect the fit for dataset 3 more closely</span> -<span class='co'># Using double brackets makes the returned object an mkinfit object</span> -<span class='co'># instead of a list of mkinfit objects, so plot.mkinfit is used</span> -<span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_SFO_SFO</span><span class='op'>[[</span><span class='fl'>3</span><span class='op'>]</span><span class='op'>]</span>, show_residuals <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -</div><div class='img'><img src='add_err-2.png' alt='' width='700' height='433' /></div><div class='input'> -<span class='co'># If we use single brackets, we should give two indices (model and dataset),</span> -<span class='co'># and plot.mmkin is used</span> -<span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_SFO_SFO</span><span class='op'>[</span><span class='fl'>1</span>, <span class='fl'>3</span><span class='op'>]</span><span class='op'>)</span> -</div><div class='img'><img src='add_err-3.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># }</span> - -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># The kinetic model</span></span></span> +<span class="r-in"><span><span class="va">m_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Generate a prediction for a specific set of parameters</span></span></span> +<span class="r-in"><span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># This is the prediction used for the "Type 2 datasets" on the Piacenza poster</span></span></span> +<span class="r-in"><span><span class="co"># from 2015</span></span></span> +<span class="r-in"><span><span class="va">d_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO_SFO</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.1</span>, f_parent_to_M1 <span class="op">=</span> <span class="fl">0.5</span>,</span></span> +<span class="r-in"><span> k_M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">/</span><span class="fl">1000</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="va">sampling_times</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Add an error term with a constant (independent of the value) standard deviation</span></span></span> +<span class="r-in"><span><span class="co"># of 10, and generate three datasets</span></span></span> +<span class="r-in"><span><span class="va">d_SFO_SFO_err</span> <span class="op"><-</span> <span class="fu">add_err</span><span class="op">(</span><span class="va">d_SFO_SFO</span>, <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fl">10</span>, n <span class="op">=</span> <span class="fl">3</span>, seed <span class="op">=</span> <span class="fl">123456789</span> <span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Name the datasets for nicer plotting</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">d_SFO_SFO_err</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Name the model in the list of models (with only one member in this case) for</span></span></span> +<span class="r-in"><span><span class="co"># nicer plotting later on. Be quiet and use only one core not to offend CRAN</span></span></span> +<span class="r-in"><span><span class="co"># checks</span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="va">f_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"SFO-SFO"</span> <span class="op">=</span> <span class="va">m_SFO_SFO</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="va">d_SFO_SFO_err</span>, cores <span class="op">=</span> <span class="fl">1</span>,</span></span> +<span class="r-in"><span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_SFO_SFO</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="add_err-1.png" alt="" width="700" height="433"></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># We would like to inspect the fit for dataset 3 more closely</span></span></span> +<span class="r-in"><span><span class="co"># Using double brackets makes the returned object an mkinfit object</span></span></span> +<span class="r-in"><span><span class="co"># instead of a list of mkinfit objects, so plot.mkinfit is used</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_SFO_SFO</span><span class="op">[[</span><span class="fl">3</span><span class="op">]</span><span class="op">]</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="add_err-2.png" alt="" width="700" height="433"></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># If we use single brackets, we should give two indices (model and dataset),</span></span></span> +<span class="r-in"><span><span class="co"># and plot.mmkin is used</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_SFO_SFO</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">]</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="add_err-3.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/anova.saem.mmkin.html b/docs/dev/reference/anova.saem.mmkin.html index 2c109cc2..1689e436 100644 --- a/docs/dev/reference/anova.saem.mmkin.html +++ b/docs/dev/reference/anova.saem.mmkin.html @@ -20,7 +20,7 @@ the model on the previous line."><meta name="robots" content="noindex"><!-- math </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -62,7 +62,10 @@ the model on the previous line."><meta name="robots" content="noindex"><!-- math <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/aw.html b/docs/dev/reference/aw.html index e552cc62..81b9b2e9 100644 --- a/docs/dev/reference/aw.html +++ b/docs/dev/reference/aw.html @@ -19,7 +19,7 @@ by Burnham and Anderson (2004)."><meta name="robots" content="noindex"><!-- math </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -61,7 +61,10 @@ by Burnham and Anderson (2004)."><meta name="robots" content="noindex"><!-- math <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/confint.mkinfit.html b/docs/dev/reference/confint.mkinfit.html index 2237a539..16bd388b 100644 --- a/docs/dev/reference/confint.mkinfit.html +++ b/docs/dev/reference/confint.mkinfit.html @@ -1,74 +1,19 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - <head> - <meta charset="utf-8"> -<meta http-equiv="X-UA-Compatible" content="IE=edge"> -<meta name="viewport" content="width=device-width, initial-scale=1.0"> - -<title>Confidence intervals for parameters of mkinfit objects — confint.mkinfit • mkin</title> - - -<!-- jquery --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script> -<!-- Bootstrap --> - -<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous" /> - -<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script> - -<!-- bootstrap-toc --> -<link rel="stylesheet" href="../bootstrap-toc.css"> -<script src="../bootstrap-toc.js"></script> - 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The alternative method 'profile' is based on the profile likelihood for each parameter. The 'profile' method uses two nested optimisations and can take a very long time, even if parallelized by specifying 'cores' on unixoid platforms. The speed of the method could likely be improved by using the -method of Venzon and Moolgavkar (1988)." /> - - -<meta name="robots" content="noindex"> - -<!-- mathjax --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script> - -<!--[if lt IE 9]> +method of Venzon and Moolgavkar (1988)."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> - - - - </head> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -79,23 +24,21 @@ method of Venzon and Moolgavkar (1988)." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -105,48 +48,50 @@ method of Venzon and Moolgavkar (1988)." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Confidence intervals for parameters of mkinfit objects</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/confint.mkinfit.R'><code>R/confint.mkinfit.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/confint.mkinfit.R" class="external-link"><code>R/confint.mkinfit.R</code></a></small> <div class="hidden name"><code>confint.mkinfit.Rd</code></div> </div> @@ -161,279 +106,301 @@ platforms. The speed of the method could likely be improved by using the method of Venzon and Moolgavkar (1988).</p> </div> - <pre class="usage"><span class='co'># S3 method for mkinfit</span> -<span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span><span class='op'>(</span> - <span class='va'>object</span>, - <span class='va'>parm</span>, - level <span class='op'>=</span> <span class='fl'>0.95</span>, - alpha <span class='op'>=</span> <span class='fl'>1</span> <span class='op'>-</span> <span class='va'>level</span>, - <span class='va'>cutoff</span>, - method <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"quadratic"</span>, <span class='st'>"profile"</span><span class='op'>)</span>, - transformed <span class='op'>=</span> <span class='cn'>TRUE</span>, - backtransform <span class='op'>=</span> <span class='cn'>TRUE</span>, - cores <span class='op'>=</span> <span class='fu'>parallel</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/r/parallel/detectCores.html'>detectCores</a></span><span class='op'>(</span><span class='op'>)</span>, - rel_tol <span class='op'>=</span> <span class='fl'>0.01</span>, - quiet <span class='op'>=</span> <span class='cn'>FALSE</span>, - <span class='va'>...</span> -<span class='op'>)</span></pre> - - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>object</th> - <td><p>An <code><a href='mkinfit.html'>mkinfit</a></code> object</p></td> - </tr> - <tr> - <th>parm</th> - <td><p>A vector of names of the parameters which are to be given -confidence intervals. If missing, all parameters are considered.</p></td> - </tr> - <tr> - <th>level</th> - <td><p>The confidence level required</p></td> - </tr> - <tr> - <th>alpha</th> - <td><p>The allowed error probability, overrides 'level' if specified.</p></td> - </tr> - <tr> - <th>cutoff</th> - <td><p>Possibility to specify an alternative cutoff for the difference + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span></span> +<span> <span class="va">object</span>,</span> +<span> <span class="va">parm</span>,</span> +<span> level <span class="op">=</span> <span class="fl">0.95</span>,</span> +<span> alpha <span class="op">=</span> <span class="fl">1</span> <span class="op">-</span> <span class="va">level</span>,</span> +<span> <span class="va">cutoff</span>,</span> +<span> method <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"quadratic"</span>, <span class="st">"profile"</span><span class="op">)</span>,</span> +<span> transformed <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> backtransform <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> cores <span class="op">=</span> <span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span>,</span> +<span> rel_tol <span class="op">=</span> <span class="fl">0.01</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>object</dt> +<dd><p>An <code><a href="mkinfit.html">mkinfit</a></code> object</p></dd> + + +<dt>parm</dt> +<dd><p>A vector of names of the parameters which are to be given +confidence intervals. If missing, all parameters are considered.</p></dd> + + +<dt>level</dt> +<dd><p>The confidence level required</p></dd> + + +<dt>alpha</dt> +<dd><p>The allowed error probability, overrides 'level' if specified.</p></dd> + + +<dt>cutoff</dt> +<dd><p>Possibility to specify an alternative cutoff for the difference in the log-likelihoods at the confidence boundary. Specifying an explicit -cutoff value overrides arguments 'level' and 'alpha'</p></td> - </tr> - <tr> - <th>method</th> - <td><p>The 'quadratic' method approximates the likelihood function at +cutoff value overrides arguments 'level' and 'alpha'</p></dd> + + +<dt>method</dt> +<dd><p>The 'quadratic' method approximates the likelihood function at the optimised parameters using the second term of the Taylor expansion, using a second derivative (hessian) contained in the object. The 'profile' method searches the parameter space for the -cutoff of the confidence intervals by means of a likelihood ratio test.</p></td> - </tr> - <tr> - <th>transformed</th> - <td><p>If the quadratic approximation is used, should it be -applied to the likelihood based on the transformed parameters?</p></td> - </tr> - <tr> - <th>backtransform</th> - <td><p>If we approximate the likelihood in terms of the +cutoff of the confidence intervals by means of a likelihood ratio test.</p></dd> + + +<dt>transformed</dt> +<dd><p>If the quadratic approximation is used, should it be +applied to the likelihood based on the transformed parameters?</p></dd> + + +<dt>backtransform</dt> +<dd><p>If we approximate the likelihood in terms of the transformed parameters, should we backtransform the parameters with -their confidence intervals?</p></td> - </tr> - <tr> - <th>cores</th> - <td><p>The number of cores to be used for multicore processing. -On Windows machines, cores > 1 is currently not supported.</p></td> - </tr> - <tr> - <th>rel_tol</th> - <td><p>If the method is 'profile', what should be the accuracy +their confidence intervals?</p></dd> + + +<dt>cores</dt> +<dd><p>The number of cores to be used for multicore processing. +On Windows machines, cores > 1 is currently not supported.</p></dd> + + +<dt>rel_tol</dt> +<dd><p>If the method is 'profile', what should be the accuracy of the lower and upper bounds, relative to the estimate obtained from -the quadratic method?</p></td> - </tr> - <tr> - <th>quiet</th> - <td><p>Should we suppress the message "Profiling the likelihood"</p></td> - </tr> - <tr> - <th>...</th> - <td><p>Not used</p></td> - </tr> - </table> - - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> - - <p>A matrix with columns giving lower and upper confidence limits for -each parameter.</p> - <h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2> +the quadratic method?</p></dd> + + +<dt>quiet</dt> +<dd><p>Should we suppress the message "Profiling the likelihood"</p></dd> + +<dt>...</dt> +<dd><p>Not used</p></dd> + +</dl></div> + <div id="value"> + <h2>Value</h2> + + +<p>A matrix with columns giving lower and upper confidence limits for +each parameter.</p> + </div> + <div id="references"> + <h2>References</h2> <p>Bates DM and Watts GW (1988) Nonlinear regression analysis & its applications</p> <p>Pawitan Y (2013) In all likelihood - Statistical modelling and inference using likelihood. Clarendon Press, Oxford.</p> <p>Venzon DJ and Moolgavkar SH (1988) A Method for Computing Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, 87–94.</p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'><span class='va'>f</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='va'>FOCUS_2006_C</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span><span class='op'>(</span><span class='va'>f</span>, method <span class='op'>=</span> <span class='st'>"quadratic"</span><span class='op'>)</span> -</div><div class='output co'>#> 2.5% 97.5% -#> parent_0 71.8242430 93.1600766 -#> k_parent 0.2109541 0.4440528 -#> sigma 1.9778868 7.3681380</div><div class='input'> -<span class='co'># \dontrun{</span> -<span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span><span class='op'>(</span><span class='va'>f</span>, method <span class='op'>=</span> <span class='st'>"profile"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Profiling the likelihood</span></div><div class='output co'>#> 2.5% 97.5% -#> parent_0 73.0641834 92.1392181 -#> k_parent 0.2170293 0.4235348 -#> sigma 3.1307772 8.0628314</div><div class='input'> -<span class='co'># Set the number of cores for the profiling method for further examples</span> -<span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/identical.html'>identical</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/Sys.getenv.html'>Sys.getenv</a></span><span class='op'>(</span><span class='st'>"NOT_CRAN"</span><span class='op'>)</span>, <span class='st'>"true"</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span> - <span class='va'>n_cores</span> <span class='op'><-</span> <span class='fu'>parallel</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/r/parallel/detectCores.html'>detectCores</a></span><span class='op'>(</span><span class='op'>)</span> <span class='op'>-</span> <span class='fl'>1</span> -<span class='op'>}</span> <span class='kw'>else</span> <span class='op'>{</span> - <span class='va'>n_cores</span> <span class='op'><-</span> <span class='fl'>1</span> -<span class='op'>}</span> -<span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/Sys.getenv.html'>Sys.getenv</a></span><span class='op'>(</span><span class='st'>"TRAVIS"</span><span class='op'>)</span> <span class='op'>!=</span> <span class='st'>""</span><span class='op'>)</span> <span class='va'>n_cores</span> <span class='op'>=</span> <span class='fl'>1</span> -<span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/Sys.info.html'>Sys.info</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>[</span><span class='st'>"sysname"</span><span class='op'>]</span> <span class='op'>==</span> <span class='st'>"Windows"</span><span class='op'>)</span> <span class='va'>n_cores</span> <span class='op'>=</span> <span class='fl'>1</span> - -<span class='va'>SFO_SFO</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"m1"</span><span class='op'>)</span>, m1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span>, - use_of_ff <span class='op'>=</span> <span class='st'>"min"</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='va'>SFO_SFO.ff</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"m1"</span><span class='op'>)</span>, m1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span>, - use_of_ff <span class='op'>=</span> <span class='st'>"max"</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='va'>f_d_1</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>SFO_SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span><span class='op'>(</span><span class='va'>FOCUS_2006_D</span>, <span class='va'>value</span> <span class='op'>!=</span> <span class='fl'>0</span><span class='op'>)</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span><span class='op'>(</span><span class='va'>ci_profile</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span><span class='op'>(</span><span class='va'>f_d_1</span>, method <span class='op'>=</span> <span class='st'>"profile"</span>, cores <span class='op'>=</span> <span class='fl'>1</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> user system elapsed -#> 4.295 1.008 3.959 </div><div class='input'><span class='co'># Using more cores does not save much time here, as parent_0 takes up most of the time</span> -<span class='co'># If we additionally exclude parent_0 (the confidence of which is often of</span> -<span class='co'># minor interest), we get a nice performance improvement if we use at least 4 cores</span> -<span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span><span class='op'>(</span><span class='va'>ci_profile_no_parent_0</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span><span class='op'>(</span><span class='va'>f_d_1</span>, method <span class='op'>=</span> <span class='st'>"profile"</span>, - <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"k_parent_sink"</span>, <span class='st'>"k_parent_m1"</span>, <span class='st'>"k_m1_sink"</span>, <span class='st'>"sigma"</span><span class='op'>)</span>, cores <span class='op'>=</span> <span class='va'>n_cores</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Profiling the likelihood</span></div><div class='output co'>#> user system elapsed -#> 1.451 0.126 0.923 </div><div class='input'><span class='va'>ci_profile</span> -</div><div class='output co'>#> 2.5% 97.5% -#> parent_0 96.456003640 1.027703e+02 -#> k_parent_sink 0.040762501 5.549764e-02 -#> k_parent_m1 0.046786482 5.500879e-02 -#> k_m1_sink 0.003892605 6.702778e-03 -#> sigma 2.535612399 3.985263e+00</div><div class='input'><span class='va'>ci_quadratic_transformed</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span><span class='op'>(</span><span class='va'>f_d_1</span>, method <span class='op'>=</span> <span class='st'>"quadratic"</span><span class='op'>)</span> -<span class='va'>ci_quadratic_transformed</span> -</div><div class='output co'>#> 2.5% 97.5% -#> parent_0 96.403841640 1.027931e+02 -#> k_parent_sink 0.041033378 5.596269e-02 -#> k_parent_m1 0.046777902 5.511931e-02 -#> k_m1_sink 0.004012217 6.897547e-03 -#> sigma 2.396089689 3.854918e+00</div><div class='input'><span class='va'>ci_quadratic_untransformed</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span><span class='op'>(</span><span class='va'>f_d_1</span>, method <span class='op'>=</span> <span class='st'>"quadratic"</span>, transformed <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span> -<span class='va'>ci_quadratic_untransformed</span> -</div><div class='output co'>#> 2.5% 97.5% -#> parent_0 96.403841645 102.79312449 -#> k_parent_sink 0.040485331 0.05535491 -#> k_parent_m1 0.046611582 0.05494364 -#> k_m1_sink 0.003835483 0.00668582 -#> sigma 2.396089689 3.85491806</div><div class='input'><span class='co'># Against the expectation based on Bates and Watts (1988), the confidence</span> -<span class='co'># intervals based on the internal parameter transformation are less</span> -<span class='co'># congruent with the likelihood based intervals. Note the superiority of the</span> -<span class='co'># interval based on the untransformed fit for k_m1_sink</span> -<span class='va'>rel_diffs_transformed</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/MathFun.html'>abs</a></span><span class='op'>(</span><span class='op'>(</span><span class='va'>ci_quadratic_transformed</span> <span class='op'>-</span> <span class='va'>ci_profile</span><span class='op'>)</span><span class='op'>/</span><span class='va'>ci_profile</span><span class='op'>)</span> -<span class='va'>rel_diffs_untransformed</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/MathFun.html'>abs</a></span><span class='op'>(</span><span class='op'>(</span><span class='va'>ci_quadratic_untransformed</span> <span class='op'>-</span> <span class='va'>ci_profile</span><span class='op'>)</span><span class='op'>/</span><span class='va'>ci_profile</span><span class='op'>)</span> -<span class='va'>rel_diffs_transformed</span> <span class='op'><</span> <span class='va'>rel_diffs_untransformed</span> -</div><div class='output co'>#> 2.5% 97.5% -#> parent_0 FALSE FALSE -#> k_parent_sink TRUE FALSE -#> k_parent_m1 TRUE FALSE -#> k_m1_sink FALSE FALSE -#> sigma FALSE FALSE</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/Round.html'>signif</a></span><span class='op'>(</span><span class='va'>rel_diffs_transformed</span>, <span class='fl'>3</span><span class='op'>)</span> -</div><div class='output co'>#> 2.5% 97.5% -#> parent_0 0.000541 0.000222 -#> k_parent_sink 0.006650 0.008380 -#> k_parent_m1 0.000183 0.002010 -#> k_m1_sink 0.030700 0.029100 -#> sigma 0.055000 0.032700</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/Round.html'>signif</a></span><span class='op'>(</span><span class='va'>rel_diffs_untransformed</span>, <span class='fl'>3</span><span class='op'>)</span> -</div><div class='output co'>#> 2.5% 97.5% -#> parent_0 0.000541 0.000222 -#> k_parent_sink 0.006800 0.002570 -#> k_parent_m1 0.003740 0.001180 -#> k_m1_sink 0.014700 0.002530 -#> sigma 0.055000 0.032700</div><div class='input'> - -<span class='co'># Investigate a case with formation fractions</span> -<span class='va'>f_d_2</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>SFO_SFO.ff</span>, <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span><span class='op'>(</span><span class='va'>FOCUS_2006_D</span>, <span class='va'>value</span> <span class='op'>!=</span> <span class='fl'>0</span><span class='op'>)</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='va'>ci_profile_ff</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span><span class='op'>(</span><span class='va'>f_d_2</span>, method <span class='op'>=</span> <span class='st'>"profile"</span>, cores <span class='op'>=</span> <span class='va'>n_cores</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Profiling the likelihood</span></div><div class='input'><span class='va'>ci_profile_ff</span> -</div><div class='output co'>#> 2.5% 97.5% -#> parent_0 96.456003640 1.027703e+02 -#> k_parent 0.090911032 1.071578e-01 -#> k_m1 0.003892606 6.702775e-03 -#> f_parent_to_m1 0.471328495 5.611550e-01 -#> sigma 2.535612399 3.985263e+00</div><div class='input'><span class='va'>ci_quadratic_transformed_ff</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span><span class='op'>(</span><span class='va'>f_d_2</span>, method <span class='op'>=</span> <span class='st'>"quadratic"</span><span class='op'>)</span> -<span class='va'>ci_quadratic_transformed_ff</span> -</div><div class='output co'>#> 2.5% 97.5% -#> parent_0 96.403833578 102.79311649 -#> k_parent 0.090823771 0.10725430 -#> k_m1 0.004012219 0.00689755 -#> f_parent_to_m1 0.469118824 0.55959615 -#> sigma 2.396089689 3.85491806</div><div class='input'><span class='va'>ci_quadratic_untransformed_ff</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span><span class='op'>(</span><span class='va'>f_d_2</span>, method <span class='op'>=</span> <span class='st'>"quadratic"</span>, transformed <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span> -<span class='va'>ci_quadratic_untransformed_ff</span> -</div><div class='output co'>#> 2.5% 97.5% -#> parent_0 96.403833583 1.027931e+02 -#> k_parent 0.090491913 1.069035e-01 -#> k_m1 0.003835485 6.685823e-03 -#> f_parent_to_m1 0.469113477 5.598387e-01 -#> sigma 2.396089689 3.854918e+00</div><div class='input'><span class='va'>rel_diffs_transformed_ff</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/MathFun.html'>abs</a></span><span class='op'>(</span><span class='op'>(</span><span class='va'>ci_quadratic_transformed_ff</span> <span class='op'>-</span> <span class='va'>ci_profile_ff</span><span class='op'>)</span><span class='op'>/</span><span class='va'>ci_profile_ff</span><span class='op'>)</span> -<span class='va'>rel_diffs_untransformed_ff</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/MathFun.html'>abs</a></span><span class='op'>(</span><span class='op'>(</span><span class='va'>ci_quadratic_untransformed_ff</span> <span class='op'>-</span> <span class='va'>ci_profile_ff</span><span class='op'>)</span><span class='op'>/</span><span class='va'>ci_profile_ff</span><span class='op'>)</span> -<span class='co'># While the confidence interval for the parent rate constant is closer to</span> -<span class='co'># the profile based interval when using the internal parameter</span> -<span class='co'># transformation, the interval for the metabolite rate constant is 'better</span> -<span class='co'># without internal parameter transformation.</span> -<span class='va'>rel_diffs_transformed_ff</span> <span class='op'><</span> <span class='va'>rel_diffs_untransformed_ff</span> -</div><div class='output co'>#> 2.5% 97.5% -#> parent_0 FALSE FALSE -#> k_parent TRUE TRUE -#> k_m1 FALSE FALSE -#> f_parent_to_m1 TRUE FALSE -#> sigma TRUE FALSE</div><div class='input'><span class='va'>rel_diffs_transformed_ff</span> -</div><div class='output co'>#> 2.5% 97.5% -#> parent_0 0.0005408690 0.0002217233 -#> k_parent 0.0009598532 0.0009001864 -#> k_m1 0.0307283041 0.0290588361 -#> f_parent_to_m1 0.0046881769 0.0027780063 -#> sigma 0.0550252516 0.0327066836</div><div class='input'><span class='va'>rel_diffs_untransformed_ff</span> -</div><div class='output co'>#> 2.5% 97.5% -#> parent_0 0.0005408689 0.0002217232 -#> k_parent 0.0046102156 0.0023732281 -#> k_m1 0.0146740690 0.0025291820 -#> f_parent_to_m1 0.0046995211 0.0023457712 -#> sigma 0.0550252516 0.0327066836</div><div class='input'> -<span class='co'># The profiling for the following fit does not finish in a reasonable time,</span> -<span class='co'># therefore we use the quadratic approximation</span> -<span class='va'>m_synth_DFOP_par</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"DFOP"</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"M1"</span>, <span class='st'>"M2"</span><span class='op'>)</span><span class='op'>)</span>, - M1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span>, - M2 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span>, - use_of_ff <span class='op'>=</span> <span class='st'>"max"</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='va'>DFOP_par_c</span> <span class='op'><-</span> <span class='va'>synthetic_data_for_UBA_2014</span><span class='op'>[[</span><span class='fl'>12</span><span class='op'>]</span><span class='op'>]</span><span class='op'>$</span><span class='va'>data</span> -<span class='va'>f_tc_2</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>m_synth_DFOP_par</span>, <span class='va'>DFOP_par_c</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span>, - error_model_algorithm <span class='op'>=</span> <span class='st'>"direct"</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span><span class='op'>(</span><span class='va'>f_tc_2</span>, method <span class='op'>=</span> <span class='st'>"quadratic"</span><span class='op'>)</span> -</div><div class='output co'>#> 2.5% 97.5% -#> parent_0 94.596039609 106.19954892 -#> k_M1 0.037605368 0.04490762 -#> k_M2 0.008568731 0.01087676 -#> f_parent_to_M1 0.021462489 0.62023882 -#> f_parent_to_M2 0.015165617 0.37975348 -#> k1 0.273897348 0.33388101 -#> k2 0.018614554 0.02250378 -#> g 0.671943411 0.73583305 -#> sigma_low 0.251283495 0.83992077 -#> rsd_high 0.040411024 0.07662008</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span><span class='op'>(</span><span class='va'>f_tc_2</span>, <span class='st'>"parent_0"</span>, method <span class='op'>=</span> <span class='st'>"quadratic"</span><span class='op'>)</span> -</div><div class='output co'>#> 2.5% 97.5% -#> parent_0 94.59604 106.1995</div><div class='input'><span class='co'># }</span> -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f</span>, method <span class="op">=</span> <span class="st">"quadratic"</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 71.8242430 93.1600766</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent 0.2109541 0.4440528</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 1.9778868 7.3681380</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f</span>, method <span class="op">=</span> <span class="st">"profile"</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Profiling the likelihood</span> +<span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 73.0641834 92.1392181</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent 0.2170293 0.4235348</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 3.1307772 8.0628314</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Set the number of cores for the profiling method for further examples</span></span></span> +<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/identical.html" class="external-link">identical</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.getenv.html" class="external-link">Sys.getenv</a></span><span class="op">(</span><span class="st">"NOT_CRAN"</span><span class="op">)</span>, <span class="st">"true"</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span> +<span class="r-in"><span> <span class="va">n_cores</span> <span class="op"><-</span> <span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span> <span class="op">-</span> <span class="fl">1</span></span></span> +<span class="r-in"><span><span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span></span> +<span class="r-in"><span> <span class="va">n_cores</span> <span class="op"><-</span> <span class="fl">1</span></span></span> +<span class="r-in"><span><span class="op">}</span></span></span> +<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.getenv.html" class="external-link">Sys.getenv</a></span><span class="op">(</span><span class="st">"TRAVIS"</span><span class="op">)</span> <span class="op">!=</span> <span class="st">""</span><span class="op">)</span> <span class="va">n_cores</span> <span class="op">=</span> <span class="fl">1</span></span></span> +<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="va">n_cores</span> <span class="op">=</span> <span class="fl">1</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> use_of_ff <span class="op">=</span> <span class="st">"min"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO.ff</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> use_of_ff <span class="op">=</span> <span class="st">"max"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">f_d_1</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">ci_profile</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_1</span>, method <span class="op">=</span> <span class="st">"profile"</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> user system elapsed </span> +<span class="r-out co"><span class="r-pr">#></span> 3.811 0.004 3.815 </span> +<span class="r-in"><span><span class="co"># Using more cores does not save much time here, as parent_0 takes up most of the time</span></span></span> +<span class="r-in"><span><span class="co"># If we additionally exclude parent_0 (the confidence of which is often of</span></span></span> +<span class="r-in"><span><span class="co"># minor interest), we get a nice performance improvement if we use at least 4 cores</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">ci_profile_no_parent_0</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_1</span>, method <span class="op">=</span> <span class="st">"profile"</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"k_parent_sink"</span>, <span class="st">"k_parent_m1"</span>, <span class="st">"k_m1_sink"</span>, <span class="st">"sigma"</span><span class="op">)</span>, cores <span class="op">=</span> <span class="va">n_cores</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Profiling the likelihood</span> +<span class="r-out co"><span class="r-pr">#></span> user system elapsed </span> +<span class="r-out co"><span class="r-pr">#></span> 2.313 0.004 2.318 </span> +<span class="r-in"><span><span class="va">ci_profile</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 96.456003640 1.027703e+02</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent_sink 0.040762501 5.549764e-02</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent_m1 0.046786482 5.500879e-02</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1_sink 0.003892605 6.702778e-03</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 2.535612399 3.985263e+00</span> +<span class="r-in"><span><span class="va">ci_quadratic_transformed</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_1</span>, method <span class="op">=</span> <span class="st">"quadratic"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">ci_quadratic_transformed</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 96.403841640 1.027931e+02</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent_sink 0.041033378 5.596269e-02</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent_m1 0.046777902 5.511931e-02</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1_sink 0.004012217 6.897547e-03</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 2.396089689 3.854918e+00</span> +<span class="r-in"><span><span class="va">ci_quadratic_untransformed</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_1</span>, method <span class="op">=</span> <span class="st">"quadratic"</span>, transformed <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">ci_quadratic_untransformed</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 96.403841645 102.79312449</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent_sink 0.040485331 0.05535491</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent_m1 0.046611582 0.05494364</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1_sink 0.003835483 0.00668582</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 2.396089689 3.85491806</span> +<span class="r-in"><span><span class="co"># Against the expectation based on Bates and Watts (1988), the confidence</span></span></span> +<span class="r-in"><span><span class="co"># intervals based on the internal parameter transformation are less</span></span></span> +<span class="r-in"><span><span class="co"># congruent with the likelihood based intervals. Note the superiority of the</span></span></span> +<span class="r-in"><span><span class="co"># interval based on the untransformed fit for k_m1_sink</span></span></span> +<span class="r-in"><span><span class="va">rel_diffs_transformed</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">abs</a></span><span class="op">(</span><span class="op">(</span><span class="va">ci_quadratic_transformed</span> <span class="op">-</span> <span class="va">ci_profile</span><span class="op">)</span><span class="op">/</span><span class="va">ci_profile</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">rel_diffs_untransformed</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">abs</a></span><span class="op">(</span><span class="op">(</span><span class="va">ci_quadratic_untransformed</span> <span class="op">-</span> <span class="va">ci_profile</span><span class="op">)</span><span class="op">/</span><span class="va">ci_profile</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">rel_diffs_transformed</span> <span class="op"><</span> <span class="va">rel_diffs_untransformed</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 FALSE FALSE</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent_sink TRUE FALSE</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent_m1 TRUE FALSE</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1_sink FALSE FALSE</span> +<span class="r-out co"><span class="r-pr">#></span> sigma FALSE FALSE</span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">signif</a></span><span class="op">(</span><span class="va">rel_diffs_transformed</span>, <span class="fl">3</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 0.000541 0.000222</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent_sink 0.006650 0.008380</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent_m1 0.000183 0.002010</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1_sink 0.030700 0.029100</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 0.055000 0.032700</span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">signif</a></span><span class="op">(</span><span class="va">rel_diffs_untransformed</span>, <span class="fl">3</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 0.000541 0.000222</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent_sink 0.006800 0.002570</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent_m1 0.003740 0.001180</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1_sink 0.014700 0.002530</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 0.055000 0.032700</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Investigate a case with formation fractions</span></span></span> +<span class="r-in"><span><span class="va">f_d_2</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO.ff</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">ci_profile_ff</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_2</span>, method <span class="op">=</span> <span class="st">"profile"</span>, cores <span class="op">=</span> <span class="va">n_cores</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Profiling the likelihood</span> +<span class="r-in"><span><span class="va">ci_profile_ff</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 96.456003640 1.027703e+02</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent 0.090911032 1.071578e-01</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 0.003892606 6.702775e-03</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.471328495 5.611550e-01</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 2.535612399 3.985263e+00</span> +<span class="r-in"><span><span class="va">ci_quadratic_transformed_ff</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_2</span>, method <span class="op">=</span> <span class="st">"quadratic"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">ci_quadratic_transformed_ff</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 96.403833578 102.79311649</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent 0.090823771 0.10725430</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 0.004012219 0.00689755</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.469118824 0.55959615</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 2.396089689 3.85491806</span> +<span class="r-in"><span><span class="va">ci_quadratic_untransformed_ff</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_2</span>, method <span class="op">=</span> <span class="st">"quadratic"</span>, transformed <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">ci_quadratic_untransformed_ff</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 96.403833583 1.027931e+02</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent 0.090491913 1.069035e-01</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 0.003835485 6.685823e-03</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.469113477 5.598387e-01</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 2.396089689 3.854918e+00</span> +<span class="r-in"><span><span class="va">rel_diffs_transformed_ff</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">abs</a></span><span class="op">(</span><span class="op">(</span><span class="va">ci_quadratic_transformed_ff</span> <span class="op">-</span> <span class="va">ci_profile_ff</span><span class="op">)</span><span class="op">/</span><span class="va">ci_profile_ff</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">rel_diffs_untransformed_ff</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">abs</a></span><span class="op">(</span><span class="op">(</span><span class="va">ci_quadratic_untransformed_ff</span> <span class="op">-</span> <span class="va">ci_profile_ff</span><span class="op">)</span><span class="op">/</span><span class="va">ci_profile_ff</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># While the confidence interval for the parent rate constant is closer to</span></span></span> +<span class="r-in"><span><span class="co"># the profile based interval when using the internal parameter</span></span></span> +<span class="r-in"><span><span class="co"># transformation, the interval for the metabolite rate constant is 'better</span></span></span> +<span class="r-in"><span><span class="co"># without internal parameter transformation.</span></span></span> +<span class="r-in"><span><span class="va">rel_diffs_transformed_ff</span> <span class="op"><</span> <span class="va">rel_diffs_untransformed_ff</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 FALSE FALSE</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent TRUE TRUE</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 FALSE FALSE</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 TRUE FALSE</span> +<span class="r-out co"><span class="r-pr">#></span> sigma TRUE FALSE</span> +<span class="r-in"><span><span class="va">rel_diffs_transformed_ff</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 0.0005408690 0.0002217233</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent 0.0009598532 0.0009001864</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 0.0307283041 0.0290588361</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.0046881769 0.0027780063</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 0.0550252516 0.0327066836</span> +<span class="r-in"><span><span class="va">rel_diffs_untransformed_ff</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 0.0005408689 0.0002217232</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent 0.0046102156 0.0023732281</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 0.0146740690 0.0025291820</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.0046995211 0.0023457712</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 0.0550252516 0.0327066836</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># The profiling for the following fit does not finish in a reasonable time,</span></span></span> +<span class="r-in"><span><span class="co"># therefore we use the quadratic approximation</span></span></span> +<span class="r-in"><span><span class="va">m_synth_DFOP_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> use_of_ff <span class="op">=</span> <span class="st">"max"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">DFOP_par_c</span> <span class="op"><-</span> <span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span></span> +<span class="r-in"><span><span class="va">f_tc_2</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_DFOP_par</span>, <span class="va">DFOP_par_c</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>,</span></span> +<span class="r-in"><span> error_model_algorithm <span class="op">=</span> <span class="st">"direct"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_tc_2</span>, method <span class="op">=</span> <span class="st">"quadratic"</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 94.596039609 106.19954892</span> +<span class="r-out co"><span class="r-pr">#></span> k_M1 0.037605368 0.04490762</span> +<span class="r-out co"><span class="r-pr">#></span> k_M2 0.008568731 0.01087676</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_M1 0.021462489 0.62023882</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_M2 0.015165617 0.37975348</span> +<span class="r-out co"><span class="r-pr">#></span> k1 0.273897348 0.33388101</span> +<span class="r-out co"><span class="r-pr">#></span> k2 0.018614554 0.02250378</span> +<span class="r-out co"><span class="r-pr">#></span> g 0.671943411 0.73583305</span> +<span class="r-out co"><span class="r-pr">#></span> sigma_low 0.251283495 0.83992077</span> +<span class="r-out co"><span class="r-pr">#></span> rsd_high 0.040411024 0.07662008</span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_tc_2</span>, <span class="st">"parent_0"</span>, method <span class="op">=</span> <span class="st">"quadratic"</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 94.59604 106.1995</span> +<span class="r-in"><span><span class="co"># }</span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/create_deg_func.html b/docs/dev/reference/create_deg_func.html index 5d5870fe..e2a42873 100644 --- a/docs/dev/reference/create_deg_func.html +++ b/docs/dev/reference/create_deg_func.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.2</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -44,19 +44,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -125,8 +131,8 @@ <span class="r-in"><span> replications <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Loading required package: rbenchmark</span> <span class="r-out co"><span class="r-pr">#></span> test replications elapsed relative user.self sys.self user.child</span> -<span class="r-out co"><span class="r-pr">#></span> 1 analytical 2 0.401 1.000 0.401 0 0</span> -<span class="r-out co"><span class="r-pr">#></span> 2 deSolve 2 0.654 1.631 0.654 0 0</span> +<span class="r-out co"><span class="r-pr">#></span> 1 analytical 2 0.458 1.000 0.458 0 0</span> +<span class="r-out co"><span class="r-pr">#></span> 2 deSolve 2 0.713 1.557 0.713 0 0</span> <span class="r-out co"><span class="r-pr">#></span> sys.child</span> <span class="r-out co"><span class="r-pr">#></span> 1 0</span> <span class="r-out co"><span class="r-pr">#></span> 2 0</span> @@ -139,8 +145,8 @@ <span class="r-in"><span> deSolve <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">DFOP_SFO</span>, <span class="va">FOCUS_D</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span></span> <span class="r-in"><span> replications <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> test replications elapsed relative user.self sys.self user.child</span> -<span class="r-out co"><span class="r-pr">#></span> 1 analytical 2 0.843 1.000 0.843 0 0</span> -<span class="r-out co"><span class="r-pr">#></span> 2 deSolve 2 1.445 1.714 1.445 0 0</span> +<span class="r-out co"><span class="r-pr">#></span> 1 analytical 2 0.884 1.000 0.883 0.000 0</span> +<span class="r-out co"><span class="r-pr">#></span> 2 deSolve 2 1.526 1.726 1.522 0.004 0</span> <span class="r-out co"><span class="r-pr">#></span> sys.child</span> <span class="r-out co"><span class="r-pr">#></span> 1 0</span> <span class="r-out co"><span class="r-pr">#></span> 2 0</span> diff --git a/docs/dev/reference/dimethenamid_2018-1.png b/docs/dev/reference/dimethenamid_2018-1.png Binary files differindex 4300b0c0..c8b05bf5 100644 --- a/docs/dev/reference/dimethenamid_2018-1.png +++ b/docs/dev/reference/dimethenamid_2018-1.png diff --git a/docs/dev/reference/dimethenamid_2018.html b/docs/dev/reference/dimethenamid_2018.html index 2454a609..96ec73c6 100644 --- a/docs/dev/reference/dimethenamid_2018.html +++ b/docs/dev/reference/dimethenamid_2018.html @@ -22,7 +22,7 @@ constrained by data protection regulations."><meta name="robots" content="noinde </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.2</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -49,19 +49,25 @@ constrained by data protection regulations."><meta name="robots" content="noinde <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -180,17 +186,15 @@ specific pieces of information in the comments.</p> <span class="r-in"><span><span class="co"># influence of ill-defined rate constants that have</span></span></span> <span class="r-in"><span><span class="co"># extremely small values:</span></span></span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="mixed.html">mixed</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span> -<span class="r-plt img"><img src="dimethenamid_2018-1.png" alt="" width="700" height="433"></span> <span class="r-in"><span><span class="co"># If we disregards ill-defined rate constants, the results</span></span></span> <span class="r-in"><span><span class="co"># look more plausible, but the truth is likely to be in</span></span></span> <span class="r-in"><span><span class="co"># between these variants</span></span></span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="mixed.html">mixed</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> -<span class="r-plt img"><img src="dimethenamid_2018-2.png" alt="" width="700" height="433"></span> +<span class="r-plt img"><img src="dimethenamid_2018-1.png" alt="" width="700" height="433"></span> <span class="r-in"><span><span class="co"># We can also specify a default value for the failing</span></span></span> <span class="r-in"><span><span class="co"># log parameters, to mimic FOCUS guidance</span></span></span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="mixed.html">mixed</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span>,</span></span> <span class="r-in"><span> default_log_parms <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">/</span><span class="fl">1000</span><span class="op">)</span></span></span> -<span class="r-plt img"><img src="dimethenamid_2018-3.png" alt="" width="700" height="433"></span> <span class="r-in"><span><span class="co"># As these attempts are not satisfying, we use nonlinear mixed-effects models</span></span></span> <span class="r-in"><span><span class="co"># f_dmta_nlme_tc <- nlme(dmta_sfo_sfo3p_tc)</span></span></span> <span class="r-in"><span><span class="co"># nlme reaches maxIter = 50 without convergence</span></span></span> @@ -200,11 +204,11 @@ specific pieces of information in the comments.</p> <span class="r-in"><span><span class="co"># graphics device used)</span></span></span> <span class="r-in"><span><span class="co">#saemix::plot(f_dmta_saem_tc$so, plot.type = "convergence")</span></span></span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_dmta_saem_tc</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> saemix version used for fitting: 3.1 </span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.1.2 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.1 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Fri Sep 16 10:29:07 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Fri Sep 16 10:29:07 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> saemix version used for fitting: 3.2 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.2.2 </span> +<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Nov 24 08:05:16 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Nov 24 08:05:16 2022 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_DMTA/dt = - k_DMTA * DMTA</span> @@ -217,7 +221,7 @@ specific pieces of information in the comments.</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type deSolve </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted in 797.539 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted in 819.725 s</span> <span class="r-out co"><span class="r-pr">#></span> Using 300, 100 iterations and 9 chains</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Variance model: Two-component variance function </span> @@ -235,69 +239,79 @@ specific pieces of information in the comments.</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Likelihood computed by importance sampling</span> <span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> -<span class="r-out co"><span class="r-pr">#></span> 2276 2272 -1120</span> +<span class="r-out co"><span class="r-pr">#></span> 2276 2273 -1120</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Optimised parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0 88.5943 84.3961 92.7925</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_DMTA -3.0466 -3.5609 -2.5322</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M23 -4.0684 -4.9340 -3.2029</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M27 -3.8628 -4.2627 -3.4628</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M31 -3.9803 -4.4804 -3.4801</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_1 0.1304 -0.2186 0.4795</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_2 0.1490 -0.2559 0.5540</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_3 -1.3970 -1.6976 -1.0964</span> +<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> DMTA_0 88.3192 83.8656 92.7729</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_DMTA -3.0530 -3.5686 -2.5373</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M23 -4.0620 -4.9202 -3.2038</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M27 -3.8633 -4.2668 -3.4598</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M31 -3.9731 -4.4763 -3.4699</span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_1 0.1346 -0.2150 0.4841</span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_2 0.1449 -0.2593 0.5491</span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_3 -1.3882 -1.7011 -1.0753</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 0.9156 0.8229 1.0084</span> +<span class="r-out co"><span class="r-pr">#></span> b.1 0.1383 0.1215 0.1551</span> +<span class="r-out co"><span class="r-pr">#></span> SD.DMTA_0 3.7280 -0.6951 8.1511</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_DMTA 0.6431 0.2781 1.0080</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_M23 1.0096 0.3782 1.6409</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_M27 0.4583 0.1541 0.7625</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_M31 0.5738 0.1942 0.9533</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_DMTA_ilr_1 0.4119 0.1528 0.6709</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_DMTA_ilr_2 0.4780 0.1806 0.7754</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_DMTA_ilr_3 0.3657 0.1383 0.5931</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Correlation: </span> <span class="r-out co"><span class="r-pr">#></span> DMTA_0 l__DMTA lg__M23 lg__M27 lg__M31 f_DMTA__1 f_DMTA__2</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_DMTA 0.0309 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M23 -0.0231 -0.0031 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M27 -0.0381 -0.0048 0.0039 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M31 -0.0251 -0.0031 0.0021 0.0830 </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_1 -0.0046 -0.0006 0.0417 -0.0437 0.0328 </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_2 -0.0008 -0.0002 0.0214 -0.0270 -0.0909 -0.0361 </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_3 -0.1832 -0.0135 0.0434 0.0804 0.0395 -0.0070 0.0059 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k_DMTA 0.0303 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M23 -0.0229 -0.0032 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M27 -0.0372 -0.0049 0.0041 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M31 -0.0245 -0.0032 0.0022 0.0815 </span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_1 -0.0046 -0.0006 0.0415 -0.0433 0.0324 </span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_2 -0.0008 -0.0002 0.0214 -0.0267 -0.0893 -0.0361 </span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_3 -0.1755 -0.0135 0.0423 0.0775 0.0377 -0.0066 0.0060 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Random effects:</span> <span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> SD.DMTA_0 3.3651 -0.9649 7.6951</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k_DMTA 0.6415 0.2774 1.0055</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k_M23 1.0176 0.3809 1.6543</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k_M27 0.4538 0.1522 0.7554</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k_M31 0.5684 0.1905 0.9464</span> -<span class="r-out co"><span class="r-pr">#></span> SD.f_DMTA_ilr_1 0.4111 0.1524 0.6699</span> -<span class="r-out co"><span class="r-pr">#></span> SD.f_DMTA_ilr_2 0.4788 0.1808 0.7768</span> -<span class="r-out co"><span class="r-pr">#></span> SD.f_DMTA_ilr_3 0.3501 0.1316 0.5685</span> +<span class="r-out co"><span class="r-pr">#></span> SD.DMTA_0 3.7280 -0.6951 8.1511</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_DMTA 0.6431 0.2781 1.0080</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_M23 1.0096 0.3782 1.6409</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_M27 0.4583 0.1541 0.7625</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_M31 0.5738 0.1942 0.9533</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_DMTA_ilr_1 0.4119 0.1528 0.6709</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_DMTA_ilr_2 0.4780 0.1806 0.7754</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_DMTA_ilr_3 0.3657 0.1383 0.5931</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Variance model:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> a.1 0.9349 0.8409 1.029</span> -<span class="r-out co"><span class="r-pr">#></span> b.1 0.1344 0.1178 0.151</span> +<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 0.9156 0.8229 1.0084</span> +<span class="r-out co"><span class="r-pr">#></span> b.1 0.1383 0.1215 0.1551</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Backtransformed parameters:</span> <span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0 88.59431 84.396147 92.79246</span> -<span class="r-out co"><span class="r-pr">#></span> k_DMTA 0.04752 0.028413 0.07948</span> -<span class="r-out co"><span class="r-pr">#></span> k_M23 0.01710 0.007198 0.04064</span> -<span class="r-out co"><span class="r-pr">#></span> k_M27 0.02101 0.014084 0.03134</span> -<span class="r-out co"><span class="r-pr">#></span> k_M31 0.01868 0.011329 0.03080</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M23 0.14498 NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M27 0.12056 NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M31 0.11015 NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> DMTA_0 88.31924 83.865625 92.77286</span> +<span class="r-out co"><span class="r-pr">#></span> k_DMTA 0.04722 0.028196 0.07908</span> +<span class="r-out co"><span class="r-pr">#></span> k_M23 0.01721 0.007298 0.04061</span> +<span class="r-out co"><span class="r-pr">#></span> k_M27 0.02100 0.014027 0.03144</span> +<span class="r-out co"><span class="r-pr">#></span> k_M31 0.01882 0.011375 0.03112</span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M23 0.14608 NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M27 0.12077 NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M31 0.11123 NA NA</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Resulting formation fractions:</span> <span class="r-out co"><span class="r-pr">#></span> ff</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_M23 0.1450</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_M27 0.1206</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_M31 0.1101</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_sink 0.6243</span> +<span class="r-out co"><span class="r-pr">#></span> DMTA_M23 0.1461</span> +<span class="r-out co"><span class="r-pr">#></span> DMTA_M27 0.1208</span> +<span class="r-out co"><span class="r-pr">#></span> DMTA_M31 0.1112</span> +<span class="r-out co"><span class="r-pr">#></span> DMTA_sink 0.6219</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Estimated disappearance times:</span> <span class="r-out co"><span class="r-pr">#></span> DT50 DT90</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA 14.59 48.45</span> -<span class="r-out co"><span class="r-pr">#></span> M23 40.52 134.62</span> -<span class="r-out co"><span class="r-pr">#></span> M27 32.99 109.60</span> -<span class="r-out co"><span class="r-pr">#></span> M31 37.11 123.26</span> +<span class="r-out co"><span class="r-pr">#></span> DMTA 14.68 48.76</span> +<span class="r-out co"><span class="r-pr">#></span> M23 40.27 133.76</span> +<span class="r-out co"><span class="r-pr">#></span> M27 33.01 109.65</span> +<span class="r-out co"><span class="r-pr">#></span> M31 36.84 122.38</span> <span class="r-in"><span><span class="co"># As the confidence interval for the random effects of DMTA_0</span></span></span> <span class="r-in"><span><span class="co"># includes zero, we could try an alternative model without</span></span></span> <span class="r-in"><span><span class="co"># such random effects</span></span></span> diff --git a/docs/dev/reference/ds_mixed-1.png b/docs/dev/reference/ds_mixed-1.png Binary files differnew file mode 100644 index 00000000..a7f5c395 --- /dev/null +++ b/docs/dev/reference/ds_mixed-1.png diff --git a/docs/dev/reference/ds_mixed.html b/docs/dev/reference/ds_mixed.html new file mode 100644 index 00000000..09a6cc8c --- /dev/null +++ b/docs/dev/reference/ds_mixed.html @@ -0,0 +1,240 @@ +<!DOCTYPE html> +<!-- Generated by pkgdown: do not edit by hand 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class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/ds_mixed.R" class="external-link"><code>R/ds_mixed.R</code></a></small> + <div class="hidden name"><code>ds_mixed.Rd</code></div> + </div> + + <div class="ref-description"> + <p>The R code used to create this data object is installed with this package in +the 'dataset_generation' directory.</p> + </div> + + + + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span> <span class="va">sfo_mmkin</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">ds_sfo</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, cores <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">sfo_saem</span> <span class="op"><-</span> <span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">sfo_mmkin</span>, no_random_effect <span class="op">=</span> <span class="st">"parent_0"</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">sfo_saem</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="ds_mixed-1.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># This is the code used to generate the datasets</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/readLines.html" class="external-link">readLines</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/system.file.html" class="external-link">system.file</a></span><span class="op">(</span><span class="st">"dataset_generation/ds_mixed.R"</span>, package <span class="op">=</span> <span class="st">"mkin"</span><span class="op">)</span><span class="op">)</span>, sep <span class="op">=</span> <span class="st">"\n"</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> # Synthetic data for hierarchical kinetic models</span> +<span class="r-out co"><span class="r-pr">#></span> # Refactored version of the code previously in tests/testthat/setup_script.R</span> +<span class="r-out co"><span class="r-pr">#></span> # The number of datasets was 3 for FOMC, and 10 for HS in that script, now it</span> +<span class="r-out co"><span class="r-pr">#></span> # is always 15 for consistency</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> library(mkin) # We use mkinmod and mkinpredict</span> +<span class="r-out co"><span class="r-pr">#></span> sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)</span> +<span class="r-out co"><span class="r-pr">#></span> n <- 15</span> +<span class="r-out co"><span class="r-pr">#></span> log_sd <- 0.3</span> +<span class="r-out co"><span class="r-pr">#></span> err_1 = list(const = 1, prop = 0.05)</span> +<span class="r-out co"><span class="r-pr">#></span> tc <- function(value) sigma_twocomp(value, err_1$const, err_1$prop)</span> +<span class="r-out co"><span class="r-pr">#></span> const <- function(value) 2</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span> +<span class="r-out co"><span class="r-pr">#></span> SFO <- mkinmod(parent = mkinsub("SFO"))</span> +<span class="r-out co"><span class="r-pr">#></span> sfo_pop <- list(parent_0 = 100, k_parent = 0.03)</span> +<span class="r-out co"><span class="r-pr">#></span> sfo_parms <- as.matrix(data.frame(</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent = rlnorm(n, log(sfo_pop$k_parent), log_sd)))</span> +<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span> +<span class="r-out co"><span class="r-pr">#></span> ds_sfo <- lapply(1:n, function(i) {</span> +<span class="r-out co"><span class="r-pr">#></span> ds_mean <- mkinpredict(SFO, sfo_parms[i, ],</span> +<span class="r-out co"><span class="r-pr">#></span> c(parent = sfo_pop$parent_0), sampling_times)</span> +<span class="r-out co"><span class="r-pr">#></span> add_err(ds_mean, tc, n = 1)[[1]]</span> +<span class="r-out co"><span class="r-pr">#></span> })</span> +<span class="r-out co"><span class="r-pr">#></span> attr(ds_sfo, "pop") <- sfo_pop</span> +<span class="r-out co"><span class="r-pr">#></span> attr(ds_sfo, "parms") <- sfo_parms</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC <- mkinmod(parent = mkinsub("FOMC"))</span> +<span class="r-out co"><span class="r-pr">#></span> fomc_pop <- list(parent_0 = 100, alpha = 2, beta = 8)</span> +<span class="r-out co"><span class="r-pr">#></span> fomc_parms <- as.matrix(data.frame(</span> +<span class="r-out co"><span class="r-pr">#></span> alpha = rlnorm(n, log(fomc_pop$alpha), 0.4),</span> +<span class="r-out co"><span class="r-pr">#></span> beta = rlnorm(n, log(fomc_pop$beta), 0.2)))</span> +<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span> +<span class="r-out co"><span class="r-pr">#></span> ds_fomc <- lapply(1:n, function(i) {</span> +<span class="r-out co"><span class="r-pr">#></span> ds_mean <- mkinpredict(FOMC, fomc_parms[i, ],</span> +<span class="r-out co"><span class="r-pr">#></span> c(parent = fomc_pop$parent_0), sampling_times)</span> +<span class="r-out co"><span class="r-pr">#></span> add_err(ds_mean, tc, n = 1)[[1]]</span> +<span class="r-out co"><span class="r-pr">#></span> })</span> +<span class="r-out co"><span class="r-pr">#></span> attr(ds_fomc, "pop") <- fomc_pop</span> +<span class="r-out co"><span class="r-pr">#></span> attr(ds_fomc, "parms") <- fomc_parms</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span> +<span class="r-out co"><span class="r-pr">#></span> DFOP <- mkinmod(parent = mkinsub("DFOP"))</span> +<span class="r-out co"><span class="r-pr">#></span> dfop_pop <- list(parent_0 = 100, k1 = 0.06, k2 = 0.015, g = 0.4)</span> +<span class="r-out co"><span class="r-pr">#></span> dfop_parms <- as.matrix(data.frame(</span> +<span class="r-out co"><span class="r-pr">#></span> k1 = rlnorm(n, log(dfop_pop$k1), log_sd),</span> +<span class="r-out co"><span class="r-pr">#></span> k2 = rlnorm(n, log(dfop_pop$k2), log_sd),</span> +<span class="r-out co"><span class="r-pr">#></span> g = plogis(rnorm(n, qlogis(dfop_pop$g), log_sd))))</span> +<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span> +<span class="r-out co"><span class="r-pr">#></span> ds_dfop <- lapply(1:n, function(i) {</span> +<span class="r-out co"><span class="r-pr">#></span> ds_mean <- mkinpredict(DFOP, dfop_parms[i, ],</span> +<span class="r-out co"><span class="r-pr">#></span> c(parent = dfop_pop$parent_0), sampling_times)</span> +<span class="r-out co"><span class="r-pr">#></span> add_err(ds_mean, tc, n = 1)[[1]]</span> +<span class="r-out co"><span class="r-pr">#></span> })</span> +<span class="r-out co"><span class="r-pr">#></span> attr(ds_dfop, "pop") <- dfop_pop</span> +<span class="r-out co"><span class="r-pr">#></span> attr(ds_dfop, "parms") <- dfop_parms</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span> +<span class="r-out co"><span class="r-pr">#></span> HS <- mkinmod(parent = mkinsub("HS"))</span> +<span class="r-out co"><span class="r-pr">#></span> hs_pop <- list(parent_0 = 100, k1 = 0.08, k2 = 0.01, tb = 15)</span> +<span class="r-out co"><span class="r-pr">#></span> hs_parms <- as.matrix(data.frame(</span> +<span class="r-out co"><span class="r-pr">#></span> k1 = rlnorm(n, log(hs_pop$k1), log_sd),</span> +<span class="r-out co"><span class="r-pr">#></span> k2 = rlnorm(n, log(hs_pop$k2), log_sd),</span> +<span class="r-out co"><span class="r-pr">#></span> tb = rlnorm(n, log(hs_pop$tb), 0.1)))</span> +<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span> +<span class="r-out co"><span class="r-pr">#></span> ds_hs <- lapply(1:n, function(i) {</span> +<span class="r-out co"><span class="r-pr">#></span> ds_mean <- mkinpredict(HS, hs_parms[i, ],</span> +<span class="r-out co"><span class="r-pr">#></span> c(parent = hs_pop$parent_0), sampling_times)</span> +<span class="r-out co"><span class="r-pr">#></span> add_err(ds_mean, const, n = 1)[[1]]</span> +<span class="r-out co"><span class="r-pr">#></span> })</span> +<span class="r-out co"><span class="r-pr">#></span> attr(ds_hs, "pop") <- hs_pop</span> +<span class="r-out co"><span class="r-pr">#></span> attr(ds_hs, "parms") <- hs_parms</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span> +<span class="r-out co"><span class="r-pr">#></span> DFOP_SFO <- mkinmod(</span> +<span class="r-out co"><span class="r-pr">#></span> parent = mkinsub("DFOP", "m1"),</span> +<span class="r-out co"><span class="r-pr">#></span> m1 = mkinsub("SFO"),</span> +<span class="r-out co"><span class="r-pr">#></span> quiet = TRUE)</span> +<span class="r-out co"><span class="r-pr">#></span> dfop_sfo_pop <- list(parent_0 = 100,</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 = 0.007, f_parent_to_m1 = 0.5,</span> +<span class="r-out co"><span class="r-pr">#></span> k1 = 0.1, k2 = 0.02, g = 0.5)</span> +<span class="r-out co"><span class="r-pr">#></span> dfop_sfo_parms <- as.matrix(data.frame(</span> +<span class="r-out co"><span class="r-pr">#></span> k1 = rlnorm(n, log(dfop_sfo_pop$k1), log_sd),</span> +<span class="r-out co"><span class="r-pr">#></span> k2 = rlnorm(n, log(dfop_sfo_pop$k2), log_sd),</span> +<span class="r-out co"><span class="r-pr">#></span> g = plogis(rnorm(n, qlogis(dfop_sfo_pop$g), log_sd)),</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 = plogis(rnorm(n,</span> +<span class="r-out co"><span class="r-pr">#></span> qlogis(dfop_sfo_pop$f_parent_to_m1), log_sd)),</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 = rlnorm(n, log(dfop_sfo_pop$k_m1), log_sd)))</span> +<span class="r-out co"><span class="r-pr">#></span> ds_dfop_sfo_mean <- lapply(1:n,</span> +<span class="r-out co"><span class="r-pr">#></span> function(i) {</span> +<span class="r-out co"><span class="r-pr">#></span> mkinpredict(DFOP_SFO, dfop_sfo_parms[i, ],</span> +<span class="r-out co"><span class="r-pr">#></span> c(parent = dfop_sfo_pop$parent_0, m1 = 0), sampling_times)</span> +<span class="r-out co"><span class="r-pr">#></span> }</span> +<span class="r-out co"><span class="r-pr">#></span> )</span> +<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span> +<span class="r-out co"><span class="r-pr">#></span> ds_dfop_sfo <- lapply(ds_dfop_sfo_mean, function(ds) {</span> +<span class="r-out co"><span class="r-pr">#></span> add_err(ds,</span> +<span class="r-out co"><span class="r-pr">#></span> sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2),</span> +<span class="r-out co"><span class="r-pr">#></span> n = 1, secondary = "m1")[[1]]</span> +<span class="r-out co"><span class="r-pr">#></span> })</span> +<span class="r-out co"><span class="r-pr">#></span> attr(ds_dfop_sfo, "pop") <- dfop_sfo_pop</span> +<span class="r-out co"><span class="r-pr">#></span> attr(ds_dfop_sfo, "parms") <- dfop_sfo_parms</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> #save(ds_sfo, ds_fomc, ds_dfop, ds_hs, ds_dfop_sfo, file = "data/ds_mixed.rda", version = 2)</span> +</code></pre></div> + </div> + </div> + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> +</div> + + + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +</div> + + </footer></div> + + + + + + + </body></html> + diff --git a/docs/dev/reference/endpoints.html b/docs/dev/reference/endpoints.html index e8198521..f8064098 100644 --- a/docs/dev/reference/endpoints.html +++ b/docs/dev/reference/endpoints.html @@ -23,7 +23,7 @@ advantage that the SFORB model can also be used for metabolites."><meta name="ro </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -65,7 +65,10 @@ advantage that the SFORB model can also be used for metabolites."><meta name="ro <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/example_analysis/dlls/sforb_sfo2.so b/docs/dev/reference/example_analysis/dlls/sforb_sfo2.so Binary files differnew file mode 100755 index 00000000..a692256d --- /dev/null +++ b/docs/dev/reference/example_analysis/dlls/sforb_sfo2.so diff --git a/docs/dev/reference/example_analysis/example_analysis.Rmd b/docs/dev/reference/example_analysis/example_analysis.Rmd new file mode 100644 index 00000000..38a6bd20 --- /dev/null +++ b/docs/dev/reference/example_analysis/example_analysis.Rmd @@ -0,0 +1,314 @@ +--- +title: "Hierarchical kinetic modelling of degradation data" +author: +date: +output: mkin::hierarchical_kinetics +geometry: margin=2cm +--- + +\clearpage + +# Setup + +```{r packages, cache = FALSE, message = FALSE} +library(mkin) +library(knitr) +library(saemix) +library(parallel) +library(readxl) +``` + +```{r n_cores, cache = FALSE} +n_cores <- detectCores() + +if (Sys.info()["sysname"] == "Windows") { + cl <- makePSOCKcluster(n_cores) +} else { + cl <- makeForkCluster(n_cores) +} +``` + +\clearpage + +# Introduction + +This report shows hierarchical kinetic modelling for ... +The data were obtained from ... + +```{r ds} +data_path <- system.file( + "testdata", "lambda-cyhalothrin_soil_efsa_2014.xlsx", + package = "mkin") +ds <- read_spreadsheet(data_path, valid_datasets = c(1:4, 7:13)) +covariates <- attr(ds, "covariates") +``` + +The covariate data are shown below. + +```{r results = "asis", dependson = "ds", echo = FALSE} +kable(covariates, caption = "Covariate data for all datasets") +``` + +\clearpage + +The datasets with the residue time series are shown in the tables below. Please +refer to the spreadsheet for details like data sources, treatment of values +below reporting limits and time step normalisation factors. + +```{r results = "asis", dependson = "ds", echo = FALSE} +for (ds_name in names(ds)) { + print( + kable(mkin_long_to_wide(ds[[ds_name]]), + caption = paste("Dataset", ds_name), + booktabs = TRUE, row.names = FALSE)) + cat("\n\\clearpage\n") +} +``` + +# Parent only evaluations + +The following code performs separate fits of the candidate degradation models +to all datasets using constant variance and the two-component error model. + +```{r parent-sep, dependson = "ds"} +parent_deg_mods <- c("SFO", "FOMC", "DFOP", "SFORB") +errmods <- c(const = "constant variance", tc = "two-component error") +parent_sep_const <- mmkin( + parent_deg_mods, ds, + error_model = "const", + cluster = cl, quiet = TRUE) +parent_sep_tc <- update(parent_sep_const, error_model = "tc") +``` + +To select the parent model, the corresponding hierarchical fits are performed below. + +```{r parent-mhmkin, dependson = "parent-sep"} +parent_mhmkin <- mhmkin(list(parent_sep_const, parent_sep_tc), cluster = cl) +status(parent_mhmkin) |> kable() +``` + +All fits terminate without errors (status OK). The check for ill-defined +parameters shows that not all random effect parameters can be robustly +quantified. + +```{r dependson = "parent_mhmkin"} +illparms(parent_mhmkin) |> kable() +``` + +Therefore, the fits are updated, excluding random effects that were +ill-defined according to the `illparms` function. The status of the fits +is checked. + +```{r parent-mhmkin-refined} +parent_mhmkin_refined <- update(parent_mhmkin, + no_random_effect = illparms(parent_mhmkin)) +status(parent_mhmkin_refined) |> kable() +``` + +Also, it is checked if the AIC values of the refined fits are actually smaller +than the AIC values of the original fits. + +```{r dependson = "parent-mhmkin-refined"} +(AIC(parent_mhmkin_refined) < AIC(parent_mhmkin)) |> kable() +``` + +From the refined fits, the most suitable model is selected using the AIC. + +```{r parent-best, dependson = "parent-mhmkin"} +aic_parent <- AIC(parent_mhmkin_refined) +min_aic <- which(aic_parent == min(aic_parent), arr.ind = TRUE) +best_degmod_parent <- rownames(aic_parent)[min_aic[1]] +best_errmod_parent <- colnames(aic_parent)[min_aic[2]] +anova(parent_mhmkin_refined) |> kable(digits = 1) +parent_best <- parent_mhmkin_refined[[best_degmod_parent, best_errmod_parent]] +``` + +Based on the AIC, the combination of the `r best_degmod_parent` degradation +model with the error model `r errmods[best_errmod_parent]` is identified to +be most suitable for the degradation of the parent. The check below +confirms that no ill-defined parameters remain for this combined model. + +```{r dependson = "parent-best"} +illparms(parent_best) +``` + +The corresponding fit is plotted below. + +```{r dependson = "parent-best"} +plot(parent_best) +``` +The fitted parameters, together with approximate confidence +intervals are listed below. + +```{r dependson = "parent-best"} +parms(parent_best, ci = TRUE) |> kable(digits = 3) +``` + +To investigate a potential covariate influence on degradation parameters, a +covariate model is added to the hierarchical model for each of the degradation +parameters with well-defined random effects. Also, a version with covariate +models for both of them is fitted. + +```{r parent-best-pH} +parent_best_pH_1 <- update(parent_best, covariates = covariates, + covariate_models = list(log_k_lambda_free ~ pH)) +parent_best_pH_2 <- update(parent_best, covariates = covariates, + covariate_models = list(log_k_lambda_bound_free ~ pH)) +parent_best_pH_3 <- update(parent_best, covariates = covariates, + covariate_models = list(log_k_lambda_free ~ pH, log_k_lambda_bound_free ~ pH)) +``` + +The resulting models are compared. + +```{r dependson = "parent-best-pH"} +anova(parent_best, parent_best_pH_1, parent_best_pH_2, parent_best_pH_3) |> + kable(digits = 1) +``` + +The model fit with the lowest AIC is the one with a pH correlation of the +desorption rate constant `k_lambda_bound_free`. Plot and parameter listing +of this fit are shown below. Also, it is confirmed that no ill-defined +variance parameters are found. + +```{r dependson = "parent-best-pH"} +plot(parent_best_pH_2) +``` + +```{r dependson = "parent-best-pH"} +illparms(parent_best_pH_2) +parms(parent_best_pH_2, ci = TRUE) |> kable(digits = 3) +``` + +\clearpage + +# Pathway fits + +As an example of a pathway fit, a model with SFORB for the parent compound and +parallel formation of two metabolites is set up. + +```{r path-1-degmod} +if (!dir.exists("dlls")) dir.create("dlls") + +m_sforb_sfo2 = mkinmod( + lambda = mkinsub("SFORB", to = c("c_V", "c_XV")), + c_V = mkinsub("SFO"), + c_XV = mkinsub("SFO"), + name = "sforb_sfo2", + dll_dir = "dlls", + overwrite = TRUE, quiet = TRUE +) +``` + +Separate evaluations of all datasets are performed with constant variance +and using two-component error. + +```{r path-1-sep, dependson = c("path-1-degmod", "ds")} +sforb_sep_const <- mmkin(list(sforb_path = m_sforb_sfo2), ds, + cluster = cl, quiet = TRUE) +sforb_sep_tc <- update(sforb_sep_const, error_model = "tc") +``` + +The separate fits with constant variance are plotted. + +```{r dependson = "path-1-sep", fig.height = 9} +plot(mixed(sforb_sep_const)) +``` + +The two corresponding hierarchical fits, with the random effects for the parent +degradation parameters excluded as discussed above, and including the covariate +model that was identified for the parent degradation, are attempted below. + +```{r path-1, dependson = "path-1-sep"} +path_1 <- mhmkin(list(sforb_sep_const, sforb_sep_tc), + no_random_effect = c("lambda_free_0", "log_k_lambda_free_bound"), + covariates = covariates, covariate_models = list(log_k_lambda_bound_free ~ pH), + cluster = cl) +``` + +```{r dependson = "path-1"} +status(path_1) |> kable() +``` + +The status information shows that both fits were successfully completed. + +```{r dependson = "path-1"} +anova(path_1) |> kable(digits = 1) +``` +Model comparison shows that the two-component error model provides a much +better fit. + +```{r dependson = "path-1"} +illparms(path_1[["sforb_path", "tc"]]) +``` + +Two ill-defined variance components are found. Therefore, the fit is +repeated with the corresponding random effects removed. + +```{r path-1-refined, dependson = "path-1"} +path_1_refined <- update(path_1[["sforb_path", "tc"]], + no_random_effect = c("lambda_free_0", "log_k_lambda_free_bound", + "log_k_c_XV", "f_lambda_ilr_2")) +``` + +The empty output of the illparms function indicates that there are no +ill-defined parameters remaining in the refined fit. + +```{r dependson = "path-1-refined"} +illparms(path_1_refined) +``` + +Below, the refined fit is plotted and the fitted parameters are shown together +with their 95% confidence intervals. + +```{r dependson = "path-1-refined", fig.height = 9} +plot(path_1_refined) +``` + +```{r dependson = "path-1-refined", fig.height = 9} +parms(path_1_refined, ci = TRUE) |> kable(digits = 3) +``` + +\clearpage + +# Appendix + +## Listings of initial parent fits + +```{r listings-parent, results = "asis", echo = FALSE, dependson = "parent_mhmkin"} +for (deg_mod in parent_deg_mods) { + for (err_mod in c("const", "tc")) { + caption <- paste("Hierarchical", deg_mod, "fit with", errmods[err_mod]) + tex_listing(parent_mhmkin[[deg_mod, err_mod]], caption) + } +} +``` + +## Listings of refined parent fits + +```{r listings-parent-refined, results = "asis", echo = FALSE, dependson = "parent_mhmkin_refined"} +for (deg_mod in parent_deg_mods) { + for (err_mod in c("const", "tc")) { + caption <- paste("Refined hierarchical", deg_mod, "fit with", errmods[err_mod]) + tex_listing(parent_mhmkin_refined[[deg_mod, err_mod]], caption) + } +} +``` + +## Listings of pathway fits + +```{r listings-path-1, results = "asis", echo = FALSE, dependson = "path-1-refined"} +tex_listing(path_1[["sforb_path", "const"]], + caption = "Hierarchical fit of SFORB-SFO2 with constant variance") +tex_listing(path_1[["sforb_path", "tc"]], + caption = "Hierarchical fit of SFORB-SFO2 with two-component error") +tex_listing(path_1_refined, + caption = "Refined hierarchical fit of SFORB-SFO2 with two-component error") +``` + +## Session info + +```{r echo = FALSE, cache = FALSE} +parallel::stopCluster(cl) +sessionInfo() +``` + diff --git a/docs/dev/reference/example_analysis/header.tex b/docs/dev/reference/example_analysis/header.tex new file mode 100644 index 00000000..a2b7ce83 --- /dev/null +++ b/docs/dev/reference/example_analysis/header.tex @@ -0,0 +1 @@ +\definecolor{shadecolor}{RGB}{248,248,248} diff --git a/docs/dev/reference/example_analysis/skeleton.pdf b/docs/dev/reference/example_analysis/skeleton.pdf Binary files differnew file mode 100644 index 00000000..53c5fb31 --- /dev/null +++ b/docs/dev/reference/example_analysis/skeleton.pdf diff --git a/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-11-1.pdf b/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-11-1.pdf Binary files differnew file mode 100644 index 00000000..ab685d92 --- /dev/null +++ b/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-11-1.pdf diff --git a/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-16-1.pdf b/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-16-1.pdf Binary files differnew file mode 100644 index 00000000..5d88063b --- /dev/null +++ b/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-16-1.pdf diff --git a/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-6-1.pdf b/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-6-1.pdf Binary files differnew file mode 100644 index 00000000..5e0d7b6f --- /dev/null +++ b/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-6-1.pdf diff --git a/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-9-1.pdf b/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-9-1.pdf Binary files differnew file mode 100644 index 00000000..eecd06a8 --- /dev/null +++ b/docs/dev/reference/example_analysis/skeleton_files/figure-latex/unnamed-chunk-9-1.pdf diff --git a/docs/dev/reference/experimental_data_for_UBA-1.png b/docs/dev/reference/experimental_data_for_UBA-1.png Binary files differindex 33946ded..b7b4d63b 100644 --- a/docs/dev/reference/experimental_data_for_UBA-1.png +++ b/docs/dev/reference/experimental_data_for_UBA-1.png diff --git a/docs/dev/reference/experimental_data_for_UBA.html b/docs/dev/reference/experimental_data_for_UBA.html index 9904370f..a51ace27 100644 --- a/docs/dev/reference/experimental_data_for_UBA.html +++ b/docs/dev/reference/experimental_data_for_UBA.html @@ -1,46 +1,5 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - 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Kinetic evaluations shown for these datasets are intended to illustrate and advance error model specifications. The fact that these data and some results are shown here do not imply a license to use them in the context of @@ -68,28 +27,14 @@ Dataset 11 is from the Renewal Assessment Report (RAR) for 2,4-D the exception of the day three sampling of metabolite A2, which was set to one half of the LOD reported to be 1% AR. Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl - (United Kingdom, 2014, p. 81)." /> - - -<meta name="robots" content="noindex"> - -<!-- mathjax --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script> - -<!--[if lt IE 9]> + (United Kingdom, 2014, p. 81)."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> - - - - </head> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -100,23 +45,21 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -126,44 +69,46 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Experimental datasets used for development and testing of error models</h1> @@ -203,30 +148,33 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl (United Kingdom, 2014, p. 81).</p> </div> - <pre class="usage"><span class='va'>experimental_data_for_UBA_2019</span></pre> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">experimental_data_for_UBA_2019</span></span></code></pre></div> + </div> + <div id="format"> + <h2>Format</h2> + <p>A list containing twelve datasets as an R6 class defined by <code><a href="mkinds.html">mkinds</a></code>, + each containing, among others, the following components</p><dl><dt><code>title</code></dt> +<dd><p>The name of the dataset, e.g. <code>Soil 1</code></p></dd> - <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2> + <dt><code>data</code></dt> +<dd><p>A data frame with the data in the form expected by <code><a href="mkinfit.html">mkinfit</a></code></p></dd> - <p>A list containing twelve datasets as an R6 class defined by <code><a href='mkinds.html'>mkinds</a></code>, - each containing, among others, the following components</p><dl> - <dt><code>title</code></dt><dd><p>The name of the dataset, e.g. <code>Soil 1</code></p></dd> - <dt><code>data</code></dt><dd><p>A data frame with the data in the form expected by <code><a href='mkinfit.html'>mkinfit</a></code></p></dd> -</dl> - - <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2> - +</dl></div> + <div id="source"> + <h2>Source</h2> <p>Austria (2015). Ethofumesate Renewal Assessment Report Volume 3 Annex B.8 (AS)</p> <p>Belgium (2014). Isofetamid (IKF-5411) Draft Assessment Report Volume 3 Annex B.8 (AS)</p> <p>France (2015). Imazamox Draft Renewal Assessment Report Volume 3 Annex B.8 (AS)</p> -<p>FOCUS (2014) “Generic guidance for Estimating Persistence and +<p>FOCUS (2014) “Generic guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU - Registration” Report of the FOCUS Work Group on Degradation Kinetics, + Registration” Report of the FOCUS Work Group on Degradation Kinetics, Version 1.1, 18 December 2014 - <a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> <p>Germany (2013). Renewal Assessment Report Glyphosate Volume 3 Annex B.8: Environmental Fate and Behaviour</p> <p>Hellas (2013). Renewal Assessment Report 2,4-D Volume 3 Annex B.8: Fate and behaviour in the @@ -236,70 +184,75 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl <p>United Kingdom (2014). Thifensulfuron-methyl - Annex B.8 (Volume 3) to the Report and Proposed Decision of the United Kingdom made to the European Commission under Regulation (EC) No. 1141/2010 for renewal of an active substance</p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'><span class='co'># \dontrun{</span> - -<span class='co'># Model definitions</span> -<span class='va'>sfo_sfo</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span> - parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='st'>"A1"</span><span class='op'>)</span>, - A1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span>, - use_of_ff <span class='op'>=</span> <span class='st'>"max"</span> -<span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'> -<span class='va'>dfop_sfo</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span> - parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"DFOP"</span>, to <span class='op'>=</span> <span class='st'>"A1"</span><span class='op'>)</span>, - A1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span>, - use_of_ff <span class='op'>=</span> <span class='st'>"max"</span> -<span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'> -<span class='va'>sfo_sfo_sfo</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span> - parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='st'>"A1"</span><span class='op'>)</span>, - A1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='st'>"A2"</span><span class='op'>)</span>, - A2 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span>, - use_of_ff <span class='op'>=</span> <span class='st'>"max"</span> -<span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'> -<span class='va'>dfop_sfo_sfo</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span> - parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"DFOP"</span>, to <span class='op'>=</span> <span class='st'>"A1"</span><span class='op'>)</span>, - A1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='st'>"A2"</span><span class='op'>)</span>, - A2 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span>, - use_of_ff <span class='op'>=</span> <span class='st'>"max"</span> -<span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'><span class='va'>d_1_2</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='va'>experimental_data_for_UBA_2019</span><span class='op'>[</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>2</span><span class='op'>]</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span> <span class='va'>x</span><span class='op'>$</span><span class='va'>data</span><span class='op'>)</span> -<span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span><span class='op'>(</span><span class='va'>d_1_2</span><span class='op'>)</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span><span class='op'>(</span><span class='st'>"Soil"</span>, <span class='fl'>1</span><span class='op'>:</span><span class='fl'>2</span><span class='op'>)</span> - - -<span class='va'>f_1_2_tc</span> <span class='op'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='st'>"DFOP-SFO-SFO"</span> <span class='op'>=</span> <span class='va'>dfop_sfo_sfo</span><span class='op'>)</span>, <span class='va'>d_1_2</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span><span class='op'>)</span> - -<span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_1_2_tc</span>, resplot <span class='op'>=</span> <span class='st'>"errmod"</span><span class='op'>)</span> -</div><div class='img'><img src='experimental_data_for_UBA-1.png' alt='' width='700' height='433' /></div><div class='input'> -<span class='co'># }</span></div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Model definitions</span></span></span> +<span class="r-in"><span><span class="va">sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"A1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> use_of_ff <span class="op">=</span> <span class="st">"max"</span></span></span> +<span class="r-in"><span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">dfop_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="st">"A1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> use_of_ff <span class="op">=</span> <span class="st">"max"</span></span></span> +<span class="r-in"><span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">sfo_sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"A1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"A2"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> A2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> use_of_ff <span class="op">=</span> <span class="st">"max"</span></span></span> +<span class="r-in"><span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">dfop_sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="st">"A1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"A2"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> A2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> use_of_ff <span class="op">=</span> <span class="st">"max"</span></span></span> +<span class="r-in"><span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span><span class="va">d_1_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">2</span><span class="op">]</span>, <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">d_1_2</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Soil"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">2</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">f_1_2_tc</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"DFOP-SFO-SFO"</span> <span class="op">=</span> <span class="va">dfop_sfo_sfo</span><span class="op">)</span>, <span class="va">d_1_2</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_1_2_tc</span>, resplot <span class="op">=</span> <span class="st">"errmod"</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="experimental_data_for_UBA-1.png" alt="" width="700" height="433"></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/f_time_norm_focus.html b/docs/dev/reference/f_time_norm_focus.html index 852e00a0..b556df04 100644 --- a/docs/dev/reference/f_time_norm_focus.html +++ 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-73,23 +18,21 @@ in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369)." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -99,48 +42,50 @@ in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369)." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Normalisation factors for aerobic soil degradation according to FOCUS guidance</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/f_time_norm_focus.R'><code>R/f_time_norm_focus.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/f_time_norm_focus.R" class="external-link"><code>R/f_time_norm_focus.R</code></a></small> <div class="hidden name"><code>f_time_norm_focus.Rd</code></div> </div> @@ -149,137 +94,138 @@ in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369)." /> in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369).</p> </div> - <pre class="usage"><span class='fu'>f_time_norm_focus</span><span class='op'>(</span><span class='va'>object</span>, <span class='va'>...</span><span class='op'>)</span> - -<span class='co'># S3 method for numeric</span> -<span class='fu'>f_time_norm_focus</span><span class='op'>(</span> - <span class='va'>object</span>, - moisture <span class='op'>=</span> <span class='cn'>NA</span>, - field_moisture <span class='op'>=</span> <span class='cn'>NA</span>, - temperature <span class='op'>=</span> <span class='va'>object</span>, - Q10 <span class='op'>=</span> <span class='fl'>2.58</span>, - walker <span class='op'>=</span> <span class='fl'>0.7</span>, - f_na <span class='op'>=</span> <span class='cn'>NA</span>, - <span class='va'>...</span> -<span class='op'>)</span> - -<span class='co'># S3 method for mkindsg</span> -<span class='fu'>f_time_norm_focus</span><span class='op'>(</span> - <span class='va'>object</span>, - study_moisture_ref_source <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"auto"</span>, <span class='st'>"meta"</span>, <span class='st'>"focus"</span><span class='op'>)</span>, - Q10 <span class='op'>=</span> <span class='fl'>2.58</span>, - walker <span class='op'>=</span> <span class='fl'>0.7</span>, - f_na <span class='op'>=</span> <span class='cn'>NA</span>, - <span class='va'>...</span> -<span class='op'>)</span></pre> - - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>object</th> - <td><p>An object containing information used for the calculations</p></td> - </tr> - <tr> - <th>...</th> - <td><p>Currently not used</p></td> - </tr> - <tr> - <th>moisture</th> - <td><p>Numeric vector of moisture contents in \% w/w</p></td> - </tr> - <tr> - <th>field_moisture</th> - <td><p>Numeric vector of moisture contents at field capacity -(pF2) in \% w/w</p></td> - </tr> - <tr> - <th>temperature</th> - <td><p>Numeric vector of temperatures in °C</p></td> - </tr> - <tr> - <th>Q10</th> - <td><p>The Q10 value used for temperature normalisation</p></td> - </tr> - <tr> - <th>walker</th> - <td><p>The Walker exponent used for moisture normalisation</p></td> - </tr> - <tr> - <th>f_na</th> - <td><p>The factor to use for NA values. If set to NA, only factors -for complete cases will be returned.</p></td> - </tr> - <tr> - <th>study_moisture_ref_source</th> - <td><p>Source for the reference value + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">f_time_norm_focus</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for numeric</span></span> +<span><span class="fu">f_time_norm_focus</span><span class="op">(</span></span> +<span> <span class="va">object</span>,</span> +<span> moisture <span class="op">=</span> <span class="cn">NA</span>,</span> +<span> field_moisture <span class="op">=</span> <span class="cn">NA</span>,</span> +<span> temperature <span class="op">=</span> <span class="va">object</span>,</span> +<span> Q10 <span class="op">=</span> <span class="fl">2.58</span>,</span> +<span> walker <span class="op">=</span> <span class="fl">0.7</span>,</span> +<span> f_na <span class="op">=</span> <span class="cn">NA</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mkindsg</span></span> +<span><span class="fu">f_time_norm_focus</span><span class="op">(</span></span> +<span> <span class="va">object</span>,</span> +<span> study_moisture_ref_source <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"auto"</span>, <span class="st">"meta"</span>, <span class="st">"focus"</span><span class="op">)</span>,</span> +<span> Q10 <span class="op">=</span> <span class="fl">2.58</span>,</span> +<span> walker <span class="op">=</span> <span class="fl">0.7</span>,</span> +<span> f_na <span class="op">=</span> <span class="cn">NA</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>object</dt> +<dd><p>An object containing information used for the calculations</p></dd> + + +<dt>...</dt> +<dd><p>Currently not used</p></dd> + + +<dt>moisture</dt> +<dd><p>Numeric vector of moisture contents in \% w/w</p></dd> + + +<dt>field_moisture</dt> +<dd><p>Numeric vector of moisture contents at field capacity +(pF2) in \% w/w</p></dd> + + +<dt>temperature</dt> +<dd><p>Numeric vector of temperatures in °C</p></dd> + + +<dt>Q10</dt> +<dd><p>The Q10 value used for temperature normalisation</p></dd> + + +<dt>walker</dt> +<dd><p>The Walker exponent used for moisture normalisation</p></dd> + + +<dt>f_na</dt> +<dd><p>The factor to use for NA values. If set to NA, only factors +for complete cases will be returned.</p></dd> + + +<dt>study_moisture_ref_source</dt> +<dd><p>Source for the reference value used to calculate the study moisture. If 'auto', preference is given to a reference moisture given in the meta information, otherwise -the focus soil moisture for the soil class is used</p></td> - </tr> - </table> +the focus soil moisture for the soil class is used</p></dd> - <h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2> - - <p>FOCUS (2006) “Guidance Document on Estimating Persistence +</dl></div> + <div id="references"> + <h2>References</h2> + <p>FOCUS (2006) “Guidance Document on Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in -EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, +EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, -<a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a> -FOCUS (2014) “Generic guidance for Estimating Persistence +<a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a> +FOCUS (2014) “Generic guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in -EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, +EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, Version 1.1, 18 December 2014 -<a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> - <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2> - - <div class='dont-index'><p><a href='focus_soil_moisture.html'>focus_soil_moisture</a></p></div> - - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'><span class='fu'>f_time_norm_focus</span><span class='op'>(</span><span class='fl'>25</span>, <span class='fl'>20</span>, <span class='fl'>25</span><span class='op'>)</span> <span class='co'># 1.37, compare FOCUS 2014 p. 184</span> -</div><div class='output co'>#> [1] 1.373956</div><div class='input'> -<span class='va'>D24_2014</span><span class='op'>$</span><span class='va'>meta</span> -</div><div class='output co'>#> study usda_soil_type study_moisture_ref_type -#> Mississippi Cohen 1991 Silt loam <NA> -#> Fayette Liu and Adelfinskaya 2011 Silt loam pF1 -#> RefSol 03-G Liu and Adelfinskaya 2011 Loam pF1 -#> Site E1 Liu and Adelfinskaya 2011 Loam pF1 -#> Site I2 Liu and Adelfinskaya 2011 Loamy sand pF1 -#> rel_moisture temperature -#> Mississippi NA 25 -#> Fayette 0.5 20 -#> RefSol 03-G 0.5 20 -#> Site E1 0.5 20 -#> Site I2 0.5 20</div><div class='input'><span class='co'># No moisture normalisation in the first dataset, so we use f_na = 1 to get</span> -<span class='co'># temperature only normalisation as in the EU evaluation</span> -<span class='fu'>f_time_norm_focus</span><span class='op'>(</span><span class='va'>D24_2014</span>, study_moisture_ref_source <span class='op'>=</span> <span class='st'>"focus"</span>, f_na <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span> -</div><div class='output co'>#> $time_norm was set to -#> [1] 1.6062378 0.7118732 0.7156063 0.7156063 0.8977124</div></pre> +<a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + </div> + <div id="see-also"> + <h2>See also</h2> + <div class="dont-index"><p><a href="focus_soil_moisture.html">focus_soil_moisture</a></p></div> + </div> + + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu">f_time_norm_focus</span><span class="op">(</span><span class="fl">25</span>, <span class="fl">20</span>, <span class="fl">25</span><span class="op">)</span> <span class="co"># 1.37, compare FOCUS 2014 p. 184</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] 1.373956</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">D24_2014</span><span class="op">$</span><span class="va">meta</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> study usda_soil_type study_moisture_ref_type</span> +<span class="r-out co"><span class="r-pr">#></span> Mississippi Cohen 1991 Silt loam <NA></span> +<span class="r-out co"><span class="r-pr">#></span> Fayette Liu and Adelfinskaya 2011 Silt loam pF1</span> +<span class="r-out co"><span class="r-pr">#></span> RefSol 03-G Liu and Adelfinskaya 2011 Loam pF1</span> +<span class="r-out co"><span class="r-pr">#></span> Site E1 Liu and Adelfinskaya 2011 Loam pF1</span> +<span class="r-out co"><span class="r-pr">#></span> Site I2 Liu and Adelfinskaya 2011 Loamy sand pF1</span> +<span class="r-out co"><span class="r-pr">#></span> rel_moisture temperature</span> +<span class="r-out co"><span class="r-pr">#></span> Mississippi NA 25</span> +<span class="r-out co"><span class="r-pr">#></span> Fayette 0.5 20</span> +<span class="r-out co"><span class="r-pr">#></span> RefSol 03-G 0.5 20</span> +<span class="r-out co"><span class="r-pr">#></span> Site E1 0.5 20</span> +<span class="r-out co"><span class="r-pr">#></span> Site I2 0.5 20</span> +<span class="r-in"><span><span class="co"># No moisture normalisation in the first dataset, so we use f_na = 1 to get</span></span></span> +<span class="r-in"><span><span class="co"># temperature only normalisation as in the EU evaluation</span></span></span> +<span class="r-in"><span><span class="fu">f_time_norm_focus</span><span class="op">(</span><span class="va">D24_2014</span>, study_moisture_ref_source <span class="op">=</span> <span class="st">"focus"</span>, f_na <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> $f_time_norm was (re)set to normalised values</span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - 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The table assumes field capacity +corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> - - +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - </head> - - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -73,23 +18,21 @@ corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" 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href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/focus_soil_moisture.R'><code>R/focus_soil_moisture.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/focus_soil_moisture.R" class="external-link"><code>R/focus_soil_moisture.R</code></a></small> <div class="hidden name"><code>focus_soil_moisture.Rd</code></div> </div> @@ -149,58 +94,60 @@ corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5." /> corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5.</p> </div> - <pre class="usage"><span class='va'>focus_soil_moisture</span></pre> - - - <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">focus_soil_moisture</span></span></code></pre></div> + </div> + <div id="format"> + <h2>Format</h2> <p>A matrix with upper case USDA soil classes as row names, and water tension ('pF1', 'pF2', 'pF 2.5') as column names</p> - <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2> - + </div> + <div id="source"> + <h2>Source</h2> <p>Anonymous (2014) Generic Guidance for Tier 1 FOCUS Ground Water Assessment -Version 2.2, May 2014 <a href='https://esdac.jrc.ec.europa.eu/projects/ground-water'>https://esdac.jrc.ec.europa.eu/projects/ground-water</a></p> - - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'><span class='va'>focus_soil_moisture</span> -</div><div class='output co'>#> pF1 pF2 pF2.5 -#> Sand 24 12 7 -#> Loamy sand 24 14 9 -#> Sandy loam 27 19 15 -#> Sandy clay loam 28 22 18 -#> Clay loam 32 28 25 -#> Loam 31 25 21 -#> Silt loam 32 26 21 -#> Silty clay loam 34 30 27 -#> Silt 31 27 21 -#> Sandy clay 41 35 31 -#> Silty clay 44 40 36 -#> Clay 53 48 43</div></pre> +Version 2.2, May 2014 <a href="https://esdac.jrc.ec.europa.eu/projects/ground-water" class="external-link">https://esdac.jrc.ec.europa.eu/projects/ground-water</a></p> + </div> + + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">focus_soil_moisture</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> pF1 pF2 pF2.5</span> +<span class="r-out co"><span class="r-pr">#></span> Sand 24 12 7</span> +<span class="r-out co"><span class="r-pr">#></span> Loamy sand 24 14 9</span> +<span class="r-out co"><span class="r-pr">#></span> Sandy loam 27 19 15</span> +<span class="r-out co"><span class="r-pr">#></span> Sandy clay loam 28 22 18</span> +<span class="r-out co"><span class="r-pr">#></span> Clay loam 32 28 25</span> +<span class="r-out co"><span class="r-pr">#></span> Loam 31 25 21</span> +<span class="r-out co"><span class="r-pr">#></span> Silt loam 32 26 21</span> +<span class="r-out co"><span class="r-pr">#></span> Silty clay loam 34 30 27</span> +<span class="r-out co"><span class="r-pr">#></span> Silt 31 27 21</span> +<span class="r-out co"><span class="r-pr">#></span> Sandy clay 41 35 31</span> +<span class="r-out co"><span class="r-pr">#></span> Silty clay 44 40 36</span> +<span class="r-out co"><span class="r-pr">#></span> Clay 53 48 43</span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes 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navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -72,23 +17,21 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -98,48 +41,50 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Retrieve a degradation function from the mmkin namespace</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/nlme.mmkin.R'><code>R/nlme.mmkin.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nlme.mmkin.R" class="external-link"><code>R/nlme.mmkin.R</code></a></small> <div class="hidden name"><code>get_deg_func.Rd</code></div> </div> @@ -147,39 +92,39 @@ <p>Retrieve a degradation function from the mmkin namespace</p> </div> - <pre class="usage"><span class='fu'>get_deg_func</span><span class='op'>(</span><span class='op'>)</span></pre> - + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">get_deg_func</span><span class="op">(</span><span class="op">)</span></span></code></pre></div> + </div> - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> + <div id="value"> + <h2>Value</h2> + - <p>A function that was likely previously assigned from within +<p>A function that was likely previously assigned from within nlme.mmkin</p> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/hierarchical_kinetics.html b/docs/dev/reference/hierarchical_kinetics.html new file mode 100644 index 00000000..bedb8753 --- /dev/null +++ b/docs/dev/reference/hierarchical_kinetics.html @@ -0,0 +1,154 @@ +<!DOCTYPE html> +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Hierarchical kinetics template — hierarchical_kinetics • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Hierarchical kinetics template — hierarchical_kinetics"><meta property="og:description" content="R markdown format for setting up hierarchical kinetics based on a template +provided with the mkin package."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> +<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> +<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + + + <div class="container template-reference-topic"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <div class="container"> + <div class="navbar-header"> + <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> + <span class="sr-only">Toggle navigation</span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + </button> + <span class="navbar-brand"> + <a class="navbar-link" href="../index.html">mkin</a> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> + </span> + </div> + + <div id="navbar" class="navbar-collapse collapse"> + <ul class="nav navbar-nav"><li> + <a href="../reference/index.html">Functions and data</a> +</li> +<li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> + Articles + + <span class="caret"></span> + </a> + <ul class="dropdown-menu" role="menu"><li> + <a href="../articles/mkin.html">Introduction to mkin</a> + </li> + <li> + <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> + </li> + <li> + <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> + </li> + <li> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + </li> + <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> + <li> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> +<li> + <a href="../news/index.html">News</a> +</li> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> + <span class="fab fa-github fa-lg"></span> + + </a> +</li> + </ul></div><!--/.nav-collapse --> + </div><!--/.container --> +</div><!--/.navbar --> + + + + </header><div class="row"> + <div class="col-md-9 contents"> + <div class="page-header"> + <h1>Hierarchical kinetics template</h1> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/hierarchical_kinetics.R" class="external-link"><code>R/hierarchical_kinetics.R</code></a></small> + <div class="hidden name"><code>hierarchical_kinetics.Rd</code></div> + </div> + + <div class="ref-description"> + <p>R markdown format for setting up hierarchical kinetics based on a template +provided with the mkin package.</p> + </div> + + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">hierarchical_kinetics</span><span class="op">(</span><span class="va">...</span>, keep_tex <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>...</dt> +<dd><p>Arguments to <code><a href="https://pkgs.rstudio.com/rmarkdown/reference/pdf_document.html" class="external-link">rmarkdown::pdf_document</a></code></p></dd> + + +<dt>keep_tex</dt> +<dd><p>Keep the intermediate tex file used in the conversion to PDF</p></dd> + +</dl></div> + <div id="value"> + <h2>Value</h2> + + +<p>R Markdown output format to pass to +<code><a href="https://pkgs.rstudio.com/rmarkdown/reference/render.html" class="external-link">render</a></code></p> + + + </div> + + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/rstudio/rmarkdown" class="external-link">rmarkdown</a></span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://pkgs.rstudio.com/rmarkdown/reference/draft.html" class="external-link">draft</a></span><span class="op">(</span><span class="st">"example_analysis.rmd"</span>, template <span class="op">=</span> <span class="st">"hierarchical_kinetics"</span>, package <span class="op">=</span> <span class="st">"mkin"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> +</code></pre></div> + </div> + </div> + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> +</div> + + + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> +</div> + + </footer></div> + + + + + + + </body></html> + diff --git a/docs/dev/reference/illparms.html b/docs/dev/reference/illparms.html index c0de4115..9c498e1c 100644 --- a/docs/dev/reference/illparms.html +++ b/docs/dev/reference/illparms.html @@ -21,7 +21,7 @@ without parameter transformations is used."><meta name="robots" content="noindex </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -63,7 +63,10 @@ without parameter transformations is used."><meta name="robots" content="noindex <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -113,7 +116,14 @@ without parameter transformations is used.</p> <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span> <span></span> <span><span class="co"># S3 method for saem.mmkin</span></span> -<span><span class="fu">illparms</span><span class="op">(</span><span class="va">object</span>, conf.level <span class="op">=</span> <span class="fl">0.95</span>, random <span class="op">=</span> <span class="cn">TRUE</span>, errmod <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span> +<span><span class="fu">illparms</span><span class="op">(</span></span> +<span> <span class="va">object</span>,</span> +<span> conf.level <span class="op">=</span> <span class="fl">0.95</span>,</span> +<span> random <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> errmod <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> slopes <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span> <span></span> <span><span class="co"># S3 method for illparms.saem.mmkin</span></span> <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span> @@ -151,6 +161,12 @@ without parameter transformations is used.</p> <dd><p>For hierarchical fits, should error model parameters be tested?</p></dd> + +<dt>slopes</dt> +<dd><p>For hierarchical <a href="saem.html">saem</a> fits using saemix as backend, +should slope parameters in the covariate model(starting with 'beta_') be +tested?</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> diff --git a/docs/dev/reference/ilr.html b/docs/dev/reference/ilr.html index 452647d6..c1396303 100644 --- a/docs/dev/reference/ilr.html +++ b/docs/dev/reference/ilr.html @@ -1,68 +1,13 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - <head> - <meta charset="utf-8"> -<meta http-equiv="X-UA-Compatible" content="IE=edge"> -<meta name="viewport" content="width=device-width, initial-scale=1.0"> - -<title>Function to perform isometric log-ratio transformation — ilr • mkin</title> - - -<!-- jquery --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script> -<!-- Bootstrap --> - -<link rel="stylesheet" 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data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -73,23 +18,21 @@ transformations." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -99,48 +42,50 @@ transformations." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Function to perform isometric log-ratio transformation</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/ilr.R'><code>R/ilr.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/ilr.R" class="external-link"><code>R/ilr.R</code></a></small> <div class="hidden name"><code>ilr.Rd</code></div> </div> @@ -149,86 +94,100 @@ transformations." /> transformations.</p> </div> - <pre class="usage"><span class='fu'>ilr</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span> - -<span class='fu'>invilr</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span></pre> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">ilr</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span> +<span></span> +<span><span class="fu">invilr</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span></code></pre></div> + </div> - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>x</th> - <td><p>A numeric vector. Naturally, the forward transformation is only -sensible for vectors with all elements being greater than zero.</p></td> - </tr> - </table> + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>x</dt> +<dd><p>A numeric vector. Naturally, the forward transformation is only +sensible for vectors with all elements being greater than zero.</p></dd> - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> +</dl></div> + <div id="value"> + <h2>Value</h2> + - <p>The result of the forward or backward transformation. The returned +<p>The result of the forward or backward transformation. The returned components always sum to 1 for the case of the inverse log-ratio transformation.</p> - <h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2> - + </div> + <div id="references"> + <h2>References</h2> <p>Peter Filzmoser, Karel Hron (2008) Outlier Detection for Compositional Data Using Robust Methods. Math Geosci 40 233-248</p> - <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2> - - <div class='dont-index'><p>Another implementation can be found in R package + </div> + <div id="see-also"> + <h2>See also</h2> + <div class="dont-index"><p>Another implementation can be found in R package <code>robCompositions</code>.</p></div> - <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2> - + </div> + <div id="author"> + <h2>Author</h2> <p>René Lehmann and Johannes Ranke</p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> -<span class='co'># Order matters</span> -<span class='fu'>ilr</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0.1</span>, <span class='fl'>1</span>, <span class='fl'>10</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> [1] -1.628174 -2.820079</div><div class='input'><span class='fu'>ilr</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>10</span>, <span class='fl'>1</span>, <span class='fl'>0.1</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> [1] 1.628174 2.820079</div><div class='input'><span class='co'># Equal entries give ilr transformations with zeros as elements</span> -<span class='fu'>ilr</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>3</span>, <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> [1] 0 0</div><div class='input'><span class='co'># Almost equal entries give small numbers</span> -<span class='fu'>ilr</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0.3</span>, <span class='fl'>0.4</span>, <span class='fl'>0.3</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> [1] -0.2034219 0.1174457</div><div class='input'><span class='co'># Only the ratio between the numbers counts, not their sum</span> -<span class='fu'>invilr</span><span class='op'>(</span><span class='fu'>ilr</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0.7</span>, <span class='fl'>0.29</span>, <span class='fl'>0.01</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> [1] 0.70 0.29 0.01</div><div class='input'><span class='fu'>invilr</span><span class='op'>(</span><span class='fu'>ilr</span><span class='op'>(</span><span class='fl'>2.1</span> <span class='op'>*</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0.7</span>, <span class='fl'>0.29</span>, <span class='fl'>0.01</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> [1] 0.70 0.29 0.01</div><div class='input'><span class='co'># Inverse transformation of larger numbers gives unequal elements</span> -<span class='fu'>invilr</span><span class='op'>(</span><span class='op'>-</span><span class='fl'>10</span><span class='op'>)</span> -</div><div class='output co'>#> [1] 7.213536e-07 9.999993e-01</div><div class='input'><span class='fu'>invilr</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>10</span>, <span class='fl'>0</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> [1] 7.207415e-07 9.991507e-01 8.486044e-04</div><div class='input'><span class='co'># The sum of the elements of the inverse ilr is 1</span> -<span class='fu'><a href='https://rdrr.io/r/base/sum.html'>sum</a></span><span class='op'>(</span><span class='fu'>invilr</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='op'>-</span><span class='fl'>10</span>, <span class='fl'>0</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> [1] 1</div><div class='input'><span class='co'># This is why we do not need all elements of the inverse transformation to go back:</span> -<span class='va'>a</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0.1</span>, <span class='fl'>0.3</span>, <span class='fl'>0.5</span><span class='op'>)</span> -<span class='va'>b</span> <span class='op'><-</span> <span class='fu'>invilr</span><span class='op'>(</span><span class='va'>a</span><span class='op'>)</span> -<span class='fu'><a href='https://rdrr.io/r/base/length.html'>length</a></span><span class='op'>(</span><span class='va'>b</span><span class='op'>)</span> <span class='co'># Four elements</span> -</div><div class='output co'>#> [1] 4</div><div class='input'><span class='fu'>ilr</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='va'>b</span><span class='op'>[</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>3</span><span class='op'>]</span>, <span class='fl'>1</span> <span class='op'>-</span> <span class='fu'><a href='https://rdrr.io/r/base/sum.html'>sum</a></span><span class='op'>(</span><span class='va'>b</span><span class='op'>[</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>3</span><span class='op'>]</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span> <span class='co'># Gives c(0.1, 0.3, 0.5)</span> -</div><div class='output co'>#> [1] 0.1 0.3 0.5</div><div class='input'> -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Order matters</span></span></span> +<span class="r-in"><span><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.1</span>, <span class="fl">1</span>, <span class="fl">10</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] -1.628174 -2.820079</span> +<span class="r-in"><span><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">1</span>, <span class="fl">0.1</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] 1.628174 2.820079</span> +<span class="r-in"><span><span class="co"># Equal entries give ilr transformations with zeros as elements</span></span></span> +<span class="r-in"><span><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] 0 0</span> +<span class="r-in"><span><span class="co"># Almost equal entries give small numbers</span></span></span> +<span class="r-in"><span><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.3</span>, <span class="fl">0.4</span>, <span class="fl">0.3</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] -0.2034219 0.1174457</span> +<span class="r-in"><span><span class="co"># Only the ratio between the numbers counts, not their sum</span></span></span> +<span class="r-in"><span><span class="fu">invilr</span><span class="op">(</span><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.7</span>, <span class="fl">0.29</span>, <span class="fl">0.01</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] 0.70 0.29 0.01</span> +<span class="r-in"><span><span class="fu">invilr</span><span class="op">(</span><span class="fu">ilr</span><span class="op">(</span><span class="fl">2.1</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.7</span>, <span class="fl">0.29</span>, <span class="fl">0.01</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] 0.70 0.29 0.01</span> +<span class="r-in"><span><span class="co"># Inverse transformation of larger numbers gives unequal elements</span></span></span> +<span class="r-in"><span><span class="fu">invilr</span><span class="op">(</span><span class="op">-</span><span class="fl">10</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] 7.213536e-07 9.999993e-01</span> +<span class="r-in"><span><span class="fu">invilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">10</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] 7.207415e-07 9.991507e-01 8.486044e-04</span> +<span class="r-in"><span><span class="co"># The sum of the elements of the inverse ilr is 1</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="fu">invilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">10</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] 1</span> +<span class="r-in"><span><span class="co"># This is why we do not need all elements of the inverse transformation to go back:</span></span></span> +<span class="r-in"><span><span class="va">a</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.1</span>, <span class="fl">0.3</span>, <span class="fl">0.5</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">b</span> <span class="op"><-</span> <span class="fu">invilr</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">b</span><span class="op">)</span> <span class="co"># Four elements</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] 4</span> +<span class="r-in"><span><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">b</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">]</span>, <span class="fl">1</span> <span class="op">-</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">b</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">]</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="co"># Gives c(0.1, 0.3, 0.5)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] 0.1 0.3 0.5</span> +<span class="r-in"><span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/index.html b/docs/dev/reference/index.html index 5a0ec596..b6d2db20 100644 --- a/docs/dev/reference/index.html +++ b/docs/dev/reference/index.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -195,6 +212,10 @@ of an mmkin object</p></td> <p class="section-desc"></p><p>Create and work with nonlinear hierarchical models</p> </th> </tr></tbody><tbody><tr><td> + <p><code><a href="hierarchical_kinetics.html">hierarchical_kinetics()</a></code> </p> + </td> + <td><p>Hierarchical kinetics template</p></td> + </tr><tr><td> <p><code><a href="read_spreadsheet.html">read_spreadsheet()</a></code> </p> </td> <td><p>Read datasets and relevant meta information from a spreadsheet file</p></td> @@ -268,9 +289,9 @@ degradation models and one or more error models</p></td> <p class="section-desc"></p> </th> </tr></tbody><tbody><tr><td> - <p><code><a href="focus_soil_moisture.html">focus_soil_moisture</a></code> </p> + <p><code><a href="ds_mixed.html">ds_mixed</a></code> <code><a href="ds_mixed.html">ds_sfo</a></code> <code><a href="ds_mixed.html">ds_fomc</a></code> <code><a href="ds_mixed.html">ds_dfop</a></code> <code><a href="ds_mixed.html">ds_hs</a></code> <code><a href="ds_mixed.html">ds_dfop_sfo</a></code> </p> </td> - <td><p>FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar</p></td> + <td><p>Synthetic data for hierarchical kinetic degradation models</p></td> </tr><tr><td> <p><code><a href="D24_2014.html">D24_2014</a></code> </p> </td> @@ -328,6 +349,10 @@ degradation models and one or more error models</p></td> </td> <td><p>Three experimental datasets from two water sediment systems and one soil</p></td> </tr><tr><td> + <p><code><a href="focus_soil_moisture.html">focus_soil_moisture</a></code> </p> + </td> + <td><p>FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar</p></td> + </tr><tr><td> <p><code><a href="mkinds.html">print(<i><mkinds></i>)</a></code> </p> </td> <td><p>A dataset class for mkin</p></td> @@ -352,9 +377,9 @@ degradation models and one or more error models</p></td> <p class="section-desc"></p> </th> </tr></tbody><tbody><tr><td> - <p><code><a href="tex_listing.html">tex_listing()</a></code> </p> + <p><code><a href="summary_listing.html">summary_listing()</a></code> <code><a href="summary_listing.html">tex_listing()</a></code> <code><a href="summary_listing.html">html_listing()</a></code> </p> </td> - <td><p>Wrap the output of a summary function in tex listing environment</p></td> + <td><p>Display the output of a summary function according to the output format</p></td> </tr><tr><td> <p><code><a href="f_time_norm_focus.html">f_time_norm_focus()</a></code> </p> </td> @@ -489,7 +514,7 @@ kinetic models fitted with mkinfit</p></td> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/dev/reference/intervals.saem.mmkin.html b/docs/dev/reference/intervals.saem.mmkin.html index ee714ad0..e67d8da0 100644 --- a/docs/dev/reference/intervals.saem.mmkin.html +++ b/docs/dev/reference/intervals.saem.mmkin.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.2</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -44,19 +44,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/llhist.html b/docs/dev/reference/llhist.html index 314cb923..27e55455 100644 --- a/docs/dev/reference/llhist.html +++ b/docs/dev/reference/llhist.html @@ -18,7 +18,7 @@ original fit is shown as a red vertical line."><meta name="robots" content="noin </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -60,7 +60,10 @@ original fit is shown as a red vertical line."><meta name="robots" content="noin <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/loftest-1.png b/docs/dev/reference/loftest-1.png Binary files differindex d6006ecc..f1dc5fa7 100644 --- a/docs/dev/reference/loftest-1.png +++ b/docs/dev/reference/loftest-1.png diff --git a/docs/dev/reference/loftest-2.png b/docs/dev/reference/loftest-2.png Binary files differindex 4d0dc551..3f1015a9 100644 --- a/docs/dev/reference/loftest-2.png +++ b/docs/dev/reference/loftest-2.png diff --git a/docs/dev/reference/loftest-3.png b/docs/dev/reference/loftest-3.png Binary files differindex 6afd084b..d897c363 100644 --- 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It fits an anova model to the data contained in the object and compares the likelihoods using the likelihood ratio test -lrtest.default from the lmtest package." /> - - -<meta name="robots" content="noindex"> - -<!-- mathjax --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script> - -<!--[if lt IE 9]> +lrtest.default from the lmtest package."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> - 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<ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -101,48 +44,50 @@ lrtest.default from the lmtest package." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Lack-of-fit test for models fitted to data with replicates</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/loftest.R'><code>R/loftest.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/loftest.R" class="external-link"><code>R/loftest.R</code></a></small> <div class="hidden name"><code>loftest.Rd</code></div> </div> @@ -150,216 +95,231 @@ lrtest.default from the lmtest package." /> <p>This is a generic function with a method currently only defined for mkinfit objects. It fits an anova model to the data contained in the object and compares the likelihoods using the likelihood ratio test -<code><a href='https://rdrr.io/pkg/lmtest/man/lrtest.html'>lrtest.default</a></code> from the lmtest package.</p> +<code><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest.default</a></code> from the lmtest package.</p> </div> - <pre class="usage"><span class='fu'>loftest</span><span class='op'>(</span><span class='va'>object</span>, <span class='va'>...</span><span class='op'>)</span> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">loftest</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mkinfit</span></span> +<span><span class="fu">loftest</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> + </div> -<span class='co'># S3 method for mkinfit</span> -<span class='fu'>loftest</span><span class='op'>(</span><span class='va'>object</span>, <span class='va'>...</span><span class='op'>)</span></pre> + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>object</dt> +<dd><p>A model object with a defined loftest method</p></dd> - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>object</th> - <td><p>A model object with a defined loftest method</p></td> - </tr> - <tr> - <th>...</th> - <td><p>Not used</p></td> - </tr> - </table> - <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2> +<dt>...</dt> +<dd><p>Not used</p></dd> +</dl></div> + <div id="details"> + <h2>Details</h2> <p>The anova model is interpreted as the simplest form of an mkinfit model, assuming only a constant variance about the means, but not enforcing any structure of the means, so we have one model parameter for every mean of replicate samples.</p> - <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2> - - <div class='dont-index'><p>lrtest</p></div> + </div> + <div id="see-also"> + <h2>See also</h2> + <div class="dont-index"><p>lrtest</p></div> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'><span class='co'># \dontrun{</span> -<span class='va'>test_data</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span><span class='op'>(</span><span class='va'>synthetic_data_for_UBA_2014</span><span class='op'>[[</span><span class='fl'>12</span><span class='op'>]</span><span class='op'>]</span><span class='op'>$</span><span class='va'>data</span>, <span class='va'>name</span> <span class='op'>==</span> <span class='st'>"parent"</span><span class='op'>)</span> -<span class='va'>sfo_fit</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='va'>test_data</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='fu'><a href='plot.mkinfit.html'>plot_res</a></span><span class='op'>(</span><span class='va'>sfo_fit</span><span class='op'>)</span> <span class='co'># We see a clear pattern in the residuals</span> -</div><div class='img'><img src='loftest-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'>loftest</span><span class='op'>(</span><span class='va'>sfo_fit</span><span class='op'>)</span> <span class='co'># We have a clear lack of fit</span> -</div><div class='output co'>#> Likelihood ratio test -#> -#> Model 1: ANOVA with error model const -#> Model 2: SFO with error model const -#> #Df LogLik Df Chisq Pr(>Chisq) -#> 1 10 -40.710 -#> 2 3 -63.954 -7 46.487 7.027e-08 *** -#> --- -#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</div><div class='input'><span class='co'>#</span> -<span class='co'># We try a different model (the one that was used to generate the data)</span> -<span class='va'>dfop_fit</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='st'>"DFOP"</span>, <span class='va'>test_data</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='fu'><a href='plot.mkinfit.html'>plot_res</a></span><span class='op'>(</span><span class='va'>dfop_fit</span><span class='op'>)</span> <span class='co'># We don't see systematic deviations, but heteroscedastic residuals</span> -</div><div class='img'><img src='loftest-2.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># therefore we should consider adapting the error model, although we have</span> -<span class='fu'>loftest</span><span class='op'>(</span><span class='va'>dfop_fit</span><span class='op'>)</span> <span class='co'># no lack of fit</span> -</div><div class='output co'>#> Likelihood ratio test -#> -#> Model 1: ANOVA with error model const -#> Model 2: DFOP with error model const -#> #Df LogLik Df Chisq Pr(>Chisq) -#> 1 10 -40.710 -#> 2 5 -42.453 -5 3.485 0.6257</div><div class='input'><span class='co'>#</span> -<span class='co'># This is the anova model used internally for the comparison</span> -<span class='va'>test_data_anova</span> <span class='op'><-</span> <span class='va'>test_data</span> -<span class='va'>test_data_anova</span><span class='op'>$</span><span class='va'>time</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/factor.html'>as.factor</a></span><span class='op'>(</span><span class='va'>test_data_anova</span><span class='op'>$</span><span class='va'>time</span><span class='op'>)</span> -<span class='va'>anova_fit</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/lm.html'>lm</a></span><span class='op'>(</span><span class='va'>value</span> <span class='op'>~</span> <span class='va'>time</span>, data <span class='op'>=</span> <span class='va'>test_data_anova</span><span class='op'>)</span> -<span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>anova_fit</span><span class='op'>)</span> -</div><div class='output co'>#> -#> Call: -#> lm(formula = value ~ time, data = test_data_anova) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -6.1000 -0.5625 0.0000 0.5625 6.1000 -#> -#> Coefficients: -#> Estimate Std. Error t value Pr(>|t|) -#> (Intercept) 103.150 2.323 44.409 7.44e-12 *** -#> time1 -19.950 3.285 -6.073 0.000185 *** -#> time3 -50.800 3.285 -15.465 8.65e-08 *** -#> time7 -68.500 3.285 -20.854 6.28e-09 *** -#> time14 -79.750 3.285 -24.278 1.63e-09 *** -#> time28 -86.000 3.285 -26.181 8.35e-10 *** -#> time60 -94.900 3.285 -28.891 3.48e-10 *** -#> time90 -98.500 3.285 -29.986 2.49e-10 *** -#> time120 -100.450 3.285 -30.580 2.09e-10 *** -#> --- -#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 -#> -#> Residual standard error: 3.285 on 9 degrees of freedom -#> Multiple R-squared: 0.9953, Adjusted R-squared: 0.9912 -#> F-statistic: 240.5 on 8 and 9 DF, p-value: 1.417e-09 -#> </div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/logLik.html'>logLik</a></span><span class='op'>(</span><span class='va'>anova_fit</span><span class='op'>)</span> <span class='co'># We get the same likelihood and degrees of freedom</span> -</div><div class='output co'>#> 'log Lik.' -40.71015 (df=10)</div><div class='input'><span class='co'>#</span> -<span class='va'>test_data_2</span> <span class='op'><-</span> <span class='va'>synthetic_data_for_UBA_2014</span><span class='op'>[[</span><span class='fl'>12</span><span class='op'>]</span><span class='op'>]</span><span class='op'>$</span><span class='va'>data</span> -<span class='va'>m_synth_SFO_lin</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='st'>"M1"</span><span class='op'>)</span>, - M1 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='st'>"M2"</span><span class='op'>)</span>, - M2 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span>, use_of_ff <span class='op'>=</span> <span class='st'>"max"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'><span class='va'>sfo_lin_fit</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>m_synth_SFO_lin</span>, <span class='va'>test_data_2</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='fu'><a href='plot.mkinfit.html'>plot_res</a></span><span class='op'>(</span><span class='va'>sfo_lin_fit</span><span class='op'>)</span> <span class='co'># not a good model, we try parallel formation</span> -</div><div class='img'><img src='loftest-3.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'>loftest</span><span class='op'>(</span><span class='va'>sfo_lin_fit</span><span class='op'>)</span> -</div><div class='output co'>#> Likelihood ratio test -#> -#> Model 1: ANOVA with error model const -#> Model 2: m_synth_SFO_lin with error model const and fixed parameter(s) M1_0, M2_0 -#> #Df LogLik Df Chisq Pr(>Chisq) -#> 1 28 -93.606 -#> 2 7 -171.927 -21 156.64 < 2.2e-16 *** -#> --- -#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</div><div class='input'><span class='co'>#</span> -<span class='va'>m_synth_SFO_par</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"M1"</span>, <span class='st'>"M2"</span><span class='op'>)</span><span class='op'>)</span>, - M1 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span>, - M2 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span>, use_of_ff <span class='op'>=</span> <span class='st'>"max"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'><span class='va'>sfo_par_fit</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>m_synth_SFO_par</span>, <span class='va'>test_data_2</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='fu'><a href='plot.mkinfit.html'>plot_res</a></span><span class='op'>(</span><span class='va'>sfo_par_fit</span><span class='op'>)</span> <span class='co'># much better for metabolites</span> -</div><div class='img'><img src='loftest-4.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'>loftest</span><span class='op'>(</span><span class='va'>sfo_par_fit</span><span class='op'>)</span> -</div><div class='output co'>#> Likelihood ratio test -#> -#> Model 1: ANOVA with error model const -#> Model 2: m_synth_SFO_par with error model const and fixed parameter(s) M1_0, M2_0 -#> #Df LogLik Df Chisq Pr(>Chisq) -#> 1 28 -93.606 -#> 2 7 -156.331 -21 125.45 < 2.2e-16 *** -#> --- -#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</div><div class='input'><span class='co'>#</span> -<span class='va'>m_synth_DFOP_par</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"DFOP"</span>, to <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"M1"</span>, <span class='st'>"M2"</span><span class='op'>)</span><span class='op'>)</span>, - M1 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span>, - M2 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span>, use_of_ff <span class='op'>=</span> <span class='st'>"max"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'><span class='va'>dfop_par_fit</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>m_synth_DFOP_par</span>, <span class='va'>test_data_2</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='fu'><a href='plot.mkinfit.html'>plot_res</a></span><span class='op'>(</span><span class='va'>dfop_par_fit</span><span class='op'>)</span> <span class='co'># No visual lack of fit</span> -</div><div class='img'><img src='loftest-5.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'>loftest</span><span class='op'>(</span><span class='va'>dfop_par_fit</span><span class='op'>)</span> <span class='co'># no lack of fit found by the test</span> -</div><div class='output co'>#> Likelihood ratio test -#> -#> Model 1: ANOVA with error model const -#> Model 2: m_synth_DFOP_par with error model const and fixed parameter(s) M1_0, M2_0 -#> #Df LogLik Df Chisq Pr(>Chisq) -#> 1 28 -93.606 -#> 2 9 -102.763 -19 18.313 0.5016</div><div class='input'><span class='co'>#</span> -<span class='co'># The anova model used for comparison in the case of transformation products</span> -<span class='va'>test_data_anova_2</span> <span class='op'><-</span> <span class='va'>dfop_par_fit</span><span class='op'>$</span><span class='va'>data</span> -<span class='va'>test_data_anova_2</span><span class='op'>$</span><span class='va'>variable</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/factor.html'>as.factor</a></span><span class='op'>(</span><span class='va'>test_data_anova_2</span><span class='op'>$</span><span class='va'>variable</span><span class='op'>)</span> -<span class='va'>test_data_anova_2</span><span class='op'>$</span><span class='va'>time</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/factor.html'>as.factor</a></span><span class='op'>(</span><span class='va'>test_data_anova_2</span><span class='op'>$</span><span class='va'>time</span><span class='op'>)</span> -<span class='va'>anova_fit_2</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/lm.html'>lm</a></span><span class='op'>(</span><span class='va'>observed</span> <span class='op'>~</span> <span class='va'>time</span><span class='op'>:</span><span class='va'>variable</span> <span class='op'>-</span> <span class='fl'>1</span>, data <span class='op'>=</span> <span class='va'>test_data_anova_2</span><span class='op'>)</span> -<span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>anova_fit_2</span><span class='op'>)</span> -</div><div class='output co'>#> -#> Call: -#> lm(formula = observed ~ time:variable - 1, data = test_data_anova_2) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -6.1000 -0.5875 0.0000 0.5875 6.1000 -#> -#> Coefficients: (2 not defined because of singularities) -#> Estimate Std. Error t value Pr(>|t|) -#> time0:variableparent 103.150 1.573 65.562 < 2e-16 *** -#> time1:variableparent 83.200 1.573 52.882 < 2e-16 *** -#> time3:variableparent 52.350 1.573 33.274 < 2e-16 *** -#> time7:variableparent 34.650 1.573 22.024 < 2e-16 *** -#> time14:variableparent 23.400 1.573 14.873 6.35e-14 *** -#> time28:variableparent 17.150 1.573 10.901 5.47e-11 *** -#> time60:variableparent 8.250 1.573 5.244 1.99e-05 *** -#> time90:variableparent 4.650 1.573 2.956 0.006717 ** -#> time120:variableparent 2.700 1.573 1.716 0.098507 . -#> time0:variableM1 NA NA NA NA -#> time1:variableM1 11.850 1.573 7.532 6.93e-08 *** -#> time3:variableM1 22.700 1.573 14.428 1.26e-13 *** -#> time7:variableM1 33.050 1.573 21.007 < 2e-16 *** -#> time14:variableM1 31.250 1.573 19.863 < 2e-16 *** -#> time28:variableM1 18.900 1.573 12.013 7.02e-12 *** -#> time60:variableM1 7.550 1.573 4.799 6.28e-05 *** -#> time90:variableM1 3.850 1.573 2.447 0.021772 * -#> time120:variableM1 2.050 1.573 1.303 0.204454 -#> time0:variableM2 NA NA NA NA -#> time1:variableM2 6.700 1.573 4.259 0.000254 *** -#> time3:variableM2 16.750 1.573 10.646 8.93e-11 *** -#> time7:variableM2 25.800 1.573 16.399 6.89e-15 *** -#> time14:variableM2 28.600 1.573 18.178 6.35e-16 *** -#> time28:variableM2 25.400 1.573 16.144 9.85e-15 *** -#> time60:variableM2 21.600 1.573 13.729 3.81e-13 *** -#> time90:variableM2 17.800 1.573 11.314 2.51e-11 *** -#> time120:variableM2 14.100 1.573 8.962 2.79e-09 *** -#> --- -#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 -#> -#> Residual standard error: 2.225 on 25 degrees of freedom -#> Multiple R-squared: 0.9979, Adjusted R-squared: 0.9957 -#> F-statistic: 469.2 on 25 and 25 DF, p-value: < 2.2e-16 -#> </div><div class='input'><span class='co'># }</span> -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="va">test_data</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"parent"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">sfo_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">test_data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">sfo_fit</span><span class="op">)</span> <span class="co"># We see a clear pattern in the residuals</span></span></span> +<span class="r-plt img"><img src="loftest-1.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="fu">loftest</span><span class="op">(</span><span class="va">sfo_fit</span><span class="op">)</span> <span class="co"># We have a clear lack of fit</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Model 1: ANOVA with error model const</span> +<span class="r-out co"><span class="r-pr">#></span> Model 2: SFO with error model const</span> +<span class="r-out co"><span class="r-pr">#></span> #Df LogLik Df Chisq Pr(>Chisq) </span> +<span class="r-out co"><span class="r-pr">#></span> 1 10 -40.710 </span> +<span class="r-out co"><span class="r-pr">#></span> 2 3 -63.954 -7 46.487 7.027e-08 ***</span> +<span class="r-out co"><span class="r-pr">#></span> ---</span> +<span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> +<span class="r-in"><span><span class="co">#</span></span></span> +<span class="r-in"><span><span class="co"># We try a different model (the one that was used to generate the data)</span></span></span> +<span class="r-in"><span><span class="va">dfop_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">test_data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">dfop_fit</span><span class="op">)</span> <span class="co"># We don't see systematic deviations, but heteroscedastic residuals</span></span></span> +<span class="r-plt img"><img src="loftest-2.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="co"># therefore we should consider adapting the error model, although we have</span></span></span> +<span class="r-in"><span><span class="fu">loftest</span><span class="op">(</span><span class="va">dfop_fit</span><span class="op">)</span> <span class="co"># no lack of fit</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Model 1: ANOVA with error model const</span> +<span class="r-out co"><span class="r-pr">#></span> Model 2: DFOP with error model const</span> +<span class="r-out co"><span class="r-pr">#></span> #Df LogLik Df Chisq Pr(>Chisq)</span> +<span class="r-out co"><span class="r-pr">#></span> 1 10 -40.710 </span> +<span class="r-out co"><span class="r-pr">#></span> 2 5 -42.453 -5 3.485 0.6257</span> +<span class="r-in"><span><span class="co">#</span></span></span> +<span class="r-in"><span><span class="co"># This is the anova model used internally for the comparison</span></span></span> +<span class="r-in"><span><span class="va">test_data_anova</span> <span class="op"><-</span> <span class="va">test_data</span></span></span> +<span class="r-in"><span><span class="va">test_data_anova</span><span class="op">$</span><span class="va">time</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="va">test_data_anova</span><span class="op">$</span><span class="va">time</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">anova_fit</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/lm.html" class="external-link">lm</a></span><span class="op">(</span><span class="va">value</span> <span class="op">~</span> <span class="va">time</span>, data <span class="op">=</span> <span class="va">test_data_anova</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">anova_fit</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Call:</span> +<span class="r-out co"><span class="r-pr">#></span> lm(formula = value ~ time, data = test_data_anova)</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Residuals:</span> +<span class="r-out co"><span class="r-pr">#></span> Min 1Q Median 3Q Max </span> +<span class="r-out co"><span class="r-pr">#></span> -6.1000 -0.5625 0.0000 0.5625 6.1000 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Coefficients:</span> +<span class="r-out co"><span class="r-pr">#></span> Estimate Std. Error t value Pr(>|t|) </span> +<span class="r-out co"><span class="r-pr">#></span> (Intercept) 103.150 2.323 44.409 7.44e-12 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time1 -19.950 3.285 -6.073 0.000185 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time3 -50.800 3.285 -15.465 8.65e-08 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time7 -68.500 3.285 -20.854 6.28e-09 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time14 -79.750 3.285 -24.278 1.63e-09 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time28 -86.000 3.285 -26.181 8.35e-10 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time60 -94.900 3.285 -28.891 3.48e-10 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time90 -98.500 3.285 -29.986 2.49e-10 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time120 -100.450 3.285 -30.580 2.09e-10 ***</span> +<span class="r-out co"><span class="r-pr">#></span> ---</span> +<span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Residual standard error: 3.285 on 9 degrees of freedom</span> +<span class="r-out co"><span class="r-pr">#></span> Multiple R-squared: 0.9953, Adjusted R-squared: 0.9912 </span> +<span class="r-out co"><span class="r-pr">#></span> F-statistic: 240.5 on 8 and 9 DF, p-value: 1.417e-09</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/logLik.html" class="external-link">logLik</a></span><span class="op">(</span><span class="va">anova_fit</span><span class="op">)</span> <span class="co"># We get the same likelihood and degrees of freedom</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> 'log Lik.' -40.71015 (df=10)</span> +<span class="r-in"><span><span class="co">#</span></span></span> +<span class="r-in"><span><span class="va">test_data_2</span> <span class="op"><-</span> <span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span></span> +<span class="r-in"><span><span class="va">m_synth_SFO_lin</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M2"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span><span class="va">sfo_lin_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_SFO_lin</span>, <span class="va">test_data_2</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">sfo_lin_fit</span><span class="op">)</span> <span class="co"># not a good model, we try parallel formation</span></span></span> +<span class="r-plt img"><img src="loftest-3.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="fu">loftest</span><span class="op">(</span><span class="va">sfo_lin_fit</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Model 1: ANOVA with error model const</span> +<span class="r-out co"><span class="r-pr">#></span> Model 2: m_synth_SFO_lin with error model const and fixed parameter(s) M1_0, M2_0</span> +<span class="r-out co"><span class="r-pr">#></span> #Df LogLik Df Chisq Pr(>Chisq) </span> +<span class="r-out co"><span class="r-pr">#></span> 1 28 -93.606 </span> +<span class="r-out co"><span class="r-pr">#></span> 2 7 -171.927 -21 156.64 < 2.2e-16 ***</span> +<span class="r-out co"><span class="r-pr">#></span> ---</span> +<span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> +<span class="r-in"><span><span class="co">#</span></span></span> +<span class="r-in"><span><span class="va">m_synth_SFO_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span><span class="va">sfo_par_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_SFO_par</span>, <span class="va">test_data_2</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">sfo_par_fit</span><span class="op">)</span> <span class="co"># much better for metabolites</span></span></span> +<span class="r-plt img"><img src="loftest-4.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="fu">loftest</span><span class="op">(</span><span class="va">sfo_par_fit</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Model 1: ANOVA with error model const</span> +<span class="r-out co"><span class="r-pr">#></span> Model 2: m_synth_SFO_par with error model const and fixed parameter(s) M1_0, M2_0</span> +<span class="r-out co"><span class="r-pr">#></span> #Df LogLik Df Chisq Pr(>Chisq) </span> +<span class="r-out co"><span class="r-pr">#></span> 1 28 -93.606 </span> +<span class="r-out co"><span class="r-pr">#></span> 2 7 -156.331 -21 125.45 < 2.2e-16 ***</span> +<span class="r-out co"><span class="r-pr">#></span> ---</span> +<span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> +<span class="r-in"><span><span class="co">#</span></span></span> +<span class="r-in"><span><span class="va">m_synth_DFOP_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span><span class="va">dfop_par_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_DFOP_par</span>, <span class="va">test_data_2</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">dfop_par_fit</span><span class="op">)</span> <span class="co"># No visual lack of fit</span></span></span> +<span class="r-plt img"><img src="loftest-5.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="fu">loftest</span><span class="op">(</span><span class="va">dfop_par_fit</span><span class="op">)</span> <span class="co"># no lack of fit found by the test</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Model 1: ANOVA with error model const</span> +<span class="r-out co"><span class="r-pr">#></span> Model 2: m_synth_DFOP_par with error model const and fixed parameter(s) M1_0, M2_0</span> +<span class="r-out co"><span class="r-pr">#></span> #Df LogLik Df Chisq Pr(>Chisq)</span> +<span class="r-out co"><span class="r-pr">#></span> 1 28 -93.606 </span> +<span class="r-out co"><span class="r-pr">#></span> 2 9 -102.763 -19 18.313 0.5016</span> +<span class="r-in"><span><span class="co">#</span></span></span> +<span class="r-in"><span><span class="co"># The anova model used for comparison in the case of transformation products</span></span></span> +<span class="r-in"><span><span class="va">test_data_anova_2</span> <span class="op"><-</span> <span class="va">dfop_par_fit</span><span class="op">$</span><span class="va">data</span></span></span> +<span class="r-in"><span><span class="va">test_data_anova_2</span><span class="op">$</span><span class="va">variable</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="va">test_data_anova_2</span><span class="op">$</span><span class="va">variable</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">test_data_anova_2</span><span class="op">$</span><span class="va">time</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="va">test_data_anova_2</span><span class="op">$</span><span class="va">time</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">anova_fit_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/lm.html" class="external-link">lm</a></span><span class="op">(</span><span class="va">observed</span> <span class="op">~</span> <span class="va">time</span><span class="op">:</span><span class="va">variable</span> <span class="op">-</span> <span class="fl">1</span>, data <span class="op">=</span> <span class="va">test_data_anova_2</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">anova_fit_2</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Call:</span> +<span class="r-out co"><span class="r-pr">#></span> lm(formula = observed ~ time:variable - 1, data = test_data_anova_2)</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Residuals:</span> +<span class="r-out co"><span class="r-pr">#></span> Min 1Q Median 3Q Max </span> +<span class="r-out co"><span class="r-pr">#></span> -6.1000 -0.5875 0.0000 0.5875 6.1000 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Coefficients: (2 not defined because of singularities)</span> +<span class="r-out co"><span class="r-pr">#></span> Estimate Std. Error t value Pr(>|t|) </span> +<span class="r-out co"><span class="r-pr">#></span> time0:variableparent 103.150 1.573 65.562 < 2e-16 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time1:variableparent 83.200 1.573 52.882 < 2e-16 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time3:variableparent 52.350 1.573 33.274 < 2e-16 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time7:variableparent 34.650 1.573 22.024 < 2e-16 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time14:variableparent 23.400 1.573 14.873 6.35e-14 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time28:variableparent 17.150 1.573 10.901 5.47e-11 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time60:variableparent 8.250 1.573 5.244 1.99e-05 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time90:variableparent 4.650 1.573 2.956 0.006717 ** </span> +<span class="r-out co"><span class="r-pr">#></span> time120:variableparent 2.700 1.573 1.716 0.098507 . </span> +<span class="r-out co"><span class="r-pr">#></span> time0:variableM1 NA NA NA NA </span> +<span class="r-out co"><span class="r-pr">#></span> time1:variableM1 11.850 1.573 7.532 6.93e-08 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time3:variableM1 22.700 1.573 14.428 1.26e-13 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time7:variableM1 33.050 1.573 21.007 < 2e-16 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time14:variableM1 31.250 1.573 19.863 < 2e-16 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time28:variableM1 18.900 1.573 12.013 7.02e-12 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time60:variableM1 7.550 1.573 4.799 6.28e-05 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time90:variableM1 3.850 1.573 2.447 0.021772 * </span> +<span class="r-out co"><span class="r-pr">#></span> time120:variableM1 2.050 1.573 1.303 0.204454 </span> +<span class="r-out co"><span class="r-pr">#></span> time0:variableM2 NA NA NA NA </span> +<span class="r-out co"><span class="r-pr">#></span> time1:variableM2 6.700 1.573 4.259 0.000254 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time3:variableM2 16.750 1.573 10.646 8.93e-11 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time7:variableM2 25.800 1.573 16.399 6.89e-15 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time14:variableM2 28.600 1.573 18.178 6.35e-16 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time28:variableM2 25.400 1.573 16.144 9.85e-15 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time60:variableM2 21.600 1.573 13.729 3.81e-13 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time90:variableM2 17.800 1.573 11.314 2.51e-11 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time120:variableM2 14.100 1.573 8.962 2.79e-09 ***</span> +<span class="r-out co"><span class="r-pr">#></span> ---</span> +<span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Residual standard error: 2.225 on 25 degrees of freedom</span> +<span class="r-out co"><span class="r-pr">#></span> Multiple R-squared: 0.9979, Adjusted R-squared: 0.9957 </span> +<span class="r-out co"><span class="r-pr">#></span> F-statistic: 469.2 on 25 and 25 DF, p-value: < 2.2e-16</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-in"><span><span class="co"># }</span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/logLik.mkinfit.html b/docs/dev/reference/logLik.mkinfit.html index 3e9452c6..e77121d1 100644 --- a/docs/dev/reference/logLik.mkinfit.html +++ b/docs/dev/reference/logLik.mkinfit.html @@ -1,71 +1,16 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - <head> - <meta charset="utf-8"> -<meta http-equiv="X-UA-Compatible" content="IE=edge"> -<meta name="viewport" content="width=device-width, initial-scale=1.0"> - 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<li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -102,136 +45,143 @@ the error model." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Calculated the log-likelihood of a fitted mkinfit object</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/logLik.mkinfit.R'><code>R/logLik.mkinfit.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/logLik.mkinfit.R" class="external-link"><code>R/logLik.mkinfit.R</code></a></small> <div class="hidden name"><code>logLik.mkinfit.Rd</code></div> </div> <div class="ref-description"> <p>This function returns the product of the likelihood densities of each observed value, as calculated as part of the fitting procedure using -<code><a href='https://rdrr.io/r/stats/Normal.html'>dnorm</a></code>, i.e. assuming normal distribution, and with the means +<code><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">dnorm</a></code>, i.e. assuming normal distribution, and with the means predicted by the degradation model, and the standard deviations predicted by the error model.</p> </div> - <pre class="usage"><span class='co'># S3 method for mkinfit</span> -<span class='fu'><a href='https://rdrr.io/r/stats/logLik.html'>logLik</a></span><span class='op'>(</span><span class='va'>object</span>, <span class='va'>...</span><span class='op'>)</span></pre> - - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>object</th> - <td><p>An object of class <code><a href='mkinfit.html'>mkinfit</a></code>.</p></td> - </tr> - <tr> - <th>...</th> - <td><p>For compatibility with the generic method</p></td> - </tr> - </table> - - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> - - <p>An object of class <code><a href='https://rdrr.io/r/stats/logLik.html'>logLik</a></code> with the number of estimated + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/stats/logLik.html" class="external-link">logLik</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>object</dt> +<dd><p>An object of class <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd> + + +<dt>...</dt> +<dd><p>For compatibility with the generic method</p></dd> + +</dl></div> + <div id="value"> + <h2>Value</h2> + + +<p>An object of class <code><a href="https://rdrr.io/r/stats/logLik.html" class="external-link">logLik</a></code> with the number of estimated parameters (degradation model parameters plus variance model parameters) as attribute.</p> - <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2> - + </div> + <div id="details"> + <h2>Details</h2> <p>The total number of estimated parameters returned with the value of the likelihood is calculated as the sum of fitted degradation model parameters and the fitted error model parameters.</p> - <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2> - - <div class='dont-index'><p>Compare the AIC of columns of <code><a href='mmkin.html'>mmkin</a></code> objects using -<code><a href='AIC.mmkin.html'>AIC.mmkin</a></code>.</p></div> - <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2> - + </div> + <div id="see-also"> + <h2>See also</h2> + <div class="dont-index"><p>Compare the AIC of columns of <code><a href="mmkin.html">mmkin</a></code> objects using +<code><a href="AIC.mmkin.html">AIC.mmkin</a></code>.</p></div> + </div> + <div id="author"> + <h2>Author</h2> <p>Johannes Ranke</p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> - <span class='co'># \dontrun{</span> - <span class='va'>sfo_sfo</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span> - parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='st'>"m1"</span><span class='op'>)</span>, - m1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span> - <span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'> <span class='va'>d_t</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span><span class='op'>(</span><span class='va'>FOCUS_2006_D</span>, <span class='va'>value</span> <span class='op'>!=</span> <span class='fl'>0</span><span class='op'>)</span> - <span class='va'>f_nw</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>sfo_sfo</span>, <span class='va'>d_t</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> <span class='co'># no weighting (weights are unity)</span> - <span class='va'>f_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span><span class='op'>(</span><span class='va'>f_nw</span>, error_model <span class='op'>=</span> <span class='st'>"obs"</span><span class='op'>)</span> - <span class='va'>f_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span><span class='op'>(</span><span class='va'>f_nw</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span><span class='op'>)</span> - <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f_nw</span>, <span class='va'>f_obs</span>, <span class='va'>f_tc</span><span class='op'>)</span> -</div><div class='output co'>#> df AIC -#> f_nw 5 204.4486 -#> f_obs 6 205.8727 -#> f_tc 6 141.9656</div><div class='input'> <span class='co'># }</span> - -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span> <span class="va">sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span> <span class="va">d_t</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">f_nw</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">sfo_sfo</span>, <span class="va">d_t</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="co"># no weighting (weights are unity)</span></span></span> +<span class="r-in"><span> <span class="va">f_obs</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_nw</span>, error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">f_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_nw</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_nw</span>, <span class="va">f_obs</span>, <span class="va">f_tc</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> df AIC</span> +<span class="r-out co"><span class="r-pr">#></span> f_nw 5 204.4486</span> +<span class="r-out co"><span class="r-pr">#></span> f_obs 6 205.8727</span> +<span class="r-out co"><span class="r-pr">#></span> f_tc 6 141.9656</span> +<span class="r-in"><span> <span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/logLik.saem.mmkin.html b/docs/dev/reference/logLik.saem.mmkin.html index ebef3b10..fc06b36f 100644 --- a/docs/dev/reference/logLik.saem.mmkin.html +++ b/docs/dev/reference/logLik.saem.mmkin.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> diff --git a/docs/dev/reference/logistic.solution.html b/docs/dev/reference/logistic.solution.html index 1d1880fd..ac4961bc 100644 --- a/docs/dev/reference/logistic.solution.html +++ b/docs/dev/reference/logistic.solution.html @@ -18,7 +18,7 @@ an increasing rate constant, supposedly caused by microbial growth"><meta name=" </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -60,7 +60,10 @@ an increasing rate constant, supposedly caused by microbial growth"><meta name=" <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/lrtest.mkinfit.html b/docs/dev/reference/lrtest.mkinfit.html index f2d8472e..a7198474 100644 --- a/docs/dev/reference/lrtest.mkinfit.html +++ b/docs/dev/reference/lrtest.mkinfit.html @@ -1,71 +1,16 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - <head> - <meta charset="utf-8"> -<meta http-equiv="X-UA-Compatible" content="IE=edge"> -<meta name="viewport" content="width=device-width, initial-scale=1.0"> - -<title>Likelihood ratio test for mkinfit models — lrtest.mkinfit • mkin</title> - - -<!-- jquery --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script> -<!-- Bootstrap --> - 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If two fitted +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Likelihood ratio test for mkinfit models — lrtest.mkinfit • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Likelihood ratio test for mkinfit models — lrtest.mkinfit"><meta property="og:description" content="Compare two mkinfit models based on their likelihood. If two fitted mkinfit objects are given as arguments, it is checked if they have been fitted to the same data. It is the responsibility of the user to make sure that the models are nested, i.e. one of them has less degrees of freedom -and can be expressed by fixing the parameters of the other." /> - - -<meta name="robots" content="noindex"> - -<!-- mathjax --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script> - -<!--[if lt IE 9]> +and can be expressed by fixing the parameters of the other."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> - - - - </head> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -76,23 +21,21 @@ and can be expressed by fixing the parameters of the other." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -102,48 +45,50 @@ and can be expressed by fixing the parameters of the other." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Likelihood ratio test for mkinfit models</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/lrtest.mkinfit.R'><code>R/lrtest.mkinfit.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/lrtest.mkinfit.R" class="external-link"><code>R/lrtest.mkinfit.R</code></a></small> <div class="hidden name"><code>lrtest.mkinfit.Rd</code></div> </div> @@ -155,115 +100,117 @@ that the models are nested, i.e. one of them has less degrees of freedom and can be expressed by fixing the parameters of the other.</p> </div> - <pre class="usage"><span class='co'># S3 method for mkinfit</span> -<span class='fu'><a href='https://rdrr.io/pkg/lmtest/man/lrtest.html'>lrtest</a></span><span class='op'>(</span><span class='va'>object</span>, object_2 <span class='op'>=</span> <span class='cn'>NULL</span>, <span class='va'>...</span><span class='op'>)</span> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span> +<span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">object</span>, object_2 <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>object</dt> +<dd><p>An <code><a href="mkinfit.html">mkinfit</a></code> object, or an <code><a href="mmkin.html">mmkin</a></code> column +object containing two fits to the same data.</p></dd> + -<span class='co'># S3 method for mmkin</span> -<span class='fu'><a href='https://rdrr.io/pkg/lmtest/man/lrtest.html'>lrtest</a></span><span class='op'>(</span><span class='va'>object</span>, <span class='va'>...</span><span class='op'>)</span></pre> +<dt>object_2</dt> +<dd><p>Optionally, another mkinfit object fitted to the same data.</p></dd> - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>object</th> - <td><p>An <code><a href='mkinfit.html'>mkinfit</a></code> object, or an <code><a href='mmkin.html'>mmkin</a></code> column -object containing two fits to the same data.</p></td> - </tr> - <tr> - <th>object_2</th> - <td><p>Optionally, another mkinfit object fitted to the same data.</p></td> - </tr> - <tr> - <th>...</th> - <td><p>Argument to <code><a href='mkinfit.html'>mkinfit</a></code>, passed to -<code><a href='update.mkinfit.html'>update.mkinfit</a></code> for creating the alternative fitted object.</p></td> - </tr> - </table> - <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2> +<dt>...</dt> +<dd><p>Argument to <code><a href="mkinfit.html">mkinfit</a></code>, passed to +<code><a href="update.mkinfit.html">update.mkinfit</a></code> for creating the alternative fitted object.</p></dd> +</dl></div> + <div id="details"> + <h2>Details</h2> <p>Alternatively, an argument to mkinfit can be given which is then passed -to <code><a href='update.mkinfit.html'>update.mkinfit</a></code> to obtain the alternative model.</p> -<p>The comparison is then made by the <code><a href='https://rdrr.io/pkg/lmtest/man/lrtest.html'>lrtest.default</a></code> +to <code><a href="update.mkinfit.html">update.mkinfit</a></code> to obtain the alternative model.</p> +<p>The comparison is then made by the <code><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest.default</a></code> method from the lmtest package. The model with the higher number of fitted parameters (alternative hypothesis) is listed first, then the model with the lower number of fitted parameters (null hypothesis).</p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'><span class='co'># \dontrun{</span> -<span class='va'>test_data</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span><span class='op'>(</span><span class='va'>synthetic_data_for_UBA_2014</span><span class='op'>[[</span><span class='fl'>12</span><span class='op'>]</span><span class='op'>]</span><span class='op'>$</span><span class='va'>data</span>, <span class='va'>name</span> <span class='op'>==</span> <span class='st'>"parent"</span><span class='op'>)</span> -<span class='va'>sfo_fit</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='va'>test_data</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='va'>dfop_fit</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='st'>"DFOP"</span>, <span class='va'>test_data</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='fu'><a href='https://rdrr.io/pkg/lmtest/man/lrtest.html'>lrtest</a></span><span class='op'>(</span><span class='va'>dfop_fit</span>, <span class='va'>sfo_fit</span><span class='op'>)</span> -</div><div class='output co'>#> Likelihood ratio test -#> -#> Model 1: DFOP with error model const -#> Model 2: SFO with error model const -#> #Df LogLik Df Chisq Pr(>Chisq) -#> 1 5 -42.453 -#> 2 3 -63.954 -2 43.002 4.594e-10 *** -#> --- -#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/lmtest/man/lrtest.html'>lrtest</a></span><span class='op'>(</span><span class='va'>sfo_fit</span>, <span class='va'>dfop_fit</span><span class='op'>)</span> -</div><div class='output co'>#> Likelihood ratio test -#> -#> Model 1: DFOP with error model const -#> Model 2: SFO with error model const -#> #Df LogLik Df Chisq Pr(>Chisq) -#> 1 5 -42.453 -#> 2 3 -63.954 -2 43.002 4.594e-10 *** -#> --- -#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</div><div class='input'> -<span class='co'># The following two examples are commented out as they fail during</span> -<span class='co'># generation of the static help pages by pkgdown</span> -<span class='co'>#lrtest(dfop_fit, error_model = "tc")</span> -<span class='co'>#lrtest(dfop_fit, fixed_parms = c(k2 = 0))</span> - -<span class='co'># However, this equivalent syntax also works for static help pages</span> -<span class='fu'><a href='https://rdrr.io/pkg/lmtest/man/lrtest.html'>lrtest</a></span><span class='op'>(</span><span class='va'>dfop_fit</span>, <span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span><span class='op'>(</span><span class='va'>dfop_fit</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> Likelihood ratio test -#> -#> Model 1: DFOP with error model tc -#> Model 2: DFOP with error model const -#> #Df LogLik Df Chisq Pr(>Chisq) -#> 1 6 -34.587 -#> 2 5 -42.453 -1 15.731 7.302e-05 *** -#> --- -#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/lmtest/man/lrtest.html'>lrtest</a></span><span class='op'>(</span><span class='va'>dfop_fit</span>, <span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span><span class='op'>(</span><span class='va'>dfop_fit</span>, fixed_parms <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>k2 <span class='op'>=</span> <span class='fl'>0</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> Likelihood ratio test -#> -#> Model 1: DFOP with error model const -#> Model 2: DFOP with error model const and fixed parameter(s) k2 -#> #Df LogLik Df Chisq Pr(>Chisq) -#> 1 5 -42.453 -#> 2 4 -57.340 -1 29.776 4.851e-08 *** -#> --- -#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</div><div class='input'><span class='co'># }</span> -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="va">test_data</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"parent"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">sfo_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">test_data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">dfop_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">test_data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">dfop_fit</span>, <span class="va">sfo_fit</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Model 1: DFOP with error model const</span> +<span class="r-out co"><span class="r-pr">#></span> Model 2: SFO with error model const</span> +<span class="r-out co"><span class="r-pr">#></span> #Df LogLik Df Chisq Pr(>Chisq) </span> +<span class="r-out co"><span class="r-pr">#></span> 1 5 -42.453 </span> +<span class="r-out co"><span class="r-pr">#></span> 2 3 -63.954 -2 43.002 4.594e-10 ***</span> +<span class="r-out co"><span class="r-pr">#></span> ---</span> +<span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">sfo_fit</span>, <span class="va">dfop_fit</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Model 1: DFOP with error model const</span> +<span class="r-out co"><span class="r-pr">#></span> Model 2: SFO with error model const</span> +<span class="r-out co"><span class="r-pr">#></span> #Df LogLik Df Chisq Pr(>Chisq) </span> +<span class="r-out co"><span class="r-pr">#></span> 1 5 -42.453 </span> +<span class="r-out co"><span class="r-pr">#></span> 2 3 -63.954 -2 43.002 4.594e-10 ***</span> +<span class="r-out co"><span class="r-pr">#></span> ---</span> +<span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># The following two examples are commented out as they fail during</span></span></span> +<span class="r-in"><span><span class="co"># generation of the static help pages by pkgdown</span></span></span> +<span class="r-in"><span><span class="co">#lrtest(dfop_fit, error_model = "tc")</span></span></span> +<span class="r-in"><span><span class="co">#lrtest(dfop_fit, fixed_parms = c(k2 = 0))</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># However, this equivalent syntax also works for static help pages</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">dfop_fit</span>, <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">dfop_fit</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Model 1: DFOP with error model tc</span> +<span class="r-out co"><span class="r-pr">#></span> Model 2: DFOP with error model const</span> +<span class="r-out co"><span class="r-pr">#></span> #Df LogLik Df Chisq Pr(>Chisq) </span> +<span class="r-out co"><span class="r-pr">#></span> 1 6 -34.587 </span> +<span class="r-out co"><span class="r-pr">#></span> 2 5 -42.453 -1 15.731 7.302e-05 ***</span> +<span class="r-out co"><span class="r-pr">#></span> ---</span> +<span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">dfop_fit</span>, <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">dfop_fit</span>, fixed_parms <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Model 1: DFOP with error model const</span> +<span class="r-out co"><span class="r-pr">#></span> Model 2: DFOP with error model const and fixed parameter(s) k2</span> +<span class="r-out co"><span class="r-pr">#></span> #Df LogLik Df Chisq Pr(>Chisq) </span> +<span class="r-out co"><span class="r-pr">#></span> 1 5 -42.453 </span> +<span class="r-out co"><span class="r-pr">#></span> 2 4 -57.340 -1 29.776 4.851e-08 ***</span> +<span class="r-out co"><span class="r-pr">#></span> ---</span> +<span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> +<span class="r-in"><span><span class="co"># }</span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div 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Currently, only calculations for the parent are implemented for the SFO, FOMC, DFOP and HS models, using the analytical formulas given in the PEC -soil section of the FOCUS guidance." /> - - -<meta name="robots" content="noindex"> - -<!-- mathjax --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script> - -<!--[if lt IE 9]> +soil section of the FOCUS guidance."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> - 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<ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -104,171 +47,176 @@ soil section of the FOCUS guidance." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Function to calculate maximum time weighted average concentrations from kinetic models fitted with mkinfit</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/max_twa_parent.R'><code>R/max_twa_parent.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/max_twa_parent.R" class="external-link"><code>R/max_twa_parent.R</code></a></small> <div class="hidden name"><code>max_twa_parent.Rd</code></div> </div> <div class="ref-description"> <p>This function calculates maximum moving window time weighted average -concentrations (TWAs) for kinetic models fitted with <code><a href='mkinfit.html'>mkinfit</a></code>. +concentrations (TWAs) for kinetic models fitted with <code><a href="mkinfit.html">mkinfit</a></code>. Currently, only calculations for the parent are implemented for the SFO, FOMC, DFOP and HS models, using the analytical formulas given in the PEC soil section of the FOCUS guidance.</p> </div> - <pre class="usage"><span class='fu'>max_twa_parent</span><span class='op'>(</span><span class='va'>fit</span>, <span class='va'>windows</span><span class='op'>)</span> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">max_twa_parent</span><span class="op">(</span><span class="va">fit</span>, <span class="va">windows</span><span class="op">)</span></span> +<span></span> +<span><span class="fu">max_twa_sfo</span><span class="op">(</span>M0 <span class="op">=</span> <span class="fl">1</span>, <span class="va">k</span>, <span class="va">t</span><span class="op">)</span></span> +<span></span> +<span><span class="fu">max_twa_fomc</span><span class="op">(</span>M0 <span class="op">=</span> <span class="fl">1</span>, <span class="va">alpha</span>, <span class="va">beta</span>, <span class="va">t</span><span class="op">)</span></span> +<span></span> +<span><span class="fu">max_twa_dfop</span><span class="op">(</span>M0 <span class="op">=</span> <span class="fl">1</span>, <span class="va">k1</span>, <span class="va">k2</span>, <span class="va">g</span>, <span class="va">t</span><span class="op">)</span></span> +<span></span> +<span><span class="fu">max_twa_hs</span><span class="op">(</span>M0 <span class="op">=</span> <span class="fl">1</span>, <span class="va">k1</span>, <span class="va">k2</span>, <span class="va">tb</span>, <span class="va">t</span><span class="op">)</span></span></code></pre></div> + </div> -<span class='fu'>max_twa_sfo</span><span class='op'>(</span>M0 <span class='op'>=</span> <span class='fl'>1</span>, <span class='va'>k</span>, <span class='va'>t</span><span class='op'>)</span> + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>fit</dt> +<dd><p>An object of class <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd> -<span class='fu'>max_twa_fomc</span><span class='op'>(</span>M0 <span class='op'>=</span> <span class='fl'>1</span>, <span class='va'>alpha</span>, <span class='va'>beta</span>, <span class='va'>t</span><span class='op'>)</span> -<span class='fu'>max_twa_dfop</span><span class='op'>(</span>M0 <span class='op'>=</span> <span class='fl'>1</span>, <span class='va'>k1</span>, <span class='va'>k2</span>, <span class='va'>g</span>, <span class='va'>t</span><span class='op'>)</span> +<dt>windows</dt> +<dd><p>The width of the time windows for which the TWAs should be +calculated.</p></dd> -<span class='fu'>max_twa_hs</span><span class='op'>(</span>M0 <span class='op'>=</span> <span class='fl'>1</span>, <span class='va'>k1</span>, <span class='va'>k2</span>, <span class='va'>tb</span>, <span class='va'>t</span><span class='op'>)</span></pre> - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>fit</th> - <td><p>An object of class <code><a href='mkinfit.html'>mkinfit</a></code>.</p></td> - </tr> - <tr> - <th>windows</th> - <td><p>The width of the time windows for which the TWAs should be -calculated.</p></td> - </tr> - <tr> - <th>M0</th> - <td><p>The initial concentration for which the maximum time weighted +<dt>M0</dt> +<dd><p>The initial concentration for which the maximum time weighted average over the decline curve should be calculated. The default is to use a value of 1, which means that a relative maximum time weighted average -factor (f_twa) is calculated.</p></td> - </tr> - <tr> - <th>k</th> - <td><p>The rate constant in the case of SFO kinetics.</p></td> - </tr> - <tr> - <th>t</th> - <td><p>The width of the time window.</p></td> - </tr> - <tr> - <th>alpha</th> - <td><p>Parameter of the FOMC model.</p></td> - </tr> - <tr> - <th>beta</th> - <td><p>Parameter of the FOMC model.</p></td> - </tr> - <tr> - <th>k1</th> - <td><p>The first rate constant of the DFOP or the HS kinetics.</p></td> - </tr> - <tr> - <th>k2</th> - <td><p>The second rate constant of the DFOP or the HS kinetics.</p></td> - </tr> - <tr> - <th>g</th> - <td><p>Parameter of the DFOP model.</p></td> - </tr> - <tr> - <th>tb</th> - <td><p>Parameter of the HS model.</p></td> - </tr> - </table> - - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> - - <p>For <code>max_twa_parent</code>, a numeric vector, named using the +factor (f_twa) is calculated.</p></dd> + + +<dt>k</dt> +<dd><p>The rate constant in the case of SFO kinetics.</p></dd> + + +<dt>t</dt> +<dd><p>The width of the time window.</p></dd> + + +<dt>alpha</dt> +<dd><p>Parameter of the FOMC model.</p></dd> + + +<dt>beta</dt> +<dd><p>Parameter of the FOMC model.</p></dd> + + +<dt>k1</dt> +<dd><p>The first rate constant of the DFOP or the HS kinetics.</p></dd> + + +<dt>k2</dt> +<dd><p>The second rate constant of the DFOP or the HS kinetics.</p></dd> + + +<dt>g</dt> +<dd><p>Parameter of the DFOP model.</p></dd> + + +<dt>tb</dt> +<dd><p>Parameter of the HS model.</p></dd> + +</dl></div> + <div id="value"> + <h2>Value</h2> + + +<p>For <code>max_twa_parent</code>, a numeric vector, named using the <code>windows</code> argument. For the other functions, a numeric vector of length one (also known as 'a number').</p> - <h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2> - - <p>FOCUS (2006) “Guidance Document on Estimating Persistence + </div> + <div id="references"> + <h2>References</h2> + <p>FOCUS (2006) “Guidance Document on Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in -EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, +EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, -<a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> - <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2> - +<a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + </div> + <div id="author"> + <h2>Author</h2> <p>Johannes Ranke</p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> - <span class='va'>fit</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='st'>"FOMC"</span>, <span class='va'>FOCUS_2006_C</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> - <span class='fu'>max_twa_parent</span><span class='op'>(</span><span class='va'>fit</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>7</span>, <span class='fl'>21</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> 7 21 -#> 34.71343 18.22124 </div><div class='input'> -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu">max_twa_parent</span><span class="op">(</span><span class="va">fit</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">7</span>, <span class="fl">21</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> 7 21 </span> +<span class="r-out co"><span class="r-pr">#></span> 34.71343 18.22124 </span> +<span class="r-in"><span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - 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- - - </head> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -74,23 +19,21 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -100,44 +43,46 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Datasets on aerobic soil metabolism of 2,4,5-T in six soils</h1> @@ -151,120 +96,135 @@ extracts.</p> </div> - <pre class="usage"><span class='va'>mccall81_245T</span></pre> - - - <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">mccall81_245T</span></span></code></pre></div> + </div> - <p>A dataframe containing the following variables.</p><dl> - <dt><code>name</code></dt><dd><p>the name of the compound observed. Note that T245 is used as + <div id="format"> + <h2>Format</h2> + <p>A dataframe containing the following variables.</p><dl><dt><code>name</code></dt> +<dd><p>the name of the compound observed. Note that T245 is used as an acronym for 2,4,5-T. T245 is a legitimate object name in R, which is necessary for specifying models using - <code><a href='mkinmod.html'>mkinmod</a></code>.</p></dd> - <dt><code>time</code></dt><dd><p>a numeric vector containing sampling times in days after + <code><a href="mkinmod.html">mkinmod</a></code>.</p></dd> + + <dt><code>time</code></dt> +<dd><p>a numeric vector containing sampling times in days after treatment</p></dd> - <dt><code>value</code></dt><dd><p>a numeric vector containing concentrations in percent of applied radioactivity</p></dd> - <dt><code>soil</code></dt><dd><p>a factor containing the name of the soil</p></dd> - -</dl> - <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2> + <dt><code>value</code></dt> +<dd><p>a numeric vector containing concentrations in percent of applied radioactivity</p></dd> + + <dt><code>soil</code></dt> +<dd><p>a factor containing the name of the soil</p></dd> + +</dl></div> + <div id="source"> + <h2>Source</h2> <p>McCall P, Vrona SA, Kelley SS (1981) Fate of uniformly carbon-14 ring labelled 2,4,5-Trichlorophenoxyacetic acid and 2,4-dichlorophenoxyacetic acid. J Agric Chem 29, 100-107 - doi: <a href='https://doi.org/10.1021/jf00103a026'>10.1021/jf00103a026</a></p> + <a href="https://doi.org/10.1021/jf00103a026" class="external-link">doi:10.1021/jf00103a026</a></p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> <span class='va'>SFO_SFO_SFO</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>T245 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='st'>"phenol"</span><span class='op'>)</span>, - phenol <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='st'>"anisole"</span><span class='op'>)</span>, - anisole <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'> <span class='co'># \dontrun{</span> - <span class='va'>fit.1</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>SFO_SFO_SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span><span class='op'>(</span><span class='va'>mccall81_245T</span>, <span class='va'>soil</span> <span class='op'>==</span> <span class='st'>"Commerce"</span><span class='op'>)</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>fit.1</span><span class='op'>)</span><span class='op'>$</span><span class='va'>bpar</span> -</div><div class='output co'>#> Estimate se_notrans t value Pr(>t) -#> T245_0 1.038550e+02 2.1847074945 47.537272 4.472189e-18 -#> k_T245 4.337042e-02 0.0018983965 22.845818 2.276911e-13 -#> k_phenol 4.050581e-01 0.2986993563 1.356073 9.756989e-02 -#> k_anisole 6.678742e-03 0.0008021439 8.326114 2.623177e-07 -#> f_T245_to_phenol 6.227599e-01 0.3985340558 1.562627 6.949413e-02 -#> f_phenol_to_anisole 1.000000e+00 0.6718439825 1.488441 7.867789e-02 -#> sigma 2.514628e+00 0.4907558883 5.123989 6.233157e-05 -#> Lower Upper -#> T245_0 99.246061385 1.084640e+02 -#> k_T245 0.039631621 4.746194e-02 -#> k_phenol 0.218013879 7.525762e-01 -#> k_anisole 0.005370739 8.305299e-03 -#> f_T245_to_phenol 0.547559081 6.924813e-01 -#> f_phenol_to_anisole 0.000000000 1.000000e+00 -#> sigma 1.706607296 3.322649e+00</div><div class='input'> <span class='fu'><a href='endpoints.html'>endpoints</a></span><span class='op'>(</span><span class='va'>fit.1</span><span class='op'>)</span> -</div><div class='output co'>#> $ff -#> T245_phenol T245_sink phenol_anisole phenol_sink -#> 6.227599e-01 3.772401e-01 1.000000e+00 3.773626e-10 -#> -#> $distimes -#> DT50 DT90 -#> T245 15.982025 53.09114 -#> phenol 1.711229 5.68458 -#> anisole 103.784093 344.76329 -#> </div><div class='input'> <span class='co'># formation fraction from phenol to anisol is practically 1. As we cannot</span> - <span class='co'># fix formation fractions when using the ilr transformation, we can turn of</span> - <span class='co'># the sink in the model generation</span> - <span class='va'>SFO_SFO_SFO_2</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>T245 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='st'>"phenol"</span><span class='op'>)</span>, - phenol <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='st'>"anisole"</span>, sink <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span>, - anisole <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'> <span class='va'>fit.2</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>SFO_SFO_SFO_2</span>, <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span><span class='op'>(</span><span class='va'>mccall81_245T</span>, <span class='va'>soil</span> <span class='op'>==</span> <span class='st'>"Commerce"</span><span class='op'>)</span>, - quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>fit.2</span><span class='op'>)</span><span class='op'>$</span><span class='va'>bpar</span> -</div><div class='output co'>#> Estimate se_notrans t value Pr(>t) Lower -#> T245_0 1.038550e+02 2.1623653066 48.028439 4.993108e-19 99.271020284 -#> k_T245 4.337042e-02 0.0018343666 23.643268 3.573556e-14 0.039650976 -#> k_phenol 4.050582e-01 0.1177237473 3.440752 1.679254e-03 0.218746587 -#> k_anisole 6.678742e-03 0.0006829745 9.778903 1.872894e-08 0.005377083 -#> f_T245_to_phenol 6.227599e-01 0.0342197875 18.198824 2.039411e-12 0.547975637 -#> sigma 2.514628e+00 0.3790944250 6.633250 2.875782e-06 1.710983655 -#> Upper -#> T245_0 108.43904074 -#> k_T245 0.04743877 -#> k_phenol 0.75005585 -#> k_anisole 0.00829550 -#> f_T245_to_phenol 0.69212308 -#> sigma 3.31827222</div><div class='input'> <span class='fu'><a href='endpoints.html'>endpoints</a></span><span class='op'>(</span><span class='va'>fit.1</span><span class='op'>)</span> -</div><div class='output co'>#> $ff -#> T245_phenol T245_sink phenol_anisole phenol_sink -#> 6.227599e-01 3.772401e-01 1.000000e+00 3.773626e-10 -#> -#> $distimes -#> DT50 DT90 -#> T245 15.982025 53.09114 -#> phenol 1.711229 5.68458 -#> anisole 103.784093 344.76329 -#> </div><div class='input'> <span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span><span class='op'>(</span><span class='va'>fit.2</span><span class='op'>)</span> -</div><div class='img'><img src='mccall81_245T-1.png' alt='' width='700' height='433' /></div><div class='input'> <span class='co'># }</span> -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span> <span class="va">SFO_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>T245 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"phenol"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> phenol <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"anisole"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> anisole <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span> <span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span> <span class="va">fit.1</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO_SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">mccall81_245T</span>, <span class="va">soil</span> <span class="op">==</span> <span class="st">"Commerce"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.1</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Estimate se_notrans t value Pr(>t)</span> +<span class="r-out co"><span class="r-pr">#></span> T245_0 1.038550e+02 2.1847074945 47.537272 4.472189e-18</span> +<span class="r-out co"><span class="r-pr">#></span> k_T245 4.337042e-02 0.0018983965 22.845818 2.276911e-13</span> +<span class="r-out co"><span class="r-pr">#></span> k_phenol 4.050581e-01 0.2986993563 1.356073 9.756989e-02</span> +<span class="r-out co"><span class="r-pr">#></span> k_anisole 6.678742e-03 0.0008021439 8.326114 2.623177e-07</span> +<span class="r-out co"><span class="r-pr">#></span> f_T245_to_phenol 6.227599e-01 0.3985340558 1.562627 6.949413e-02</span> +<span class="r-out co"><span class="r-pr">#></span> f_phenol_to_anisole 1.000000e+00 0.6718439825 1.488441 7.867789e-02</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 2.514628e+00 0.4907558883 5.123989 6.233157e-05</span> +<span class="r-out co"><span class="r-pr">#></span> Lower Upper</span> +<span class="r-out co"><span class="r-pr">#></span> T245_0 99.246061385 1.084640e+02</span> +<span class="r-out co"><span class="r-pr">#></span> k_T245 0.039631621 4.746194e-02</span> +<span class="r-out co"><span class="r-pr">#></span> k_phenol 0.218013879 7.525762e-01</span> +<span class="r-out co"><span class="r-pr">#></span> k_anisole 0.005370739 8.305299e-03</span> +<span class="r-out co"><span class="r-pr">#></span> f_T245_to_phenol 0.547559081 6.924813e-01</span> +<span class="r-out co"><span class="r-pr">#></span> f_phenol_to_anisole 0.000000000 1.000000e+00</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 1.706607296 3.322649e+00</span> +<span class="r-in"><span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fit.1</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> $ff</span> +<span class="r-out co"><span class="r-pr">#></span> T245_phenol T245_sink phenol_anisole phenol_sink </span> +<span class="r-out co"><span class="r-pr">#></span> 6.227599e-01 3.772401e-01 1.000000e+00 3.773626e-10 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> $distimes</span> +<span class="r-out co"><span class="r-pr">#></span> DT50 DT90</span> +<span class="r-out co"><span class="r-pr">#></span> T245 15.982025 53.09114</span> +<span class="r-out co"><span class="r-pr">#></span> phenol 1.711229 5.68458</span> +<span class="r-out co"><span class="r-pr">#></span> anisole 103.784093 344.76329</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-in"><span> <span class="co"># formation fraction from phenol to anisol is practically 1. As we cannot</span></span></span> +<span class="r-in"><span> <span class="co"># fix formation fractions when using the ilr transformation, we can turn of</span></span></span> +<span class="r-in"><span> <span class="co"># the sink in the model generation</span></span></span> +<span class="r-in"><span> <span class="va">SFO_SFO_SFO_2</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>T245 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"phenol"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> phenol <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"anisole"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> anisole <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span> <span class="va">fit.2</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO_SFO_2</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">mccall81_245T</span>, <span class="va">soil</span> <span class="op">==</span> <span class="st">"Commerce"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.2</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Estimate se_notrans t value Pr(>t) Lower</span> +<span class="r-out co"><span class="r-pr">#></span> T245_0 1.038550e+02 2.1623653066 48.028439 4.993108e-19 99.271020284</span> +<span class="r-out co"><span class="r-pr">#></span> k_T245 4.337042e-02 0.0018343666 23.643268 3.573556e-14 0.039650976</span> +<span class="r-out co"><span class="r-pr">#></span> k_phenol 4.050582e-01 0.1177237473 3.440752 1.679254e-03 0.218746587</span> +<span class="r-out co"><span class="r-pr">#></span> k_anisole 6.678742e-03 0.0006829745 9.778903 1.872894e-08 0.005377083</span> +<span class="r-out co"><span class="r-pr">#></span> f_T245_to_phenol 6.227599e-01 0.0342197875 18.198824 2.039411e-12 0.547975637</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 2.514628e+00 0.3790944250 6.633250 2.875782e-06 1.710983655</span> +<span class="r-out co"><span class="r-pr">#></span> Upper</span> +<span class="r-out co"><span class="r-pr">#></span> T245_0 108.43904074</span> +<span class="r-out co"><span class="r-pr">#></span> k_T245 0.04743877</span> +<span class="r-out co"><span class="r-pr">#></span> k_phenol 0.75005585</span> +<span class="r-out co"><span class="r-pr">#></span> k_anisole 0.00829550</span> +<span class="r-out co"><span class="r-pr">#></span> f_T245_to_phenol 0.69212308</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 3.31827222</span> +<span class="r-in"><span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fit.1</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> $ff</span> +<span class="r-out co"><span class="r-pr">#></span> T245_phenol T245_sink phenol_anisole phenol_sink </span> +<span class="r-out co"><span class="r-pr">#></span> 6.227599e-01 3.772401e-01 1.000000e+00 3.773626e-10 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> $distimes</span> +<span class="r-out co"><span class="r-pr">#></span> DT50 DT90</span> +<span class="r-out co"><span class="r-pr">#></span> T245 15.982025 53.09114</span> +<span class="r-out co"><span class="r-pr">#></span> phenol 1.711229 5.68458</span> +<span class="r-out co"><span class="r-pr">#></span> anisole 103.784093 344.76329</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-in"><span> <span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">fit.2</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="mccall81_245T-1.png" alt="" width="700" height="433"></span> +<span class="r-in"><span> <span class="co"># }</span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/mean_degparms.html b/docs/dev/reference/mean_degparms.html index 67db1868..feb37a1d 100644 --- a/docs/dev/reference/mean_degparms.html +++ b/docs/dev/reference/mean_degparms.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.2</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -44,19 +44,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/mhmkin-1.png b/docs/dev/reference/mhmkin-1.png Binary files differnew file mode 100644 index 00000000..2ecb6759 --- /dev/null +++ b/docs/dev/reference/mhmkin-1.png diff --git a/docs/dev/reference/mhmkin-2.png b/docs/dev/reference/mhmkin-2.png Binary files differnew file mode 100644 index 00000000..9bb43d35 --- /dev/null +++ b/docs/dev/reference/mhmkin-2.png diff --git a/docs/dev/reference/mhmkin.html b/docs/dev/reference/mhmkin.html index e87e20a1..1328aa48 100644 --- a/docs/dev/reference/mhmkin.html +++ b/docs/dev/reference/mhmkin.html @@ -22,7 +22,7 @@ mixed-effects model fitting functions."><meta name="robots" content="noindex"><! </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -64,7 +64,10 @@ mixed-effects model fitting functions."><meta name="robots" content="noindex"><! <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -110,7 +113,6 @@ mhmkin( backend = "saemix", algorithm = "saem", no_random_effect = NULL, - auto_ranef_threshold = 3, ..., cores = if (Sys.info()["sysname"] == "Windows") 1 else parallel::detectCores(), cluster = NULL @@ -147,16 +149,14 @@ supported</p></dd> <dt>no_random_effect</dt> -<dd><p>Default is NULL and will be passed to <a href="saem.html">saem</a>. If -you specify "auto", random effects are only included if the number -of datasets in which the parameter passed the t-test is at least 'auto_ranef_threshold'. -Beware that while this may make for convenient model reduction or even -numerical stability of the algorithm, it will likely lead to -underparameterised models.</p></dd> - - -<dt>auto_ranef_threshold</dt> -<dd><p>See 'no_random_effect.</p></dd> +<dd><p>Default is NULL and will be passed to <a href="saem.html">saem</a>. If a +character vector is supplied, it will be passed to all calls to <a href="saem.html">saem</a>, +which will exclude random effects for all matching parameters. Alternatively, +a list of character vectors or an object of class <a href="illparms.html">illparms.mhmkin</a> can be +specified. They have to have the same dimensions that the return object of +the current call will have, i.e. the number of rows must match the number +of degradation models in the mmkin object(s), and the number of columns must +match the number of error models used in the mmkin object(s).</p></dd> <dt>cores</dt> @@ -200,7 +200,7 @@ and the error model names for the second index (column index), with class attribute 'mhmkin'.</p> -<p>An object of class <code>mhmkin</code>.</p> +<p>An object inheriting from <code>mhmkin</code>.</p> </div> <div id="see-also"> <h2>See also</h2> @@ -211,6 +211,88 @@ attribute 'mhmkin'.</p> <p>Johannes Ranke</p> </div> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="co"># We start with separate evaluations of all the first six datasets with two</span></span></span> +<span class="r-in"><span><span class="co"># degradation models and two error models</span></span></span> +<span class="r-in"><span><span class="va">f_sep_const</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>, <span class="va">ds_fomc</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">6</span><span class="op">]</span>, cores <span class="op">=</span> <span class="fl">2</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">f_sep_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_sep_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># The mhmkin function sets up hierarchical degradation models aka</span></span></span> +<span class="r-in"><span><span class="co"># nonlinear mixed-effects models for all four combinations, specifying</span></span></span> +<span class="r-in"><span><span class="co"># uncorrelated random effects for all degradation parameters</span></span></span> +<span class="r-in"><span><span class="va">f_saem_1</span> <span class="op"><-</span> <span class="fu">mhmkin</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_sep_const</span>, <span class="va">f_sep_tc</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="status.html">status</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> error</span> +<span class="r-out co"><span class="r-pr">#></span> degradation const tc</span> +<span class="r-out co"><span class="r-pr">#></span> SFO OK OK</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC OK OK</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> OK: Fit terminated successfully</span> +<span class="r-in"><span><span class="co"># The 'illparms' function shows that in all hierarchical fits, at least</span></span></span> +<span class="r-in"><span><span class="co"># one random effect is ill-defined (the confidence interval for the</span></span></span> +<span class="r-in"><span><span class="co"># random effect expressed as standard deviation includes zero)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> error</span> +<span class="r-out co"><span class="r-pr">#></span> degradation const tc </span> +<span class="r-out co"><span class="r-pr">#></span> SFO sd(parent_0) sd(parent_0) </span> +<span class="r-out co"><span class="r-pr">#></span> FOMC sd(log_beta) sd(parent_0), sd(log_beta)</span> +<span class="r-in"><span><span class="co"># Therefore we repeat the fits, excluding the ill-defined random effects</span></span></span> +<span class="r-in"><span><span class="va">f_saem_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_1</span>, no_random_effect <span class="op">=</span> <span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="status.html">status</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> error</span> +<span class="r-out co"><span class="r-pr">#></span> degradation const tc</span> +<span class="r-out co"><span class="r-pr">#></span> SFO OK OK</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC OK OK</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> OK: Fit terminated successfully</span> +<span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> error</span> +<span class="r-out co"><span class="r-pr">#></span> degradation const tc</span> +<span class="r-out co"><span class="r-pr">#></span> SFO </span> +<span class="r-out co"><span class="r-pr">#></span> FOMC </span> +<span class="r-in"><span><span class="co"># Model comparisons show that FOMC with two-component error is preferable,</span></span></span> +<span class="r-in"><span><span class="co"># and confirms our reduction of the default parameter model</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Data: 95 observations of 1 variable(s) grouped in 6 datasets</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik</span> +<span class="r-out co"><span class="r-pr">#></span> SFO const 5 574.40 573.35 -282.20</span> +<span class="r-out co"><span class="r-pr">#></span> SFO tc 6 543.72 542.47 -265.86</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC const 7 489.67 488.22 -237.84</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC tc 8 406.11 404.44 -195.05</span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Data: 95 observations of 1 variable(s) grouped in 6 datasets</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik</span> +<span class="r-out co"><span class="r-pr">#></span> SFO const 4 572.22 571.39 -282.11</span> +<span class="r-out co"><span class="r-pr">#></span> SFO tc 5 541.63 540.59 -265.81</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC const 6 487.38 486.13 -237.69</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC tc 6 402.12 400.88 -195.06</span> +<span class="r-in"><span><span class="co"># The convergence plot for the selected model looks fine</span></span></span> +<span class="r-in"><span><span class="fu">saemix</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="mhmkin-1.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="co"># The plot of predictions versus data shows that we have a pretty data-rich</span></span></span> +<span class="r-in"><span><span class="co"># situation with homogeneous distribution of residuals, because we used the</span></span></span> +<span class="r-in"><span><span class="co"># same degradation model, error model and parameter distribution model that</span></span></span> +<span class="r-in"><span><span class="co"># was used in the data generation.</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="mhmkin-2.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="co"># We can specify the same parameter model reductions manually</span></span></span> +<span class="r-in"><span><span class="va">no_ranef</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"parent_0"</span>, <span class="st">"log_beta"</span>, <span class="st">"parent_0"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"parent_0"</span>, <span class="st">"log_beta"</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/dim.html" class="external-link">dim</a></span><span class="op">(</span><span class="va">no_ranef</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">f_saem_2m</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_1</span>, no_random_effect <span class="op">=</span> <span class="va">no_ranef</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_2m</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Data: 95 observations of 1 variable(s) grouped in 6 datasets</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik</span> +<span class="r-out co"><span class="r-pr">#></span> SFO const 4 572.22 571.39 -282.11</span> +<span class="r-out co"><span class="r-pr">#></span> SFO tc 5 541.63 540.59 -265.81</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC const 6 487.38 486.13 -237.69</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC tc 6 402.12 400.88 -195.06</span> +<span class="r-in"><span><span class="co"># }</span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> diff --git a/docs/dev/reference/mixed-1.png b/docs/dev/reference/mixed-1.png Binary files differindex 54b81b70..dbba1b03 100644 --- a/docs/dev/reference/mixed-1.png +++ b/docs/dev/reference/mixed-1.png diff --git a/docs/dev/reference/mixed.html b/docs/dev/reference/mixed.html index b2b83312..01a0614b 100644 --- a/docs/dev/reference/mixed.html +++ b/docs/dev/reference/mixed.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -26,7 +26,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -41,19 +41,28 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -84,73 +93,84 @@ </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">mixed</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span> - -<span class="co"># S3 method for mmkin</span> -<span class="fu">mixed</span><span class="op">(</span><span class="va">object</span>, method <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"none"</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span> - -<span class="co"># S3 method for mixed.mmkin</span> -<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mixed</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mmkin</span></span> +<span><span class="fu">mixed</span><span class="op">(</span><span class="va">object</span>, method <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"none"</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mixed.mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>object</dt> <dd><p>An <a href="mmkin.html">mmkin</a> row object</p></dd> + + <dt>...</dt> <dd><p>Currently not used</p></dd> + + <dt>method</dt> <dd><p>The method to be used</p></dd> + + <dt>x</dt> <dd><p>A mixed.mmkin object to print</p></dd> + + <dt>digits</dt> <dd><p>Number of digits to use for printing.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>An object of class 'mixed.mmkin' which has the observed data in a + + +<p>An object of class 'mixed.mmkin' which has the observed data in a single dataframe which is convenient for plotting</p> </div> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span> -<span class="r-in"><span class="va">n_biphasic</span> <span class="op"><-</span> <span class="fl">8</span></span> -<span class="r-in"><span class="va">err_1</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>const <span class="op">=</span> <span class="fl">1</span>, prop <span class="op">=</span> <span class="fl">0.07</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">DFOP_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span> -<span class="r-in"> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span> -<span class="r-in"> m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123456</span><span class="op">)</span></span> -<span class="r-in"><span class="va">log_sd</span> <span class="op"><-</span> <span class="fl">0.3</span></span> -<span class="r-in"><span class="va">syn_biphasic_parms</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">as.matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span> -<span class="r-in"> k1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.05</span><span class="op">)</span>, <span class="va">log_sd</span><span class="op">)</span>,</span> -<span class="r-in"> k2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.01</span><span class="op">)</span>, <span class="va">log_sd</span><span class="op">)</span>,</span> -<span class="r-in"> g <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">plogis</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fl">0</span>, <span class="va">log_sd</span><span class="op">)</span><span class="op">)</span>,</span> -<span class="r-in"> f_parent_to_m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">plogis</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fl">0</span>, <span class="va">log_sd</span><span class="op">)</span><span class="op">)</span>,</span> -<span class="r-in"> k_m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.002</span><span class="op">)</span>, <span class="va">log_sd</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">ds_biphasic_mean</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="va">n_biphasic</span>,</span> -<span class="r-in"> <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span> -<span class="r-in"> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">DFOP_SFO</span>, <span class="va">syn_biphasic_parms</span><span class="op">[</span><span class="va">i</span>, <span class="op">]</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, <span class="va">sampling_times</span><span class="op">)</span></span> -<span class="r-in"> <span class="op">}</span></span> -<span class="r-in"><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123456L</span><span class="op">)</span></span> -<span class="r-in"><span class="va">ds_biphasic</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">ds_biphasic_mean</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op">{</span></span> -<span class="r-in"> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">ds</span>,</span> -<span class="r-in"> sdfunc <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">err_1</span><span class="op">$</span><span class="va">const</span><span class="op">^</span><span class="fl">2</span> <span class="op">+</span> <span class="va">value</span><span class="op">^</span><span class="fl">2</span> <span class="op">*</span> <span class="va">err_1</span><span class="op">$</span><span class="va">prop</span><span class="op">^</span><span class="fl">2</span><span class="op">)</span>,</span> -<span class="r-in"> n <span class="op">=</span> <span class="fl">1</span>, secondary <span class="op">=</span> <span class="st">"m1"</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span> -<span class="r-in"><span class="op">}</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"><span class="va">f_mmkin</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">DFOP_SFO</span><span class="op">)</span>, <span class="va">ds_biphasic</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">f_mixed</span> <span class="op"><-</span> <span class="fu">mixed</span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_mixed</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">n_biphasic</span> <span class="op"><-</span> <span class="fl">8</span></span></span> +<span class="r-in"><span><span class="va">err_1</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>const <span class="op">=</span> <span class="fl">1</span>, prop <span class="op">=</span> <span class="fl">0.07</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">DFOP_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123456</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">log_sd</span> <span class="op"><-</span> <span class="fl">0.3</span></span></span> +<span class="r-in"><span><span class="va">syn_biphasic_parms</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">as.matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> k1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.05</span><span class="op">)</span>, <span class="va">log_sd</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> k2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.01</span><span class="op">)</span>, <span class="va">log_sd</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> g <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">plogis</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fl">0</span>, <span class="va">log_sd</span><span class="op">)</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> f_parent_to_m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">plogis</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fl">0</span>, <span class="va">log_sd</span><span class="op">)</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> k_m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.002</span><span class="op">)</span>, <span class="va">log_sd</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">ds_biphasic_mean</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="va">n_biphasic</span>,</span></span> +<span class="r-in"><span> <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">DFOP_SFO</span>, <span class="va">syn_biphasic_parms</span><span class="op">[</span><span class="va">i</span>, <span class="op">]</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, <span class="va">sampling_times</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="op">}</span></span></span> +<span class="r-in"><span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123456L</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">ds_biphasic</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">ds_biphasic_mean</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op">{</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">ds</span>,</span></span> +<span class="r-in"><span> sdfunc <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">err_1</span><span class="op">$</span><span class="va">const</span><span class="op">^</span><span class="fl">2</span> <span class="op">+</span> <span class="va">value</span><span class="op">^</span><span class="fl">2</span> <span class="op">*</span> <span class="va">err_1</span><span class="op">$</span><span class="va">prop</span><span class="op">^</span><span class="fl">2</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> n <span class="op">=</span> <span class="fl">1</span>, secondary <span class="op">=</span> <span class="st">"m1"</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span></span> +<span class="r-in"><span><span class="op">}</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="va">f_mmkin</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">DFOP_SFO</span><span class="op">)</span>, <span class="va">ds_biphasic</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">f_mixed</span> <span class="op"><-</span> <span class="fu">mixed</span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_mixed</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Kinetic model fitted by nonlinear regression to each dataset</span> <span class="r-out co"><span class="r-pr">#></span> Structural model:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span> @@ -177,9 +197,9 @@ single dataframe which is convenient for plotting</p> <span class="r-out co"><span class="r-pr">#></span> 100.674757 -8.761916 -0.004347 -3.348812 -3.986853 </span> <span class="r-out co"><span class="r-pr">#></span> g_qlogis </span> <span class="r-out co"><span class="r-pr">#></span> -0.087392 </span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_mixed</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_mixed</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="mixed-1.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="co"># }</span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> </code></pre></div> </div> </div> @@ -194,7 +214,7 @@ single dataframe which is convenient for plotting</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/dev/reference/mkin_long_to_wide.html b/docs/dev/reference/mkin_long_to_wide.html index 6246fbe2..3e55885f 100644 --- a/docs/dev/reference/mkin_long_to_wide.html +++ b/docs/dev/reference/mkin_long_to_wide.html @@ -1,69 +1,14 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - <head> - <meta charset="utf-8"> -<meta http-equiv="X-UA-Compatible" content="IE=edge"> -<meta name="viewport" content="width=device-width, initial-scale=1.0"> - -<title>Convert a dataframe from long to wide format — mkin_long_to_wide • mkin</title> - - -<!-- jquery --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script> -<!-- Bootstrap --> - -<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous" /> - -<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" 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href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Convert a dataframe from long to wide format — mkin_long_to_wide"><meta property="og:description" content="This function takes a dataframe in the long form, i.e. with a row for each observed value, and converts it into a dataframe with one independent -variable and several dependent variables as columns." /> - - -<meta name="robots" content="noindex"> - -<!-- mathjax --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script> - -<!--[if lt IE 9]> +variable and several dependent variables as columns."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> - - +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - </head> - - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -74,23 +19,21 @@ variable and several dependent variables as columns." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -100,48 +43,50 @@ variable and several dependent variables as columns." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Convert a dataframe from long to wide format</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/mkin_long_to_wide.R'><code>R/mkin_long_to_wide.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkin_long_to_wide.R" class="external-link"><code>R/mkin_long_to_wide.R</code></a></small> <div class="hidden name"><code>mkin_long_to_wide.Rd</code></div> </div> @@ -151,87 +96,89 @@ observed value, and converts it into a dataframe with one independent variable and several dependent variables as columns.</p> </div> - <pre class="usage"><span class='fu'>mkin_long_to_wide</span><span class='op'>(</span><span class='va'>long_data</span>, time <span class='op'>=</span> <span class='st'>"time"</span>, outtime <span class='op'>=</span> <span class='st'>"time"</span><span class='op'>)</span></pre> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkin_long_to_wide</span><span class="op">(</span><span class="va">long_data</span>, time <span class="op">=</span> <span class="st">"time"</span>, outtime <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span></code></pre></div> + </div> - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>long_data</th> - <td><p>The dataframe must contain one variable called "time" with + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>long_data</dt> +<dd><p>The dataframe must contain one variable called "time" with the time values specified by the <code>time</code> argument, one column called "name" with the grouping of the observed values, and finally one column of -observed values called "value".</p></td> - </tr> - <tr> - <th>time</th> - <td><p>The name of the time variable in the long input data.</p></td> - </tr> - <tr> - <th>outtime</th> - <td><p>The name of the time variable in the wide output data.</p></td> - </tr> - </table> - - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> - - <p>Dataframe in wide format.</p> - <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2> +observed values called "value".</p></dd> + + +<dt>time</dt> +<dd><p>The name of the time variable in the long input data.</p></dd> + + +<dt>outtime</dt> +<dd><p>The name of the time variable in the wide output data.</p></dd> + +</dl></div> + <div id="value"> + <h2>Value</h2> + +<p>Dataframe in wide format.</p> + </div> + <div id="author"> + <h2>Author</h2> <p>Johannes Ranke</p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> -<span class='fu'>mkin_long_to_wide</span><span class='op'>(</span><span class='va'>FOCUS_2006_D</span><span class='op'>)</span> -</div><div class='output co'>#> time parent m1 -#> 1 0 99.46 0.00 -#> 2 0 102.04 0.00 -#> 3 1 93.50 4.84 -#> 4 1 92.50 5.64 -#> 5 3 63.23 12.91 -#> 6 3 68.99 12.96 -#> 7 7 52.32 22.97 -#> 8 7 55.13 24.47 -#> 9 14 27.27 41.69 -#> 10 14 26.64 33.21 -#> 11 21 11.50 44.37 -#> 12 21 11.64 46.44 -#> 13 35 2.85 41.22 -#> 14 35 2.91 37.95 -#> 15 50 0.69 41.19 -#> 16 50 0.63 40.01 -#> 17 75 0.05 40.09 -#> 18 75 0.06 33.85 -#> 19 100 NA 31.04 -#> 20 100 NA 33.13 -#> 21 120 NA 25.15 -#> 22 120 NA 33.31</div><div class='input'> -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="fu">mkin_long_to_wide</span><span class="op">(</span><span class="va">FOCUS_2006_D</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> time parent m1</span> +<span class="r-out co"><span class="r-pr">#></span> 1 0 99.46 0.00</span> +<span class="r-out co"><span class="r-pr">#></span> 2 0 102.04 0.00</span> +<span class="r-out co"><span class="r-pr">#></span> 3 1 93.50 4.84</span> +<span class="r-out co"><span class="r-pr">#></span> 4 1 92.50 5.64</span> +<span class="r-out co"><span class="r-pr">#></span> 5 3 63.23 12.91</span> +<span class="r-out co"><span class="r-pr">#></span> 6 3 68.99 12.96</span> +<span class="r-out co"><span class="r-pr">#></span> 7 7 52.32 22.97</span> +<span class="r-out co"><span class="r-pr">#></span> 8 7 55.13 24.47</span> +<span class="r-out co"><span class="r-pr">#></span> 9 14 27.27 41.69</span> +<span class="r-out co"><span class="r-pr">#></span> 10 14 26.64 33.21</span> +<span class="r-out co"><span class="r-pr">#></span> 11 21 11.50 44.37</span> +<span class="r-out co"><span class="r-pr">#></span> 12 21 11.64 46.44</span> +<span class="r-out co"><span class="r-pr">#></span> 13 35 2.85 41.22</span> +<span class="r-out co"><span class="r-pr">#></span> 14 35 2.91 37.95</span> +<span class="r-out co"><span class="r-pr">#></span> 15 50 0.69 41.19</span> +<span class="r-out co"><span class="r-pr">#></span> 16 50 0.63 40.01</span> +<span class="r-out co"><span class="r-pr">#></span> 17 75 0.05 40.09</span> +<span class="r-out co"><span class="r-pr">#></span> 18 75 0.06 33.85</span> +<span class="r-out co"><span class="r-pr">#></span> 19 100 NA 31.04</span> +<span class="r-out co"><span class="r-pr">#></span> 20 100 NA 33.13</span> +<span class="r-out co"><span class="r-pr">#></span> 21 120 NA 25.15</span> +<span class="r-out co"><span class="r-pr">#></span> 22 120 NA 33.31</span> +<span class="r-in"><span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/mkin_wide_to_long.html b/docs/dev/reference/mkin_wide_to_long.html index f2bf00c1..fb23d3dc 100644 --- a/docs/dev/reference/mkin_wide_to_long.html +++ b/docs/dev/reference/mkin_wide_to_long.html @@ -1,69 +1,14 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - <head> - <meta charset="utf-8"> -<meta http-equiv="X-UA-Compatible" content="IE=edge"> -<meta name="viewport" content="width=device-width, initial-scale=1.0"> - -<title>Convert a dataframe with observations over time into long format — mkin_wide_to_long • mkin</title> - - -<!-- jquery --> -<script 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data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -74,23 +19,21 @@ mkinfit." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -100,118 +43,122 @@ mkinfit." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Convert a dataframe with observations over time into long format</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/mkin_wide_to_long.R'><code>R/mkin_wide_to_long.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkin_wide_to_long.R" class="external-link"><code>R/mkin_wide_to_long.R</code></a></small> <div class="hidden name"><code>mkin_wide_to_long.Rd</code></div> </div> <div class="ref-description"> <p>This function simply takes a dataframe with one independent variable and several dependent variable and converts it into the long form as required by -<code><a href='mkinfit.html'>mkinfit</a></code>.</p> +<code><a href="mkinfit.html">mkinfit</a></code>.</p> </div> - <pre class="usage"><span class='fu'>mkin_wide_to_long</span><span class='op'>(</span><span class='va'>wide_data</span>, time <span class='op'>=</span> <span class='st'>"t"</span><span class='op'>)</span></pre> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkin_wide_to_long</span><span class="op">(</span><span class="va">wide_data</span>, time <span class="op">=</span> <span class="st">"t"</span><span class="op">)</span></span></code></pre></div> + </div> - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>wide_data</th> - <td><p>The dataframe must contain one variable with the time + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>wide_data</dt> +<dd><p>The dataframe must contain one variable with the time values specified by the <code>time</code> argument and usually more than one -column of observed values.</p></td> - </tr> - <tr> - <th>time</th> - <td><p>The name of the time variable.</p></td> - </tr> - </table> +column of observed values.</p></dd> + - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> +<dt>time</dt> +<dd><p>The name of the time variable.</p></dd> - <p>Dataframe in long format as needed for <code><a href='mkinfit.html'>mkinfit</a></code>.</p> - <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2> +</dl></div> + <div id="value"> + <h2>Value</h2> + +<p>Dataframe in long format as needed for <code><a href="mkinfit.html">mkinfit</a></code>.</p> + </div> + <div id="author"> + <h2>Author</h2> <p>Johannes Ranke</p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> -<span class='va'>wide</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a></span><span class='op'>(</span>t <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span><span class='op'>)</span>, x <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>7</span><span class='op'>)</span>, y <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>,<span class='fl'>4</span>,<span class='fl'>5</span><span class='op'>)</span><span class='op'>)</span> -<span class='fu'>mkin_wide_to_long</span><span class='op'>(</span><span class='va'>wide</span><span class='op'>)</span> -</div><div class='output co'>#> name time value -#> 1 x 1 1 -#> 2 x 2 4 -#> 3 x 3 7 -#> 4 y 1 3 -#> 5 y 2 4 -#> 6 y 3 5</div><div class='input'> -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">wide</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span><span class="op">)</span>, x <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">4</span>,<span class="fl">7</span><span class="op">)</span>, y <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>,<span class="fl">4</span>,<span class="fl">5</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu">mkin_wide_to_long</span><span class="op">(</span><span class="va">wide</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> name time value</span> +<span class="r-out co"><span class="r-pr">#></span> 1 x 1 1</span> +<span class="r-out co"><span class="r-pr">#></span> 2 x 2 4</span> +<span class="r-out co"><span class="r-pr">#></span> 3 x 3 7</span> +<span class="r-out co"><span class="r-pr">#></span> 4 y 1 3</span> +<span class="r-out co"><span class="r-pr">#></span> 5 y 2 4</span> +<span class="r-out co"><span class="r-pr">#></span> 6 y 3 5</span> +<span class="r-in"><span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/mkinds.html b/docs/dev/reference/mkinds.html index b571e3a0..a7fb9916 100644 --- a/docs/dev/reference/mkinds.html +++ b/docs/dev/reference/mkinds.html @@ -20,7 +20,7 @@ provided by this package come as mkinds objects nevertheless."><meta name="robot </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.2</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -47,19 +47,25 @@ provided by this package come as mkinds objects nevertheless."><meta name="robot <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/mkindsg.html b/docs/dev/reference/mkindsg.html index d19a7a1d..cbf55fca 100644 --- a/docs/dev/reference/mkindsg.html +++ b/docs/dev/reference/mkindsg.html @@ -20,7 +20,7 @@ dataset if no data are supplied."><meta name="robots" content="noindex"><!-- mat </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.2</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -47,19 +47,25 @@ dataset if no data are supplied."><meta name="robots" content="noindex"><!-- mat <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/mkinerrmin.html b/docs/dev/reference/mkinerrmin.html index 94c575cb..2c9f0b13 100644 --- a/docs/dev/reference/mkinerrmin.html +++ b/docs/dev/reference/mkinerrmin.html @@ -1,68 +1,13 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - <head> - <meta charset="utf-8"> -<meta http-equiv="X-UA-Compatible" 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src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - - - </head> - - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -73,23 +18,21 @@ the chi-squared test as defined in the FOCUS kinetics report from 2006." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -99,48 +42,50 @@ the chi-squared test as defined in the FOCUS kinetics report from 2006." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Calculate the minimum error to assume in order to pass the variance test</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/mkinerrmin.R'><code>R/mkinerrmin.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinerrmin.R" class="external-link"><code>R/mkinerrmin.R</code></a></small> <div class="hidden name"><code>mkinerrmin.Rd</code></div> </div> @@ -149,89 +94,100 @@ the chi-squared test as defined in the FOCUS kinetics report from 2006." /> the chi-squared test as defined in the FOCUS kinetics report from 2006.</p> </div> - <pre class="usage"><span class='fu'>mkinerrmin</span><span class='op'>(</span><span class='va'>fit</span>, alpha <span class='op'>=</span> <span class='fl'>0.05</span><span class='op'>)</span></pre> - - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>fit</th> - <td><p>an object of class <code><a href='mkinfit.html'>mkinfit</a></code>.</p></td> - </tr> - <tr> - <th>alpha</th> - <td><p>The confidence level chosen for the chi-squared test.</p></td> - </tr> - </table> - - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> - - <p>A dataframe with the following components:</p> -<dt>err.min</dt><dd><p>The -relative error, expressed as a fraction.</p></dd> <dt>n.optim</dt><dd><p>The number of -optimised parameters attributed to the data series.</p></dd> <dt>df</dt><dd><p>The number of + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinerrmin</span><span class="op">(</span><span class="va">fit</span>, alpha <span class="op">=</span> <span class="fl">0.05</span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>fit</dt> +<dd><p>an object of class <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd> + + +<dt>alpha</dt> +<dd><p>The confidence level chosen for the chi-squared test.</p></dd> + +</dl></div> + <div id="value"> + <h2>Value</h2> + + +<p>A dataframe with the following components:</p> +<dl><dt>err.min</dt> +<dd><p>The +relative error, expressed as a fraction.</p></dd> + <dt>n.optim</dt> +<dd><p>The number of +optimised parameters attributed to the data series.</p></dd> + <dt>df</dt> +<dd><p>The number of remaining degrees of freedom for the chi2 error level calculations. Note that mean values are used for the chi2 statistic and therefore every time -point with observed values in the series only counts one time.</p></dd> The +point with observed values in the series only counts one time.</p></dd> +</dl><p>The dataframe has one row for the total dataset and one further row for each -observed state variable in the model. - - <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2> - - <p>This function is used internally by <code><a href='summary.mkinfit.html'>summary.mkinfit</a></code>.</p> - <h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2> - - <p>FOCUS (2006) “Guidance Document on Estimating Persistence +observed state variable in the model.</p> + </div> + <div id="details"> + <h2>Details</h2> + <p>This function is used internally by <code><a href="summary.mkinfit.html">summary.mkinfit</a></code>.</p> + </div> + <div id="references"> + <h2>References</h2> + <p>FOCUS (2006) “Guidance Document on Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU -Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC +Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, -<a href='http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics'>http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> - - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> -<span class='va'>SFO_SFO</span> <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='st'>"m1"</span><span class='op'>)</span>, - m1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span>, - use_of_ff <span class='op'>=</span> <span class='st'>"max"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'> -<span class='va'>fit_FOCUS_D</span> <span class='op'>=</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>SFO_SFO</span>, <span class='va'>FOCUS_2006_D</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/Round.html'>round</a></span><span class='op'>(</span><span class='fu'>mkinerrmin</span><span class='op'>(</span><span class='va'>fit_FOCUS_D</span><span class='op'>)</span>, <span class='fl'>4</span><span class='op'>)</span> -</div><div class='output co'>#> err.min n.optim df -#> All data 0.0640 4 15 -#> parent 0.0646 2 7 -#> m1 0.0469 2 8</div><div class='input'><span class='co'># \dontrun{</span> - <span class='va'>fit_FOCUS_E</span> <span class='op'>=</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>SFO_SFO</span>, <span class='va'>FOCUS_2006_E</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> - <span class='fu'><a href='https://rdrr.io/r/base/Round.html'>round</a></span><span class='op'>(</span><span class='fu'>mkinerrmin</span><span class='op'>(</span><span class='va'>fit_FOCUS_E</span><span class='op'>)</span>, <span class='fl'>4</span><span class='op'>)</span> -</div><div class='output co'>#> err.min n.optim df -#> All data 0.1544 4 13 -#> parent 0.1659 2 7 -#> m1 0.1095 2 6</div><div class='input'><span class='co'># }</span> - -</div></pre> +<a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a></p> + </div> + + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">fit_FOCUS_D</span> <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="fu">mkinerrmin</span><span class="op">(</span><span class="va">fit_FOCUS_D</span><span class="op">)</span>, <span class="fl">4</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> err.min n.optim df</span> +<span class="r-out co"><span class="r-pr">#></span> All data 0.0640 4 15</span> +<span class="r-out co"><span class="r-pr">#></span> parent 0.0646 2 7</span> +<span class="r-out co"><span class="r-pr">#></span> m1 0.0469 2 8</span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span> <span class="va">fit_FOCUS_E</span> <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_E</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="fu">mkinerrmin</span><span class="op">(</span><span class="va">fit_FOCUS_E</span><span class="op">)</span>, <span class="fl">4</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> err.min n.optim df</span> +<span class="r-out co"><span class="r-pr">#></span> All data 0.1544 4 13</span> +<span class="r-out co"><span class="r-pr">#></span> parent 0.1659 2 7</span> +<span class="r-out co"><span class="r-pr">#></span> m1 0.1095 2 6</span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - 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In addition, one or more dashed line(s) show the fitted error model. A combined plot of the fitted model and this error model plot can be obtained with plot.mkinfit -using the argument show_errplot = TRUE." /> - - -<meta name="robots" content="noindex"> - -<!-- mathjax --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script> - -<!--[if lt IE 9]> +using the argument show_errplot = TRUE."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> - - +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - </head> - - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -76,23 +21,21 @@ using the argument show_errplot = TRUE." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -102,48 +45,50 @@ using the argument show_errplot = TRUE." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Function to plot squared residuals and the error model for an mkin object</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/mkinerrplot.R'><code>R/mkinerrplot.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinerrplot.R" class="external-link"><code>R/mkinerrplot.R</code></a></small> <div class="hidden name"><code>mkinerrplot.Rd</code></div> </div> @@ -151,128 +96,133 @@ using the argument show_errplot = TRUE." /> <p>This function plots the squared residuals for the specified subset of the observed variables from an mkinfit object. In addition, one or more dashed line(s) show the fitted error model. A combined plot of the fitted model -and this error model plot can be obtained with <code><a href='plot.mkinfit.html'>plot.mkinfit</a></code> +and this error model plot can be obtained with <code><a href="plot.mkinfit.html">plot.mkinfit</a></code> using the argument <code>show_errplot = TRUE</code>.</p> </div> - <pre class="usage"><span class='fu'>mkinerrplot</span><span class='op'>(</span> - <span class='va'>object</span>, - obs_vars <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span><span class='op'>(</span><span class='va'>object</span><span class='op'>$</span><span class='va'>mkinmod</span><span class='op'>$</span><span class='va'>map</span><span class='op'>)</span>, - xlim <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>1.1</span> <span class='op'>*</span> <span class='fu'><a href='https://rdrr.io/r/base/Extremes.html'>max</a></span><span class='op'>(</span><span class='va'>object</span><span class='op'>$</span><span class='va'>data</span><span class='op'>$</span><span class='va'>predicted</span><span class='op'>)</span><span class='op'>)</span>, - xlab <span class='op'>=</span> <span class='st'>"Predicted"</span>, - ylab <span class='op'>=</span> <span class='st'>"Squared residual"</span>, - maxy <span class='op'>=</span> <span class='st'>"auto"</span>, - legend <span class='op'>=</span> <span class='cn'>TRUE</span>, - lpos <span class='op'>=</span> <span class='st'>"topright"</span>, - col_obs <span class='op'>=</span> <span class='st'>"auto"</span>, - pch_obs <span class='op'>=</span> <span class='st'>"auto"</span>, - frame <span class='op'>=</span> <span class='cn'>TRUE</span>, - <span class='va'>...</span> -<span class='op'>)</span></pre> - - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>object</th> - <td><p>A fit represented in an <code><a href='mkinfit.html'>mkinfit</a></code> object.</p></td> - </tr> - <tr> - <th>obs_vars</th> - <td><p>A character vector of names of the observed variables for + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinerrplot</span><span class="op">(</span></span> +<span> <span class="va">object</span>,</span> +<span> obs_vars <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">object</span><span class="op">$</span><span class="va">mkinmod</span><span class="op">$</span><span class="va">map</span><span class="op">)</span>,</span> +<span> xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1.1</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="va">object</span><span class="op">$</span><span class="va">data</span><span class="op">$</span><span class="va">predicted</span><span class="op">)</span><span class="op">)</span>,</span> +<span> xlab <span class="op">=</span> <span class="st">"Predicted"</span>,</span> +<span> ylab <span class="op">=</span> <span class="st">"Squared residual"</span>,</span> +<span> maxy <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> legend <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> lpos <span class="op">=</span> <span class="st">"topright"</span>,</span> +<span> col_obs <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> pch_obs <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> frame <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>object</dt> +<dd><p>A fit represented in an <code><a href="mkinfit.html">mkinfit</a></code> object.</p></dd> + + +<dt>obs_vars</dt> +<dd><p>A character vector of names of the observed variables for which residuals should be plotted. Defaults to all observed variables in -the model</p></td> - </tr> - <tr> - <th>xlim</th> - <td><p>plot range in x direction.</p></td> - </tr> - <tr> - <th>xlab</th> - <td><p>Label for the x axis.</p></td> - </tr> - <tr> - <th>ylab</th> - <td><p>Label for the y axis.</p></td> - </tr> - <tr> - <th>maxy</th> - <td><p>Maximum value of the residuals. This is used for the scaling of -the y axis and defaults to "auto".</p></td> - </tr> - <tr> - <th>legend</th> - <td><p>Should a legend be plotted?</p></td> - </tr> - <tr> - <th>lpos</th> - <td><p>Where should the legend be placed? Default is "topright". Will -be passed on to <code><a href='https://rdrr.io/r/graphics/legend.html'>legend</a></code>.</p></td> - </tr> - <tr> - <th>col_obs</th> - <td><p>Colors for the observed variables.</p></td> - </tr> - <tr> - <th>pch_obs</th> - <td><p>Symbols to be used for the observed variables.</p></td> - </tr> - <tr> - <th>frame</th> - <td><p>Should a frame be drawn around the plots?</p></td> - </tr> - <tr> - <th>...</th> - <td><p>further arguments passed to <code><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></code>.</p></td> - </tr> - </table> - - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> - - <p>Nothing is returned by this function, as it is called for its side -effect, namely to produce a plot.</p> - <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2> +the model</p></dd> - <div class='dont-index'><p><code><a href='mkinplot.html'>mkinplot</a></code>, for a way to plot the data and the fitted -lines of the mkinfit object.</p></div> - <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2> - <p>Johannes Ranke</p> +<dt>xlim</dt> +<dd><p>plot range in x direction.</p></dd> + + +<dt>xlab</dt> +<dd><p>Label for the x axis.</p></dd> + + +<dt>ylab</dt> +<dd><p>Label for the y axis.</p></dd> + + +<dt>maxy</dt> +<dd><p>Maximum value of the residuals. This is used for the scaling of +the y axis and defaults to "auto".</p></dd> + + +<dt>legend</dt> +<dd><p>Should a legend be plotted?</p></dd> + + +<dt>lpos</dt> +<dd><p>Where should the legend be placed? Default is "topright". Will +be passed on to <code><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></code>.</p></dd> + - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> -<span class='co'># \dontrun{</span> -<span class='va'>model</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"m1"</span><span class='op'>)</span>, m1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'><span class='va'>fit</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>model</span>, <span class='va'>FOCUS_2006_D</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'>mkinerrplot</span><span class='op'>(</span><span class='va'>fit</span><span class='op'>)</span> -</div><div class='img'><img src='mkinerrplot-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># }</span> +<dt>col_obs</dt> +<dd><p>Colors for the observed variables.</p></dd> -</div></pre> + +<dt>pch_obs</dt> +<dd><p>Symbols to be used for the observed variables.</p></dd> + + +<dt>frame</dt> +<dd><p>Should a frame be drawn around the plots?</p></dd> + + +<dt>...</dt> +<dd><p>further arguments passed to <code><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></code>.</p></dd> + +</dl></div> + <div id="value"> + <h2>Value</h2> + + +<p>Nothing is returned by this function, as it is called for its side +effect, namely to produce a plot.</p> + </div> + <div id="see-also"> + <h2>See also</h2> + <div class="dont-index"><p><code><a href="mkinplot.html">mkinplot</a></code>, for a way to plot the data and the fitted +lines of the mkinfit object.</p></div> + </div> + <div id="author"> + <h2>Author</h2> + <p>Johannes Ranke</p> + </div> + + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="va">model</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">FOCUS_2006_D</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span> +<span class="r-in"><span><span class="fu">mkinerrplot</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="mkinerrplot-1.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/mkinfit.html b/docs/dev/reference/mkinfit.html index 17da44cb..ee596e89 100644 --- a/docs/dev/reference/mkinfit.html +++ b/docs/dev/reference/mkinfit.html @@ -25,7 +25,7 @@ likelihood function."><meta name="robots" content="noindex"><!-- mathjax --><scr </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -67,7 +67,10 @@ likelihood function."><meta name="robots" content="noindex"><!-- mathjax --><scr <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -381,17 +384,17 @@ Degradation Data. <em>Environments</em> 6(12) 124 <span class="r-in"><span><span class="co"># Use shorthand notation for parent only degradation</span></span></span> <span class="r-in"><span><span class="va">fit</span> <span class="op"><-</span> <span class="fu">mkinfit</span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.2.0 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.2.2 </span> <span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Tue Nov 1 14:09:26 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Tue Nov 1 14:09:26 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Nov 24 08:05:53 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Nov 24 08:05:53 2022 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type analytical </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted using 222 model solutions performed in 0.049 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted using 222 model solutions performed in 0.045 s</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Error model: Constant variance </span> <span class="r-out co"><span class="r-pr">#></span> </span> @@ -531,11 +534,10 @@ Degradation Data. <em>Environments</em> 6(12) 124 <span class="r-in"><span> analytical <span class="op">=</span> <span class="fu">mkinfit</span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>,</span></span> <span class="r-in"><span> solution_type <span class="op">=</span> <span class="st">"analytical"</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="op">}</span></span></span> -<span class="r-msg co"><span class="r-pr">#></span> Loading required package: rbenchmark</span> <span class="r-out co"><span class="r-pr">#></span> test relative elapsed</span> -<span class="r-out co"><span class="r-pr">#></span> 3 analytical 1.000 0.559</span> -<span class="r-out co"><span class="r-pr">#></span> 1 deSolve_compiled 1.556 0.870</span> -<span class="r-out co"><span class="r-pr">#></span> 2 eigen 2.603 1.455</span> +<span class="r-out co"><span class="r-pr">#></span> 3 analytical 1.000 0.616</span> +<span class="r-out co"><span class="r-pr">#></span> 1 deSolve_compiled 1.505 0.927</span> +<span class="r-out co"><span class="r-pr">#></span> 2 eigen 2.455 1.512</span> <span class="r-in"><span><span class="co"># }</span></span></span> <span class="r-in"><span></span></span> <span class="r-in"><span><span class="co"># Use stepwise fitting, using optimised parameters from parent only fit, FOMC-SFO</span></span></span> @@ -562,10 +564,10 @@ Degradation Data. <em>Environments</em> 6(12) 124 <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>NaNs produced</span> <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>NaNs produced</span> <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>diag(.) had 0 or NA entries; non-finite result is doubtful</span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.2.0 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.2.2 </span> <span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Tue Nov 1 14:09:37 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Tue Nov 1 14:09:37 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Nov 24 08:06:05 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Nov 24 08:06:05 2022 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span> @@ -574,7 +576,7 @@ Degradation Data. <em>Environments</em> 6(12) 124 <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type deSolve </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted using 3729 model solutions performed in 2.43 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted using 3729 model solutions performed in 2.81 s</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Error model: Two-component variance function </span> <span class="r-out co"><span class="r-pr">#></span> </span> diff --git a/docs/dev/reference/mkinmod.html b/docs/dev/reference/mkinmod.html index 5d362f76..145dee83 100644 --- a/docs/dev/reference/mkinmod.html +++ b/docs/dev/reference/mkinmod.html @@ -21,7 +21,7 @@ components."><meta name="robots" content="noindex"><!-- mathjax --><script src=" </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -30,7 +30,7 @@ components."><meta name="robots" content="noindex"><!-- mathjax --><script src=" <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -45,19 +45,28 @@ components."><meta name="robots" content="noindex"><!-- mathjax --><script src=" <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -92,22 +101,22 @@ components.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">mkinmod</span><span class="op">(</span> - <span class="va">...</span>, - use_of_ff <span class="op">=</span> <span class="st">"max"</span>, - name <span class="op">=</span> <span class="cn">NULL</span>, - speclist <span class="op">=</span> <span class="cn">NULL</span>, - quiet <span class="op">=</span> <span class="cn">FALSE</span>, - verbose <span class="op">=</span> <span class="cn">FALSE</span>, - dll_dir <span class="op">=</span> <span class="cn">NULL</span>, - unload <span class="op">=</span> <span class="cn">FALSE</span>, - overwrite <span class="op">=</span> <span class="cn">FALSE</span> -<span class="op">)</span> - -<span class="co"># S3 method for mkinmod</span> -<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span> - -<span class="fu">mkinsub</span><span class="op">(</span><span class="va">submodel</span>, to <span class="op">=</span> <span class="cn">NULL</span>, sink <span class="op">=</span> <span class="cn">TRUE</span>, full_name <span class="op">=</span> <span class="cn">NA</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinmod</span><span class="op">(</span></span> +<span> <span class="va">...</span>,</span> +<span> use_of_ff <span class="op">=</span> <span class="st">"max"</span>,</span> +<span> name <span class="op">=</span> <span class="cn">NULL</span>,</span> +<span> speclist <span class="op">=</span> <span class="cn">NULL</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> verbose <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> dll_dir <span class="op">=</span> <span class="cn">NULL</span>,</span> +<span> unload <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> overwrite <span class="op">=</span> <span class="cn">FALSE</span></span> +<span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mkinmod</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="fu">mkinsub</span><span class="op">(</span><span class="va">submodel</span>, to <span class="op">=</span> <span class="cn">NULL</span>, sink <span class="op">=</span> <span class="cn">TRUE</span>, full_name <span class="op">=</span> <span class="cn">NA</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> @@ -123,76 +132,112 @@ the source compartment. Additionally, <code>mkinsub()</code> has an argument <code>to</code>, specifying names of variables to which a transfer is to be assumed in the model. If the argument <code>use_of_ff</code> is set to "min" -(default) and the model for the compartment is "SFO" or "SFORB", an +and the model for the compartment is "SFO" or "SFORB", an additional <code>mkinsub()</code> argument can be <code>sink = FALSE</code>, effectively fixing the flux to sink to zero. In print.mkinmod, this argument is currently not used.</p></dd> + + <dt>use_of_ff</dt> <dd><p>Specification of the use of formation fractions in the model equations and, if applicable, the coefficient matrix. If "max", formation fractions are always used (default). If "min", a minimum use of formation fractions is made, i.e. each first-order pathway to a metabolite has its own rate constant.</p></dd> + + <dt>name</dt> <dd><p>A name for the model. Should be a valid R object name.</p></dd> + + <dt>speclist</dt> <dd><p>The specification of the observed variables and their submodel types and pathways can be given as a single list using this argument. Default is NULL.</p></dd> + + <dt>quiet</dt> <dd><p>Should messages be suppressed?</p></dd> + + <dt>verbose</dt> <dd><p>If <code>TRUE</code>, passed to <code><a href="https://rdrr.io/pkg/inline/man/cfunction.html" class="external-link">inline::cfunction()</a></code> if applicable to give detailed information about the C function being built.</p></dd> + + <dt>dll_dir</dt> <dd><p>Directory where an DLL object, if generated internally by <code><a href="https://rdrr.io/pkg/inline/man/cfunction.html" class="external-link">inline::cfunction()</a></code>, should be saved. The DLL will only be stored in a permanent location for use in future sessions, if 'dll_dir' and 'name' are specified.</p></dd> + + <dt>unload</dt> <dd><p>If a DLL from the target location in 'dll_dir' is already loaded, should that be unloaded first?</p></dd> + + <dt>overwrite</dt> <dd><p>If a file exists at the target DLL location in 'dll_dir', should this be overwritten?</p></dd> + + <dt>x</dt> <dd><p>An <code>mkinmod</code> object.</p></dd> + + <dt>submodel</dt> <dd><p>Character vector of length one to specify the submodel type. See <code>mkinmod</code> for the list of allowed submodel names.</p></dd> + + <dt>to</dt> <dd><p>Vector of the names of the state variable to which a transformation shall be included in the model.</p></dd> + + <dt>sink</dt> <dd><p>Should a pathway to sink be included in the model in addition to the pathways to other state variables?</p></dd> + + <dt>full_name</dt> <dd><p>An optional name to be used e.g. for plotting fits performed with the model. You can use non-ASCII characters here, but then your R code will not be portable, <em>i.e.</em> may produce unintended plot results on other operating systems or system configurations.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>A list of class <code>mkinmod</code> for use with <code><a href="mkinfit.html">mkinfit()</a></code>, + + +<p>A list of class <code>mkinmod</code> for use with <code><a href="mkinfit.html">mkinfit()</a></code>, containing, among others,</p> <dl><dt>diffs</dt> <dd><p>A vector of string representations of differential equations, one for each modelling variable.</p></dd> + <dt>map</dt> <dd><p>A list containing named character vectors for each observed variable, specifying the modelling variables by which it is represented.</p></dd> + <dt>use_of_ff</dt> <dd><p>The content of <code>use_of_ff</code> is passed on in this list component.</p></dd> + <dt>deg_func</dt> <dd><p>If generated, a function containing the solution of the degradation model.</p></dd> + <dt>coefmat</dt> <dd><p>The coefficient matrix, if the system of differential equations can be represented by one.</p></dd> + <dt>cf</dt> <dd><p>If generated, a compiled function calculating the derivatives as returned by cfunction.</p></dd> + + </dl><p>A list for use with <code>mkinmod</code>.</p> </div> <div id="details"> @@ -202,7 +247,7 @@ in the FOCUS and NAFTA guidance documents are used.</p> <p>For kinetic models with more than one observed variable, a symbolic solution of the system of differential equations is included in the resulting mkinmod object in some cases, speeding up the solution.</p> -<p>If a C compiler is found by <code><a href="https://rdrr.io/pkg/pkgbuild/man/has_compiler.html" class="external-link">pkgbuild::has_compiler()</a></code> and there +<p>If a C compiler is found by <code><a href="https://r-lib.github.io/pkgbuild/reference/has_compiler.html" class="external-link">pkgbuild::has_compiler()</a></code> and there is more than one observed variable in the specification, C code is generated for evaluating the differential equations, compiled using <code><a href="https://rdrr.io/pkg/inline/man/cfunction.html" class="external-link">inline::cfunction()</a></code> and added to the resulting mkinmod object.</p> @@ -230,16 +275,16 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"><span class="co"># Specify the SFO model (this is not needed any more, as we can now mkinfit("SFO", ...)</span></span> -<span class="r-in"><span class="va">SFO</span> <span class="op"><-</span> <span class="fu">mkinmod</span><span class="op">(</span>parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># One parent compound, one metabolite, both single first order</span></span> -<span class="r-in"><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu">mkinmod</span><span class="op">(</span></span> -<span class="r-in"> parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span> -<span class="r-in"> m1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Specify the SFO model (this is not needed any more, as we can now mkinfit("SFO", ...)</span></span></span> +<span class="r-in"><span><span class="va">SFO</span> <span class="op"><-</span> <span class="fu">mkinmod</span><span class="op">(</span>parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># One parent compound, one metabolite, both single first order</span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu">mkinmod</span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">SFO_SFO</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">SFO_SFO</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> <mkinmod> model generated with</span> <span class="r-out co"><span class="r-pr">#></span> Use of formation fractions $use_of_ff: max </span> <span class="r-out co"><span class="r-pr">#></span> Specification $spec:</span> @@ -252,30 +297,32 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> <span class="r-out co"><span class="r-pr">#></span> Differential equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - k_parent * parent</span> <span class="r-out co"><span class="r-pr">#></span> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1</span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"> <span class="va">fit_sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"> <span class="co"># Now supplying compound names used for plotting, and write to user defined location</span></span> -<span class="r-in"> <span class="co"># We need to choose a path outside the session tempdir because this gets removed</span></span> -<span class="r-in"> <span class="va">DLL_dir</span> <span class="op"><-</span> <span class="st">"~/.local/share/mkin"</span></span> -<span class="r-in"> <span class="kw">if</span> <span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.exists</a></span><span class="op">(</span><span class="va">DLL_dir</span><span class="op">)</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.create</a></span><span class="op">(</span><span class="va">DLL_dir</span><span class="op">)</span></span> -<span class="r-in"> <span class="va">SFO_SFO.2</span> <span class="op"><-</span> <span class="fu">mkinmod</span><span class="op">(</span></span> -<span class="r-in"> parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span>, full_name <span class="op">=</span> <span class="st">"Test compound"</span><span class="op">)</span>,</span> -<span class="r-in"> m1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, full_name <span class="op">=</span> <span class="st">"Metabolite M1"</span><span class="op">)</span>,</span> -<span class="r-in"> name <span class="op">=</span> <span class="st">"SFO_SFO"</span>, dll_dir <span class="op">=</span> <span class="va">DLL_dir</span>, unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-msg co"><span class="r-pr">#></span> Copied DLL from /tmp/Rtmp6NiOcv/fileb89c01ace19ec.so to /home/jranke/.local/share/mkin/SFO_SFO.so</span> -<span class="r-in"><span class="co"># Now we can save the model and restore it in a new session</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">saveRDS</a></span><span class="op">(</span><span class="va">SFO_SFO.2</span>, file <span class="op">=</span> <span class="st">"~/SFO_SFO.rds"</span><span class="op">)</span></span> -<span class="r-in"><span class="co"># Terminate the R session here if you would like to check, and then do</span></span> -<span class="r-in"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span> -<span class="r-in"><span class="va">SFO_SFO.3</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">readRDS</a></span><span class="op">(</span><span class="st">"~/SFO_SFO.rds"</span><span class="op">)</span></span> -<span class="r-in"><span class="va">fit_sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO.3</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># Show details of creating the C function</span></span> -<span class="r-in"><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu">mkinmod</span><span class="op">(</span></span> -<span class="r-in"> parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span> -<span class="r-in"> m1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, verbose <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span> <span class="va">fit_sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span><span class="op">)</span></span></span> +<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># Now supplying compound names used for plotting, and write to user defined location</span></span></span> +<span class="r-in"><span> <span class="co"># We need to choose a path outside the session tempdir because this gets removed</span></span></span> +<span class="r-in"><span> <span class="va">DLL_dir</span> <span class="op"><-</span> <span class="st">"~/.local/share/mkin"</span></span></span> +<span class="r-in"><span> <span class="kw">if</span> <span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.exists</a></span><span class="op">(</span><span class="va">DLL_dir</span><span class="op">)</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.create</a></span><span class="op">(</span><span class="va">DLL_dir</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">SFO_SFO.2</span> <span class="op"><-</span> <span class="fu">mkinmod</span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span>, full_name <span class="op">=</span> <span class="st">"Test compound"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, full_name <span class="op">=</span> <span class="st">"Metabolite M1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> name <span class="op">=</span> <span class="st">"SFO_SFO"</span>, dll_dir <span class="op">=</span> <span class="va">DLL_dir</span>, unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Copied DLL from /tmp/RtmpelWAOB/fileb43c31a25a86.so to /home/jranke/.local/share/mkin/SFO_SFO.so</span> +<span class="r-in"><span><span class="co"># Now we can save the model and restore it in a new session</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">saveRDS</a></span><span class="op">(</span><span class="va">SFO_SFO.2</span>, file <span class="op">=</span> <span class="st">"~/SFO_SFO.rds"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># Terminate the R session here if you would like to check, and then do</span></span></span> +<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO.3</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">readRDS</a></span><span class="op">(</span><span class="st">"~/SFO_SFO.rds"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">fit_sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO.3</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span><span class="op">)</span></span></span> +<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Show details of creating the C function</span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu">mkinmod</span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, verbose <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Program source:</span> <span class="r-out co"><span class="r-pr">#></span> 1: #include <R.h></span> <span class="r-out co"><span class="r-pr">#></span> 2: </span> @@ -297,10 +344,10 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> <span class="r-out co"><span class="r-pr">#></span> 18: f[1] = + f_parent_to_m1 * k_parent * y[0] - k_m1 * y[1];</span> <span class="r-out co"><span class="r-pr">#></span> 19: }</span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># The symbolic solution which is available in this case is not</span></span> -<span class="r-in"><span class="co"># made for human reading but for speed of computation</span></span> -<span class="r-in"><span class="va">SFO_SFO</span><span class="op">$</span><span class="va">deg_func</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># The symbolic solution which is available in this case is not</span></span></span> +<span class="r-in"><span><span class="co"># made for human reading but for speed of computation</span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO</span><span class="op">$</span><span class="va">deg_func</span></span></span> <span class="r-out co"><span class="r-pr">#></span> function (observed, odeini, odeparms) </span> <span class="r-out co"><span class="r-pr">#></span> {</span> <span class="r-out co"><span class="r-pr">#></span> predicted <- numeric(0)</span> @@ -316,21 +363,21 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> <span class="r-out co"><span class="r-pr">#></span> })</span> <span class="r-out co"><span class="r-pr">#></span> return(predicted)</span> <span class="r-out co"><span class="r-pr">#></span> }</span> -<span class="r-out co"><span class="r-pr">#></span> <environment: 0x55555cd83c70></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># If we have several parallel metabolites</span></span> -<span class="r-in"><span class="co"># (compare tests/testthat/test_synthetic_data_for_UBA_2014.R)</span></span> -<span class="r-in"><span class="va">m_synth_DFOP_par</span> <span class="op"><-</span> <span class="fu">mkinmod</span><span class="op">(</span></span> -<span class="r-in"> parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span> -<span class="r-in"> M1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> M2 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">fit_DFOP_par_c</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_DFOP_par</span>,</span> -<span class="r-in"> <span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>,</span> -<span class="r-in"> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="co"># }</span></span> -<span class="r-in"></span> +<span class="r-out co"><span class="r-pr">#></span> <environment: 0x55555f013820></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># If we have several parallel metabolites</span></span></span> +<span class="r-in"><span><span class="co"># (compare tests/testthat/test_synthetic_data_for_UBA_2014.R)</span></span></span> +<span class="r-in"><span><span class="va">m_synth_DFOP_par</span> <span class="op"><-</span> <span class="fu">mkinmod</span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">fit_DFOP_par_c</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_DFOP_par</span>,</span></span> +<span class="r-in"><span> <span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>,</span></span> +<span class="r-in"><span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -345,7 +392,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> 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parameters fitted using +mkinfit."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> - - +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - </head> - - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -73,23 +18,21 @@ mkinfit." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -99,112 +42,119 @@ mkinfit." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Function to plot the confidence intervals obtained using mkinfit</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/mkinparplot.R'><code>R/mkinparplot.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinparplot.R" class="external-link"><code>R/mkinparplot.R</code></a></small> <div class="hidden name"><code>mkinparplot.Rd</code></div> </div> <div class="ref-description"> <p>This function plots the confidence intervals for the parameters fitted using -<code><a href='mkinfit.html'>mkinfit</a></code>.</p> +<code><a href="mkinfit.html">mkinfit</a></code>.</p> </div> - <pre class="usage"><span class='fu'>mkinparplot</span><span class='op'>(</span><span class='va'>object</span><span class='op'>)</span></pre> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinparplot</span><span class="op">(</span><span class="va">object</span><span class="op">)</span></span></code></pre></div> + </div> - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>object</th> - <td><p>A fit represented in an <code><a href='mkinfit.html'>mkinfit</a></code> object.</p></td> - </tr> - </table> + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>object</dt> +<dd><p>A fit represented in an <code><a href="mkinfit.html">mkinfit</a></code> object.</p></dd> - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> +</dl></div> + <div id="value"> + <h2>Value</h2> + - <p>Nothing is returned by this function, as it is called for its side +<p>Nothing is returned by this function, as it is called for its side effect, namely to produce a plot.</p> - <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2> - + </div> + <div id="author"> + <h2>Author</h2> <p>Johannes Ranke</p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> -<span class='co'># \dontrun{</span> -<span class='va'>model</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span> - T245 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"phenol"</span><span class='op'>)</span>, sink <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span>, - phenol <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"anisole"</span><span class='op'>)</span><span class='op'>)</span>, - anisole <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span>, use_of_ff <span class='op'>=</span> <span class='st'>"max"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'><span class='va'>fit</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>model</span>, <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span><span class='op'>(</span><span class='va'>mccall81_245T</span>, <span class='va'>soil</span> <span class='op'>==</span> <span class='st'>"Commerce"</span><span class='op'>)</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#> <span class='warning'>Warning: Optimisation did not converge:</span> -#> <span class='warning'>false convergence (8)</span></div><div class='input'><span class='fu'>mkinparplot</span><span class='op'>(</span><span class='va'>fit</span><span class='op'>)</span> -</div><div class='img'><img src='mkinparplot-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># }</span> -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="va">model</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> T245 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"phenol"</span><span class="op">)</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> phenol <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"anisole"</span><span class="op">)</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> anisole <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">model</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">mccall81_245T</span>, <span class="va">soil</span> <span class="op">==</span> <span class="st">"Commerce"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span> +<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Optimisation did not converge:</span> +<span class="r-wrn co"><span class="r-pr">#></span> false convergence (8)</span> +<span class="r-in"><span><span class="fu">mkinparplot</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="mkinparplot-1.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="co"># }</span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" 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It now only calls the plot method -<code><a href='plot.mkinfit.html'>plot.mkinfit</a></code>.</p> +<code><a href="plot.mkinfit.html">plot.mkinfit</a></code>.</p> + </div> + + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinplot</span><span class="op">(</span><span class="va">fit</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> </div> - <pre class="usage"><span class='fu'>mkinplot</span><span class='op'>(</span><span class='va'>fit</span>, <span class='va'>...</span><span class='op'>)</span></pre> + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>fit</dt> +<dd><p>an object of class <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd> - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>fit</th> - <td><p>an object of class <code><a href='mkinfit.html'>mkinfit</a></code>.</p></td> - </tr> - <tr> - <th>...</th> - <td><p>further arguments passed to <code><a href='plot.mkinfit.html'>plot.mkinfit</a></code>.</p></td> - </tr> - </table> - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> +<dt>...</dt> +<dd><p>further arguments passed to <code><a href="plot.mkinfit.html">plot.mkinfit</a></code>.</p></dd> - <p>The function is called for its side effect.</p> - <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2> +</dl></div> + <div id="value"> + <h2>Value</h2> + +<p>The function is called for its side effect.</p> + </div> + <div id="author"> + <h2>Author</h2> <p>Johannes Ranke</p> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/mkinpredict.html b/docs/dev/reference/mkinpredict.html index 37930912..10d2c9a9 100644 --- a/docs/dev/reference/mkinpredict.html +++ b/docs/dev/reference/mkinpredict.html @@ -19,7 +19,7 @@ kinetic parameters and initial values for the state variables."><meta name="robo </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -372,12 +372,11 @@ as these always return mapped output.</p></dd> <span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">20</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span></span> <span class="r-in"><span> solution_type <span class="op">=</span> <span class="st">"analytical"</span>, use_compiled <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">[</span><span class="fl">201</span>,<span class="op">]</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="op">}</span></span></span> -<span class="r-msg co"><span class="r-pr">#></span> Loading required package: rbenchmark</span> <span class="r-out co"><span class="r-pr">#></span> test relative elapsed</span> <span class="r-out co"><span class="r-pr">#></span> 2 deSolve_compiled 1.0 0.005</span> -<span class="r-out co"><span class="r-pr">#></span> 1 eigen 4.2 0.021</span> -<span class="r-out co"><span class="r-pr">#></span> 4 analytical 4.2 0.021</span> -<span class="r-out co"><span class="r-pr">#></span> 3 deSolve 40.8 0.204</span> +<span class="r-out co"><span class="r-pr">#></span> 4 analytical 1.0 0.005</span> +<span class="r-out co"><span class="r-pr">#></span> 1 eigen 4.4 0.022</span> +<span class="r-out co"><span class="r-pr">#></span> 3 deSolve 41.0 0.205</span> <span class="r-in"><span></span></span> <span class="r-in"><span><span class="co"># \dontrun{</span></span></span> <span class="r-in"><span> <span class="co"># Predict from a fitted model</span></span></span> diff --git a/docs/dev/reference/mkinresplot-1.png b/docs/dev/reference/mkinresplot-1.png Binary files differindex ffd34f6f..97ccd762 100644 --- a/docs/dev/reference/mkinresplot-1.png +++ b/docs/dev/reference/mkinresplot-1.png diff --git a/docs/dev/reference/mkinresplot.html b/docs/dev/reference/mkinresplot.html index 30377f2c..4d99f5be 100644 --- a/docs/dev/reference/mkinresplot.html +++ b/docs/dev/reference/mkinresplot.html @@ -1,70 +1,15 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - <head> - <meta charset="utf-8"> -<meta http-equiv="X-UA-Compatible" content="IE=edge"> -<meta name="viewport" content="width=device-width, initial-scale=1.0"> - -<title>Function to plot residuals stored in an mkin object — mkinresplot • mkin</title> - - -<!-- jquery --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script> -<!-- Bootstrap --> - -<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous" /> - -<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script> - -<!-- bootstrap-toc --> -<link rel="stylesheet" href="../bootstrap-toc.css"> -<script src="../bootstrap-toc.js"></script> - 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A combined plot of the fitted model and the residuals can be obtained using plot.mkinfit using the -argument show_residuals = TRUE." /> - - -<meta name="robots" content="noindex"> - -<!-- mathjax --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script> - -<!--[if lt IE 9]> +argument show_residuals = TRUE."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - - - </head> - - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -75,23 +20,21 @@ argument show_residuals = TRUE." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -101,181 +44,188 @@ argument show_residuals = TRUE." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Function to plot residuals stored in an mkin object</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/mkinresplot.R'><code>R/mkinresplot.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mkinresplot.R" class="external-link"><code>R/mkinresplot.R</code></a></small> <div class="hidden name"><code>mkinresplot.Rd</code></div> </div> <div class="ref-description"> <p>This function plots the residuals for the specified subset of the observed variables from an mkinfit object. A combined plot of the fitted model and -the residuals can be obtained using <code><a href='plot.mkinfit.html'>plot.mkinfit</a></code> using the +the residuals can be obtained using <code><a href="plot.mkinfit.html">plot.mkinfit</a></code> using the argument <code>show_residuals = TRUE</code>.</p> </div> - <pre class="usage"><span class='fu'>mkinresplot</span><span class='op'>(</span> - <span class='va'>object</span>, - obs_vars <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span><span class='op'>(</span><span class='va'>object</span><span class='op'>$</span><span class='va'>mkinmod</span><span class='op'>$</span><span class='va'>map</span><span class='op'>)</span>, - xlim <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>1.1</span> <span class='op'>*</span> <span class='fu'><a href='https://rdrr.io/r/base/Extremes.html'>max</a></span><span class='op'>(</span><span class='va'>object</span><span class='op'>$</span><span class='va'>data</span><span class='op'>$</span><span class='va'>time</span><span class='op'>)</span><span class='op'>)</span>, - standardized <span class='op'>=</span> <span class='cn'>FALSE</span>, - xlab <span class='op'>=</span> <span class='st'>"Time"</span>, - ylab <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/ifelse.html'>ifelse</a></span><span class='op'>(</span><span class='va'>standardized</span>, <span class='st'>"Standardized residual"</span>, <span class='st'>"Residual"</span><span class='op'>)</span>, - maxabs <span class='op'>=</span> <span class='st'>"auto"</span>, - legend <span class='op'>=</span> <span class='cn'>TRUE</span>, - lpos <span class='op'>=</span> <span class='st'>"topright"</span>, - col_obs <span class='op'>=</span> <span class='st'>"auto"</span>, - pch_obs <span class='op'>=</span> <span class='st'>"auto"</span>, - frame <span class='op'>=</span> <span class='cn'>TRUE</span>, - <span class='va'>...</span> -<span class='op'>)</span></pre> - - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>object</th> - <td><p>A fit represented in an <code><a href='mkinfit.html'>mkinfit</a></code> object.</p></td> - </tr> - <tr> - <th>obs_vars</th> - <td><p>A character vector of names of the observed variables for + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinresplot</span><span class="op">(</span></span> +<span> <span class="va">object</span>,</span> +<span> obs_vars <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">object</span><span class="op">$</span><span class="va">mkinmod</span><span class="op">$</span><span class="va">map</span><span class="op">)</span>,</span> +<span> xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1.1</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="va">object</span><span class="op">$</span><span class="va">data</span><span class="op">$</span><span class="va">time</span><span class="op">)</span><span class="op">)</span>,</span> +<span> standardized <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> xlab <span class="op">=</span> <span class="st">"Time"</span>,</span> +<span> ylab <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">standardized</span>, <span class="st">"Standardized residual"</span>, <span class="st">"Residual"</span><span class="op">)</span>,</span> +<span> maxabs <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> legend <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> lpos <span class="op">=</span> <span class="st">"topright"</span>,</span> +<span> col_obs <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> pch_obs <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> frame <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>object</dt> +<dd><p>A fit represented in an <code><a href="mkinfit.html">mkinfit</a></code> object.</p></dd> + + +<dt>obs_vars</dt> +<dd><p>A character vector of names of the observed variables for which residuals should be plotted. Defaults to all observed variables in -the model</p></td> - </tr> - <tr> - <th>xlim</th> - <td><p>plot range in x direction.</p></td> - </tr> - <tr> - <th>standardized</th> - <td><p>Should the residuals be standardized by dividing by the -standard deviation given by the error model of the fit?</p></td> - </tr> - <tr> - <th>xlab</th> - <td><p>Label for the x axis.</p></td> - </tr> - <tr> - <th>ylab</th> - <td><p>Label for the y axis.</p></td> - </tr> - <tr> - <th>maxabs</th> - <td><p>Maximum absolute value of the residuals. This is used for the -scaling of the y axis and defaults to "auto".</p></td> - </tr> - <tr> - <th>legend</th> - <td><p>Should a legend be plotted?</p></td> - </tr> - <tr> - <th>lpos</th> - <td><p>Where should the legend be placed? Default is "topright". Will -be passed on to <code><a href='https://rdrr.io/r/graphics/legend.html'>legend</a></code>.</p></td> - </tr> - <tr> - <th>col_obs</th> - <td><p>Colors for the observed variables.</p></td> - </tr> - <tr> - <th>pch_obs</th> - <td><p>Symbols to be used for the observed variables.</p></td> - </tr> - <tr> - <th>frame</th> - <td><p>Should a frame be drawn around the plots?</p></td> - </tr> - <tr> - <th>...</th> - <td><p>further arguments passed to <code><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></code>.</p></td> - </tr> - </table> - - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> - - <p>Nothing is returned by this function, as it is called for its side -effect, namely to produce a plot.</p> - <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2> +the model</p></dd> - <div class='dont-index'><p><code><a href='mkinplot.html'>mkinplot</a></code>, for a way to plot the data and the fitted -lines of the mkinfit object, and <code><a href='plot.mkinfit.html'>plot_res</a></code> for a function -combining the plot of the fit and the residual plot.</p></div> - <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2> +<dt>xlim</dt> +<dd><p>plot range in x direction.</p></dd> + + +<dt>standardized</dt> +<dd><p>Should the residuals be standardized by dividing by the +standard deviation given by the error model of the fit?</p></dd> + + +<dt>xlab</dt> +<dd><p>Label for the x axis.</p></dd> + + +<dt>ylab</dt> +<dd><p>Label for the y axis.</p></dd> + + +<dt>maxabs</dt> +<dd><p>Maximum absolute value of the residuals. This is used for the +scaling of the y axis and defaults to "auto".</p></dd> + + +<dt>legend</dt> +<dd><p>Should a legend be plotted?</p></dd> + + +<dt>lpos</dt> +<dd><p>Where should the legend be placed? Default is "topright". Will +be passed on to <code><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></code>.</p></dd> + + +<dt>col_obs</dt> +<dd><p>Colors for the observed variables.</p></dd> + + +<dt>pch_obs</dt> +<dd><p>Symbols to be used for the observed variables.</p></dd> + + +<dt>frame</dt> +<dd><p>Should a frame be drawn around the plots?</p></dd> + + +<dt>...</dt> +<dd><p>further arguments passed to <code><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></code>.</p></dd> + +</dl></div> + <div id="value"> + <h2>Value</h2> + + +<p>Nothing is returned by this function, as it is called for its side +effect, namely to produce a plot.</p> + </div> + <div id="see-also"> + <h2>See also</h2> + <div class="dont-index"><p><code><a href="mkinplot.html">mkinplot</a></code>, for a way to plot the data and the fitted +lines of the mkinfit object, and <code><a href="plot.mkinfit.html">plot_res</a></code> for a function +combining the plot of the fit and the residual plot.</p></div> + </div> + <div id="author"> + <h2>Author</h2> <p>Johannes Ranke and Katrin Lindenberger</p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> -<span class='va'>model</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"m1"</span><span class='op'>)</span>, m1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'><span class='va'>fit</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>model</span>, <span class='va'>FOCUS_2006_D</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'>mkinresplot</span><span class='op'>(</span><span class='va'>fit</span>, <span class='st'>"m1"</span><span class='op'>)</span> -</div><div class='img'><img src='mkinresplot-1.png' alt='' width='700' height='433' /></div><div class='input'> -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">model</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span> +<span class="r-in"><span><span class="fu">mkinresplot</span><span class="op">(</span><span class="va">fit</span>, <span class="st">"m1"</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="mkinresplot-1.png" alt="" width="700" height="433"></span> +<span class="r-in"><span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/mmkin-1.png b/docs/dev/reference/mmkin-1.png Binary files differindex 701a6d6a..8ad9c11d 100644 --- a/docs/dev/reference/mmkin-1.png +++ b/docs/dev/reference/mmkin-1.png diff --git a/docs/dev/reference/mmkin-2.png b/docs/dev/reference/mmkin-2.png Binary files differindex 5277b389..da2a48a8 100644 --- a/docs/dev/reference/mmkin-2.png +++ b/docs/dev/reference/mmkin-2.png diff --git a/docs/dev/reference/mmkin-3.png b/docs/dev/reference/mmkin-3.png Binary files differindex 2659cd61..10d3f35b 100644 --- a/docs/dev/reference/mmkin-3.png +++ b/docs/dev/reference/mmkin-3.png diff --git a/docs/dev/reference/mmkin-4.png b/docs/dev/reference/mmkin-4.png Binary files differindex ae16ee79..132380a8 100644 --- a/docs/dev/reference/mmkin-4.png +++ b/docs/dev/reference/mmkin-4.png diff --git a/docs/dev/reference/mmkin-5.png b/docs/dev/reference/mmkin-5.png Binary files differindex 2b9dc831..4bfcc55e 100644 --- a/docs/dev/reference/mmkin-5.png +++ b/docs/dev/reference/mmkin-5.png diff --git a/docs/dev/reference/mmkin.html b/docs/dev/reference/mmkin.html index c385bbf6..5aa259f9 100644 --- a/docs/dev/reference/mmkin.html +++ b/docs/dev/reference/mmkin.html @@ -1,70 +1,15 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - 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- - - </head> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -75,23 +20,21 @@ datasets specified in its first two arguments." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -101,213 +44,225 @@ datasets specified in its first two arguments." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Fit one or more kinetic models with one or more state variables to one or more datasets</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/mmkin.R'><code>R/mmkin.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/mmkin.R" class="external-link"><code>R/mmkin.R</code></a></small> <div class="hidden name"><code>mmkin.Rd</code></div> </div> <div class="ref-description"> - <p>This function calls <code><a href='mkinfit.html'>mkinfit</a></code> on all combinations of models and + <p>This function calls <code><a href="mkinfit.html">mkinfit</a></code> on all combinations of models and datasets specified in its first two arguments.</p> </div> - <pre class="usage"><span class='fu'>mmkin</span><span class='op'>(</span> - models <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"FOMC"</span>, <span class='st'>"DFOP"</span><span class='op'>)</span>, - <span class='va'>datasets</span>, - cores <span class='op'>=</span> <span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/Sys.info.html'>Sys.info</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>[</span><span class='st'>"sysname"</span><span class='op'>]</span> <span class='op'>==</span> <span class='st'>"Windows"</span><span class='op'>)</span> <span class='fl'>1</span> <span class='kw'>else</span> <span class='fu'>parallel</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/r/parallel/detectCores.html'>detectCores</a></span><span class='op'>(</span><span class='op'>)</span>, - cluster <span class='op'>=</span> <span class='cn'>NULL</span>, - <span class='va'>...</span> -<span class='op'>)</span> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mmkin</span><span class="op">(</span></span> +<span> models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>,</span> +<span> <span class="va">datasets</span>,</span> +<span> cores <span class="op">=</span> <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="fl">1</span> <span class="kw">else</span> <span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span>,</span> +<span> cluster <span class="op">=</span> <span class="cn">NULL</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>models</dt> +<dd><p>Either a character vector of shorthand names like +<code>c("SFO", "FOMC", "DFOP", "HS", "SFORB")</code>, or an optionally named +list of <code><a href="mkinmod.html">mkinmod</a></code> objects.</p></dd> + -<span class='co'># S3 method for mmkin</span> -<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>...</span><span class='op'>)</span></pre> +<dt>datasets</dt> +<dd><p>An optionally named list of datasets suitable as observed +data for <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd> - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>models</th> - <td><p>Either a character vector of shorthand names like -<code><a href='https://rdrr.io/r/base/c.html'>c("SFO", "FOMC", "DFOP", "HS", "SFORB")</a></code>, or an optionally named -list of <code><a href='mkinmod.html'>mkinmod</a></code> objects.</p></td> - </tr> - <tr> - <th>datasets</th> - <td><p>An optionally named list of datasets suitable as observed -data for <code><a href='mkinfit.html'>mkinfit</a></code>.</p></td> - </tr> - <tr> - <th>cores</th> - <td><p>The number of cores to be used for multicore processing. This + +<dt>cores</dt> +<dd><p>The number of cores to be used for multicore processing. This is only used when the <code>cluster</code> argument is <code>NULL</code>. On Windows machines, cores > 1 is not supported, you need to use the <code>cluster</code> argument to use multiple logical processors. Per default, all cores -detected by <code><a href='https://rdrr.io/r/parallel/detectCores.html'>parallel::detectCores()</a></code> are used, except on Windows where -the default is 1.</p></td> - </tr> - <tr> - <th>cluster</th> - <td><p>A cluster as returned by <code>makeCluster</code> to be used -for parallel execution.</p></td> - </tr> - <tr> - <th>...</th> - <td><p>Not used.</p></td> - </tr> - <tr> - <th>x</th> - <td><p>An mmkin object.</p></td> - </tr> - </table> +detected by <code><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">parallel::detectCores()</a></code> are used, except on Windows where +the default is 1.</p></dd> - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> - <p>A two-dimensional <code><a href='https://rdrr.io/r/base/array.html'>array</a></code> of <code><a href='mkinfit.html'>mkinfit</a></code> -objects and/or try-errors that can be indexed using the model names for the -first index (row index) and the dataset names for the second index (column -index).</p> - <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2> +<dt>cluster</dt> +<dd><p>A cluster as returned by <code>makeCluster</code> to be used +for parallel execution.</p></dd> - <div class='dont-index'><p><code><a href='[.mmkin.html'>[.mmkin</a></code> for subsetting, <code><a href='plot.mmkin.html'>plot.mmkin</a></code> for -plotting.</p></div> - <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2> - <p>Johannes Ranke</p> +<dt>...</dt> +<dd><p>Not used.</p></dd> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> -<span class='co'># \dontrun{</span> -<span class='va'>m_synth_SFO_lin</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"M1"</span><span class='op'>)</span>, - M1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"M2"</span><span class='op'>)</span>, - M2 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span>, use_of_ff <span class='op'>=</span> <span class='st'>"max"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'> -<span class='va'>m_synth_FOMC_lin</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"FOMC"</span>, <span class='st'>"M1"</span><span class='op'>)</span>, - M1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"M2"</span><span class='op'>)</span>, - M2 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span>, use_of_ff <span class='op'>=</span> <span class='st'>"max"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'> -<span class='va'>models</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>SFO_lin <span class='op'>=</span> <span class='va'>m_synth_SFO_lin</span>, FOMC_lin <span class='op'>=</span> <span class='va'>m_synth_FOMC_lin</span><span class='op'>)</span> -<span class='va'>datasets</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='va'>synthetic_data_for_UBA_2014</span><span class='op'>[</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>3</span><span class='op'>]</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span> <span class='va'>x</span><span class='op'>$</span><span class='va'>data</span><span class='op'>)</span> -<span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span><span class='op'>(</span><span class='va'>datasets</span><span class='op'>)</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span><span class='op'>(</span><span class='st'>"Dataset"</span>, <span class='fl'>1</span><span class='op'>:</span><span class='fl'>3</span><span class='op'>)</span> -<span class='va'>time_default</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span><span class='op'>(</span><span class='va'>fits.0</span> <span class='op'><-</span> <span class='fu'>mmkin</span><span class='op'>(</span><span class='va'>models</span>, <span class='va'>datasets</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> -<span class='va'>time_1</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span><span class='op'>(</span><span class='va'>fits.4</span> <span class='op'><-</span> <span class='fu'>mmkin</span><span class='op'>(</span><span class='va'>models</span>, <span class='va'>datasets</span>, cores <span class='op'>=</span> <span class='fl'>1</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> +<dt>x</dt> +<dd><p>An mmkin object.</p></dd> -<span class='va'>time_default</span> -</div><div class='output co'>#> user system elapsed -#> 4.771 0.576 1.803 </div><div class='input'><span class='va'>time_1</span> -</div><div class='output co'>#> user system elapsed -#> 5.779 0.000 5.781 </div><div class='input'> -<span class='fu'><a href='endpoints.html'>endpoints</a></span><span class='op'>(</span><span class='va'>fits.0</span><span class='op'>[[</span><span class='st'>"SFO_lin"</span>, <span class='fl'>2</span><span class='op'>]</span><span class='op'>]</span><span class='op'>)</span> -</div><div class='output co'>#> $ff -#> parent_M1 parent_sink M1_M2 M1_sink -#> 0.7340481 0.2659519 0.7505683 0.2494317 -#> -#> $distimes -#> DT50 DT90 -#> parent 0.877769 2.915885 -#> M1 2.325744 7.725956 -#> M2 33.720100 112.015749 -#> </div><div class='input'> -<span class='co'># plot.mkinfit handles rows or columns of mmkin result objects</span> -<span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>fits.0</span><span class='op'>[</span><span class='fl'>1</span>, <span class='op'>]</span><span class='op'>)</span> -</div><div class='img'><img src='mmkin-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>fits.0</span><span class='op'>[</span><span class='fl'>1</span>, <span class='op'>]</span>, obs_var <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"M1"</span>, <span class='st'>"M2"</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='img'><img src='mmkin-2.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>fits.0</span><span class='op'>[</span>, <span class='fl'>1</span><span class='op'>]</span><span class='op'>)</span> -</div><div class='img'><img src='mmkin-3.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># Use double brackets to extract a single mkinfit object, which will be plotted</span> -<span class='co'># by plot.mkinfit and can be plotted using plot_sep</span> -<span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>fits.0</span><span class='op'>[[</span><span class='fl'>1</span>, <span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span>, sep_obs <span class='op'>=</span> <span class='cn'>TRUE</span>, show_residuals <span class='op'>=</span> <span class='cn'>TRUE</span>, show_errmin <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -</div><div class='img'><img src='mmkin-4.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span><span class='op'>(</span><span class='va'>fits.0</span><span class='op'>[[</span><span class='fl'>1</span>, <span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span><span class='op'>)</span> -<span class='co'># Plotting with mmkin (single brackets, extracting an mmkin object) does not</span> -<span class='co'># allow to plot the observed variables separately</span> -<span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>fits.0</span><span class='op'>[</span><span class='fl'>1</span>, <span class='fl'>1</span><span class='op'>]</span><span class='op'>)</span> -</div><div class='img'><img src='mmkin-5.png' alt='' width='700' height='433' /></div><div class='input'> -<span class='co'># On Windows, we can use multiple cores by making a cluster using the parallel</span> -<span class='co'># package, which gets loaded with mkin, and passing it to mmkin, e.g.</span> -<span class='va'>cl</span> <span class='op'><-</span> <span class='fu'>makePSOCKcluster</span><span class='op'>(</span><span class='fl'>12</span><span class='op'>)</span> -<span class='va'>f</span> <span class='op'><-</span> <span class='fu'>mmkin</span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"FOMC"</span>, <span class='st'>"DFOP"</span><span class='op'>)</span>, - <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>A <span class='op'>=</span> <span class='va'>FOCUS_2006_A</span>, B <span class='op'>=</span> <span class='va'>FOCUS_2006_B</span>, C <span class='op'>=</span> <span class='va'>FOCUS_2006_C</span>, D <span class='op'>=</span> <span class='va'>FOCUS_2006_D</span><span class='op'>)</span>, - cluster <span class='op'>=</span> <span class='va'>cl</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>f</span><span class='op'>)</span> -</div><div class='output co'>#> <mmkin> object -#> Status of individual fits: -#> -#> dataset -#> model A B C D -#> SFO OK OK OK OK -#> FOMC OK OK OK OK -#> DFOP OK OK OK OK -#> -#> OK: No warnings</div><div class='input'><span class='co'># We get false convergence for the FOMC fit to FOCUS_2006_A because this</span> -<span class='co'># dataset is really SFO, and the FOMC fit is overparameterised</span> -<span class='fu'>stopCluster</span><span class='op'>(</span><span class='va'>cl</span><span class='op'>)</span> -<span class='co'># }</span> +</dl></div> + <div id="value"> + <h2>Value</h2> + -</div></pre> +<p>A two-dimensional <code><a href="https://rdrr.io/r/base/array.html" class="external-link">array</a></code> of <code><a href="mkinfit.html">mkinfit</a></code></p> + + +<p>objects and/or try-errors that can be indexed using the model names for the +first index (row index) and the dataset names for the second index (column +index).</p> + </div> + <div id="see-also"> + <h2>See also</h2> + <div class="dont-index"><p><code><a href="Extract.mmkin.html">[.mmkin</a></code> for subsetting, <code><a href="plot.mmkin.html">plot.mmkin</a></code> for +plotting.</p></div> + </div> + <div id="author"> + <h2>Author</h2> + <p>Johannes Ranke</p> + </div> + + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="va">m_synth_SFO_lin</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">m_synth_FOMC_lin</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="st">"M1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">models</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>SFO_lin <span class="op">=</span> <span class="va">m_synth_SFO_lin</span>, FOMC_lin <span class="op">=</span> <span class="va">m_synth_FOMC_lin</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">datasets</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">synthetic_data_for_UBA_2014</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">]</span>, <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">datasets</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">time_default</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">fits.0</span> <span class="op"><-</span> <span class="fu">mmkin</span><span class="op">(</span><span class="va">models</span>, <span class="va">datasets</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">time_1</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">fits.4</span> <span class="op"><-</span> <span class="fu">mmkin</span><span class="op">(</span><span class="va">models</span>, <span class="va">datasets</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">time_default</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> user system elapsed </span> +<span class="r-out co"><span class="r-pr">#></span> 7.113 0.837 2.580 </span> +<span class="r-in"><span><span class="va">time_1</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> user system elapsed </span> +<span class="r-out co"><span class="r-pr">#></span> 5.617 0.008 5.626 </span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[[</span><span class="st">"SFO_lin"</span>, <span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> $ff</span> +<span class="r-out co"><span class="r-pr">#></span> parent_M1 parent_sink M1_M2 M1_sink </span> +<span class="r-out co"><span class="r-pr">#></span> 0.7340481 0.2659519 0.7505683 0.2494317 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> $distimes</span> +<span class="r-out co"><span class="r-pr">#></span> DT50 DT90</span> +<span class="r-out co"><span class="r-pr">#></span> parent 0.877769 2.915885</span> +<span class="r-out co"><span class="r-pr">#></span> M1 2.325744 7.725956</span> +<span class="r-out co"><span class="r-pr">#></span> M2 33.720100 112.015749</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># plot.mkinfit handles rows or columns of mmkin result objects</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[</span><span class="fl">1</span>, <span class="op">]</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="mmkin-1.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[</span><span class="fl">1</span>, <span class="op">]</span>, obs_var <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="mmkin-2.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[</span>, <span class="fl">1</span><span class="op">]</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="mmkin-3.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="co"># Use double brackets to extract a single mkinfit object, which will be plotted</span></span></span> +<span class="r-in"><span><span class="co"># by plot.mkinfit and can be plotted using plot_sep</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[[</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, sep_obs <span class="op">=</span> <span class="cn">TRUE</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="mmkin-4.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[[</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># Plotting with mmkin (single brackets, extracting an mmkin object) does not</span></span></span> +<span class="r-in"><span><span class="co"># allow to plot the observed variables separately</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">]</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="mmkin-5.png" alt="" width="700" height="433"></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># On Windows, we can use multiple cores by making a cluster using the parallel</span></span></span> +<span class="r-in"><span><span class="co"># package, which gets loaded with mkin, and passing it to mmkin, e.g.</span></span></span> +<span class="r-in"><span><span class="va">cl</span> <span class="op"><-</span> <span class="fu">makePSOCKcluster</span><span class="op">(</span><span class="fl">12</span><span class="op">)</span></span></span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in makePSOCKcluster(12):</span> could not find function "makePSOCKcluster"</span> +<span class="r-in"><span><span class="va">f</span> <span class="op"><-</span> <span class="fu">mmkin</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>A <span class="op">=</span> <span class="va">FOCUS_2006_A</span>, B <span class="op">=</span> <span class="va">FOCUS_2006_B</span>, C <span class="op">=</span> <span class="va">FOCUS_2006_C</span>, D <span class="op">=</span> <span class="va">FOCUS_2006_D</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> cluster <span class="op">=</span> <span class="va">cl</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in system.time({ if (is.null(cluster)) { results <- parallel::mclapply(as.list(1:n.fits), fit_function, mc.cores = cores, mc.preschedule = FALSE) } else { results <- parallel::parLapply(cluster, as.list(1:n.fits), fit_function) }}):</span> object 'cl' not found</span> +<span class="r-msg co"><span class="r-pr">#></span> Timing stopped at: 0 0 0.001</span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span></span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in print(f):</span> object 'f' not found</span> +<span class="r-in"><span><span class="co"># We get false convergence for the FOMC fit to FOCUS_2006_A because this</span></span></span> +<span class="r-in"><span><span class="co"># dataset is really SFO, and the FOMC fit is overparameterised</span></span></span> +<span class="r-in"><span><span class="fu">stopCluster</span><span class="op">(</span><span class="va">cl</span><span class="op">)</span></span></span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in stopCluster(cl):</span> could not find function "stopCluster"</span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/multistart-1.png b/docs/dev/reference/multistart-1.png Binary files differindex 7fc24a04..dcd493c9 100644 --- a/docs/dev/reference/multistart-1.png +++ b/docs/dev/reference/multistart-1.png diff --git a/docs/dev/reference/multistart-2.png b/docs/dev/reference/multistart-2.png Binary files differindex 7553a51a..e1983f12 100644 --- a/docs/dev/reference/multistart-2.png +++ b/docs/dev/reference/multistart-2.png diff --git a/docs/dev/reference/multistart.html b/docs/dev/reference/multistart.html index 3f5c4b35..0f2988bd 100644 --- a/docs/dev/reference/multistart.html +++ b/docs/dev/reference/multistart.html @@ -22,7 +22,7 @@ mixed-effects models by Duchesne et al (2021)."><meta name="robots" content="noi </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -64,7 +64,10 @@ mixed-effects models by Duchesne et al (2021)."><meta name="robots" content="noi <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/nafta-1.png b/docs/dev/reference/nafta-1.png Binary files differindex 4f0d7833..5d2d434b 100644 --- a/docs/dev/reference/nafta-1.png +++ b/docs/dev/reference/nafta-1.png diff --git a/docs/dev/reference/nafta.html b/docs/dev/reference/nafta.html index 6fb797a5..eafbca7f 100644 --- a/docs/dev/reference/nafta.html +++ b/docs/dev/reference/nafta.html @@ -1,71 +1,16 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - <head> - <meta charset="utf-8"> -<meta http-equiv="X-UA-Compatible" content="IE=edge"> -<meta name="viewport" content="width=device-width, initial-scale=1.0"> - -<title>Evaluate parent kinetics using the NAFTA guidance — nafta • mkin</title> - - -<!-- jquery --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script> -<!-- Bootstrap --> - -<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous" /> - -<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script> - -<!-- bootstrap-toc --> -<link rel="stylesheet" href="../bootstrap-toc.css"> -<script 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src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Evaluate parent kinetics using the NAFTA guidance — nafta"><meta property="og:description" content="The function fits the SFO, IORE and DFOP models using mmkin and returns an object of class nafta that has methods for printing and plotting. Print nafta objects. The results for the three models are printed in the -order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP." /> - - -<meta name="robots" content="noindex"> - -<!-- mathjax --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script> - -<!--[if lt IE 9]> +order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> - +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - - </head> - - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -76,23 +21,21 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -102,188 +45,198 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Evaluate parent kinetics using the NAFTA guidance</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/nafta.R'><code>R/nafta.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nafta.R" class="external-link"><code>R/nafta.R</code></a></small> <div class="hidden name"><code>nafta.Rd</code></div> </div> <div class="ref-description"> - <p>The function fits the SFO, IORE and DFOP models using <code><a href='mmkin.html'>mmkin</a></code> + <p>The function fits the SFO, IORE and DFOP models using <code><a href="mmkin.html">mmkin</a></code> and returns an object of class <code>nafta</code> that has methods for printing and plotting.</p> <p>Print nafta objects. The results for the three models are printed in the order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP.</p> </div> - <pre class="usage"><span class='fu'>nafta</span><span class='op'>(</span><span class='va'>ds</span>, title <span class='op'>=</span> <span class='cn'>NA</span>, quiet <span class='op'>=</span> <span class='cn'>FALSE</span>, <span class='va'>...</span><span class='op'>)</span> - -<span class='co'># S3 method for nafta</span> -<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>x</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span>, digits <span class='op'>=</span> <span class='fl'>3</span>, <span class='va'>...</span><span class='op'>)</span></pre> - - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>ds</th> - <td><p>A dataframe that must contain one variable called "time" with the -time values specified by the <code>time</code> argument, one column called -"name" with the grouping of the observed values, and finally one column of -observed values called "value".</p></td> - </tr> - <tr> - <th>title</th> - <td><p>Optional title of the dataset</p></td> - </tr> - <tr> - <th>quiet</th> - <td><p>Should the evaluation text be shown?</p></td> - </tr> - <tr> - <th>...</th> - <td><p>Further arguments passed to <code><a href='mmkin.html'>mmkin</a></code> (not for the -printing method).</p></td> - </tr> - <tr> - <th>x</th> - <td><p>An <code>nafta</code> object.</p></td> - </tr> - <tr> - <th>digits</th> - <td><p>Number of digits to be used for printing parameters and -dissipation times.</p></td> - </tr> - </table> - - <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">nafta</span><span class="op">(</span><span class="va">ds</span>, title <span class="op">=</span> <span class="cn">NA</span>, quiet <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for nafta</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, digits <span class="op">=</span> <span class="fl">3</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> + </div> + <div id="source"> + <h2>Source</h2> <p>NAFTA (2011) Guidance for evaluating and calculating degradation kinetics in environmental media. NAFTA Technical Working Group on Pesticides -<a href='https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation'>https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation</a> +<a href="https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation" class="external-link">https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation</a> accessed 2019-02-22</p> <p>US EPA (2015) Standard Operating Procedure for Using the NAFTA Guidance to Calculate Representative Half-life Values and Characterizing Pesticide Degradation -<a href='https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance'>https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance</a></p> - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> +<a href="https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance" class="external-link">https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance</a></p> + </div> + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>ds</dt> +<dd><p>A dataframe that must contain one variable called "time" with the +time values specified by the <code>time</code> argument, one column called +"name" with the grouping of the observed values, and finally one column of +observed values called "value".</p></dd> + + +<dt>title</dt> +<dd><p>Optional title of the dataset</p></dd> + - <p>An list of class <code>nafta</code>. The list element named "mmkin" is the -<code><a href='mmkin.html'>mmkin</a></code> object containing the fits of the three models. The +<dt>quiet</dt> +<dd><p>Should the evaluation text be shown?</p></dd> + + +<dt>...</dt> +<dd><p>Further arguments passed to <code><a href="mmkin.html">mmkin</a></code> (not for the +printing method).</p></dd> + + +<dt>x</dt> +<dd><p>An <code>nafta</code> object.</p></dd> + + +<dt>digits</dt> +<dd><p>Number of digits to be used for printing parameters and +dissipation times.</p></dd> + +</dl></div> + <div id="value"> + <h2>Value</h2> + + +<p>An list of class <code>nafta</code>. The list element named "mmkin" is the +<code><a href="mmkin.html">mmkin</a></code> object containing the fits of the three models. The list element named "title" contains the title of the dataset used. The list element "data" contains the dataset used in the fits.</p> - <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2> - + </div> + <div id="author"> + <h2>Author</h2> <p>Johannes Ranke</p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> - <span class='va'>nafta_evaluation</span> <span class='op'><-</span> <span class='fu'>nafta</span><span class='op'>(</span><span class='va'>NAFTA_SOP_Appendix_D</span>, cores <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></div><div class='output co'>#> <span class='message'>The representative half-life of the IORE model is longer than the one corresponding</span></div><div class='output co'>#> <span class='message'>to the terminal degradation rate found with the DFOP model.</span></div><div class='output co'>#> <span class='message'>The representative half-life obtained from the DFOP model may be used</span></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>nafta_evaluation</span><span class='op'>)</span> -</div><div class='output co'>#> Sums of squares: -#> SFO IORE DFOP -#> 1378.6832 615.7730 517.8836 -#> -#> Critical sum of squares for checking the SFO model: -#> [1] 717.4598 -#> -#> Parameters: -#> $SFO -#> Estimate Pr(>t) Lower Upper -#> parent_0 83.7558 1.80e-14 77.18268 90.3288 -#> k_parent 0.0017 7.43e-05 0.00112 0.0026 -#> sigma 8.7518 1.22e-05 5.64278 11.8608 -#> -#> $IORE -#> Estimate Pr(>t) Lower Upper -#> parent_0 9.69e+01 NA 8.88e+01 1.05e+02 -#> k__iore_parent 8.40e-14 NA 1.79e-18 3.94e-09 -#> N_parent 6.68e+00 NA 4.19e+00 9.17e+00 -#> sigma 5.85e+00 NA 3.76e+00 7.94e+00 -#> -#> $DFOP -#> Estimate Pr(>t) Lower Upper -#> parent_0 9.76e+01 1.94e-13 9.02e+01 1.05e+02 -#> k1 4.24e-02 5.92e-03 2.03e-02 8.88e-02 -#> k2 8.24e-04 6.48e-03 3.89e-04 1.75e-03 -#> g 2.88e-01 2.47e-05 1.95e-01 4.03e-01 -#> sigma 5.36e+00 2.22e-05 3.43e+00 7.30e+00 -#> -#> -#> DTx values: -#> DT50 DT90 DT50_rep -#> SFO 407 1350 407 -#> IORE 541 5190000 1560000 -#> DFOP 429 2380 841 -#> -#> Representative half-life: -#> [1] 841.41</div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>nafta_evaluation</span><span class='op'>)</span> -</div><div class='img'><img src='nafta-1.png' alt='' width='700' height='433' /></div><div class='input'> -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="va">nafta_evaluation</span> <span class="op"><-</span> <span class="fu">nafta</span><span class="op">(</span><span class="va">NAFTA_SOP_Appendix_D</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span> +<span class="r-msg co"><span class="r-pr">#></span> The representative half-life of the IORE model is longer than the one corresponding</span> +<span class="r-msg co"><span class="r-pr">#></span> to the terminal degradation rate found with the DFOP model.</span> +<span class="r-msg co"><span class="r-pr">#></span> The representative half-life obtained from the DFOP model may be used</span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">nafta_evaluation</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Sums of squares:</span> +<span class="r-out co"><span class="r-pr">#></span> SFO IORE DFOP </span> +<span class="r-out co"><span class="r-pr">#></span> 1378.6832 615.7730 517.8836 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Critical sum of squares for checking the SFO model:</span> +<span class="r-out co"><span class="r-pr">#></span> [1] 717.4598</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Parameters:</span> +<span class="r-out co"><span class="r-pr">#></span> $SFO</span> +<span class="r-out co"><span class="r-pr">#></span> Estimate Pr(>t) Lower Upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 83.7558 1.80e-14 77.18268 90.3288</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent 0.0017 7.43e-05 0.00112 0.0026</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 8.7518 1.22e-05 5.64278 11.8608</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> $IORE</span> +<span class="r-out co"><span class="r-pr">#></span> Estimate Pr(>t) Lower Upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 9.69e+01 NA 8.88e+01 1.05e+02</span> +<span class="r-out co"><span class="r-pr">#></span> k__iore_parent 8.40e-14 NA 1.79e-18 3.94e-09</span> +<span class="r-out co"><span class="r-pr">#></span> N_parent 6.68e+00 NA 4.19e+00 9.17e+00</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 5.85e+00 NA 3.76e+00 7.94e+00</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> $DFOP</span> +<span class="r-out co"><span class="r-pr">#></span> Estimate Pr(>t) Lower Upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 9.76e+01 1.94e-13 9.02e+01 1.05e+02</span> +<span class="r-out co"><span class="r-pr">#></span> k1 4.24e-02 5.92e-03 2.03e-02 8.88e-02</span> +<span class="r-out co"><span class="r-pr">#></span> k2 8.24e-04 6.48e-03 3.89e-04 1.75e-03</span> +<span class="r-out co"><span class="r-pr">#></span> g 2.88e-01 2.47e-05 1.95e-01 4.03e-01</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 5.36e+00 2.22e-05 3.43e+00 7.30e+00</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> DTx values:</span> +<span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50_rep</span> +<span class="r-out co"><span class="r-pr">#></span> SFO 407 1350 407</span> +<span class="r-out co"><span class="r-pr">#></span> IORE 541 5190000 1560000</span> +<span class="r-out co"><span class="r-pr">#></span> DFOP 429 2380 841</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Representative half-life:</span> +<span class="r-out co"><span class="r-pr">#></span> [1] 841.41</span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">nafta_evaluation</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="nafta-1.png" alt="" width="700" height="433"></span> +<span class="r-in"><span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/nlme-1.png b/docs/dev/reference/nlme-1.png Binary files differindex 67cc7f3c..c4fc9d31 100644 --- a/docs/dev/reference/nlme-1.png +++ b/docs/dev/reference/nlme-1.png diff --git a/docs/dev/reference/nlme-2.png b/docs/dev/reference/nlme-2.png Binary files differindex bb1e6f81..d9512f41 100644 --- a/docs/dev/reference/nlme-2.png +++ b/docs/dev/reference/nlme-2.png diff --git a/docs/dev/reference/nlme.html b/docs/dev/reference/nlme.html index e7844299..b8b36d56 100644 --- a/docs/dev/reference/nlme.html +++ b/docs/dev/reference/nlme.html @@ -20,7 +20,7 @@ datasets. They are used internally by the nlme.mmkin() method."><meta name="robo </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -29,7 +29,7 @@ datasets. They are used internally by the nlme.mmkin() method."><meta name="robo <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -44,19 +44,28 @@ datasets. They are used internally by the nlme.mmkin() method."><meta name="robo <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -90,20 +99,25 @@ datasets. They are used internally by the <code><a href="nlme.mmkin.html">nlme.m </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nlme_function</span><span class="op">(</span><span class="va">object</span><span class="op">)</span> - -<span class="fu">nlme_data</span><span class="op">(</span><span class="va">object</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">nlme_function</span><span class="op">(</span><span class="va">object</span><span class="op">)</span></span> +<span></span> +<span><span class="fu">nlme_data</span><span class="op">(</span><span class="va">object</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>object</dt> <dd><p>An mmkin row object containing several fits of the same model to different datasets</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>A function that can be used with nlme -A <code><a href="https://rdrr.io/pkg/nlme/man/groupedData.html" class="external-link">groupedData</a></code> object</p> + + +<p>A function that can be used with nlme</p> + + +<p>A <code><a href="https://rdrr.io/pkg/nlme/man/groupedData.html" class="external-link">groupedData</a></code> object</p> </div> <div id="see-also"> <h2>See also</h2> @@ -112,78 +126,78 @@ A <code><a href="https://rdrr.io/pkg/nlme/man/groupedData.html" class="external- <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span> -<span class="r-in"><span class="va">m_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"><span class="va">d_SFO_1</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">98</span><span class="op">)</span>, <span class="va">sampling_times</span><span class="op">)</span></span> -<span class="r-in"><span class="va">d_SFO_1_long</span> <span class="op"><-</span> <span class="fu"><a href="mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">d_SFO_1</span>, time <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span> -<span class="r-in"><span class="va">d_SFO_2</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.05</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">102</span><span class="op">)</span>, <span class="va">sampling_times</span><span class="op">)</span></span> -<span class="r-in"><span class="va">d_SFO_2_long</span> <span class="op"><-</span> <span class="fu"><a href="mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">d_SFO_2</span>, time <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span> -<span class="r-in"><span class="va">d_SFO_3</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.02</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">103</span><span class="op">)</span>, <span class="va">sampling_times</span><span class="op">)</span></span> -<span class="r-in"><span class="va">d_SFO_3_long</span> <span class="op"><-</span> <span class="fu"><a href="mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">d_SFO_3</span>, time <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">d1</span> <span class="op"><-</span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_SFO_1</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">3</span>, n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span> -<span class="r-in"><span class="va">d2</span> <span class="op"><-</span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_SFO_2</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">2</span>, n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span> -<span class="r-in"><span class="va">d3</span> <span class="op"><-</span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_SFO_3</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">4</span>, n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span> -<span class="r-in"><span class="va">ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>d1 <span class="op">=</span> <span class="va">d1</span>, d2 <span class="op">=</span> <span class="va">d2</span>, d3 <span class="op">=</span> <span class="va">d3</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">ds</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="va">mean_dp</span> <span class="op"><-</span> <span class="fu"><a href="mean_degparms.html">mean_degparms</a></span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span> -<span class="r-in"><span class="va">grouped_data</span> <span class="op"><-</span> <span class="fu">nlme_data</span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span> -<span class="r-in"><span class="va">nlme_f</span> <span class="op"><-</span> <span class="fu">nlme_function</span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span> -<span class="r-in"><span class="co"># These assignments are necessary for these objects to be</span></span> -<span class="r-in"><span class="co"># visible to nlme and augPred when evaluation is done by</span></span> -<span class="r-in"><span class="co"># pkgdown to generate the html docs.</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="st">"nlme_f"</span>, <span class="va">nlme_f</span>, <span class="fu"><a href="https://rdrr.io/r/base/environment.html" class="external-link">globalenv</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="st">"grouped_data"</span>, <span class="va">grouped_data</span>, <span class="fu"><a href="https://rdrr.io/r/base/environment.html" class="external-link">globalenv</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://svn.r-project.org/R-packages/trunk/nlme/" class="external-link">nlme</a></span><span class="op">)</span></span> -<span class="r-in"><span class="va">m_nlme</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">value</span> <span class="op">~</span> <span class="fu">nlme_f</span><span class="op">(</span><span class="va">name</span>, <span class="va">time</span>, <span class="va">parent_0</span>, <span class="va">log_k_parent_sink</span><span class="op">)</span>,</span> -<span class="r-in"> data <span class="op">=</span> <span class="va">grouped_data</span>,</span> -<span class="r-in"> fixed <span class="op">=</span> <span class="va">parent_0</span> <span class="op">+</span> <span class="va">log_k_parent_sink</span> <span class="op">~</span> <span class="fl">1</span>,</span> -<span class="r-in"> random <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/pdDiag.html" class="external-link">pdDiag</a></span><span class="op">(</span><span class="va">parent_0</span> <span class="op">+</span> <span class="va">log_k_parent_sink</span> <span class="op">~</span> <span class="fl">1</span><span class="op">)</span>,</span> -<span class="r-in"> start <span class="op">=</span> <span class="va">mean_dp</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m_nlme</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">m_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">d_SFO_1</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">98</span><span class="op">)</span>, <span class="va">sampling_times</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">d_SFO_1_long</span> <span class="op"><-</span> <span class="fu"><a href="mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">d_SFO_1</span>, time <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">d_SFO_2</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.05</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">102</span><span class="op">)</span>, <span class="va">sampling_times</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">d_SFO_2_long</span> <span class="op"><-</span> <span class="fu"><a href="mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">d_SFO_2</span>, time <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">d_SFO_3</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.02</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">103</span><span class="op">)</span>, <span class="va">sampling_times</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">d_SFO_3_long</span> <span class="op"><-</span> <span class="fu"><a href="mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">d_SFO_3</span>, time <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">d1</span> <span class="op"><-</span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_SFO_1</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">3</span>, n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">d2</span> <span class="op"><-</span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_SFO_2</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">2</span>, n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">d3</span> <span class="op"><-</span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_SFO_3</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">4</span>, n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>d1 <span class="op">=</span> <span class="va">d1</span>, d2 <span class="op">=</span> <span class="va">d2</span>, d3 <span class="op">=</span> <span class="va">d3</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">ds</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">mean_dp</span> <span class="op"><-</span> <span class="fu"><a href="mean_degparms.html">mean_degparms</a></span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">grouped_data</span> <span class="op"><-</span> <span class="fu">nlme_data</span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">nlme_f</span> <span class="op"><-</span> <span class="fu">nlme_function</span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># These assignments are necessary for these objects to be</span></span></span> +<span class="r-in"><span><span class="co"># visible to nlme and augPred when evaluation is done by</span></span></span> +<span class="r-in"><span><span class="co"># pkgdown to generate the html docs.</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="st">"nlme_f"</span>, <span class="va">nlme_f</span>, <span class="fu"><a href="https://rdrr.io/r/base/environment.html" class="external-link">globalenv</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="st">"grouped_data"</span>, <span class="va">grouped_data</span>, <span class="fu"><a href="https://rdrr.io/r/base/environment.html" class="external-link">globalenv</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://svn.r-project.org/R-packages/trunk/nlme/" class="external-link">nlme</a></span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">m_nlme</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">value</span> <span class="op">~</span> <span class="fu">nlme_f</span><span class="op">(</span><span class="va">name</span>, <span class="va">time</span>, <span class="va">parent_0</span>, <span class="va">log_k_parent_sink</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> data <span class="op">=</span> <span class="va">grouped_data</span>,</span></span> +<span class="r-in"><span> fixed <span class="op">=</span> <span class="va">parent_0</span> <span class="op">+</span> <span class="va">log_k_parent_sink</span> <span class="op">~</span> <span class="fl">1</span>,</span></span> +<span class="r-in"><span> random <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/pdDiag.html" class="external-link">pdDiag</a></span><span class="op">(</span><span class="va">parent_0</span> <span class="op">+</span> <span class="va">log_k_parent_sink</span> <span class="op">~</span> <span class="fl">1</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> start <span class="op">=</span> <span class="va">mean_dp</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m_nlme</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Nonlinear mixed-effects model fit by maximum likelihood</span> <span class="r-out co"><span class="r-pr">#></span> Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink) </span> <span class="r-out co"><span class="r-pr">#></span> Data: grouped_data </span> <span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> -<span class="r-out co"><span class="r-pr">#></span> 300.6824 310.2426 -145.3412</span> +<span class="r-out co"><span class="r-pr">#></span> 266.6428 275.8935 -128.3214</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Random effects:</span> <span class="r-out co"><span class="r-pr">#></span> Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1)</span> <span class="r-out co"><span class="r-pr">#></span> Level: ds</span> <span class="r-out co"><span class="r-pr">#></span> Structure: Diagonal</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_parent_sink Residual</span> -<span class="r-out co"><span class="r-pr">#></span> StdDev: 1.697361 0.6801209 3.666073</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_parent_sink Residual</span> +<span class="r-out co"><span class="r-pr">#></span> StdDev: 0.000368491 0.7058039 3.065183</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Fixed effects: parent_0 + log_k_parent_sink ~ 1 </span> -<span class="r-out co"><span class="r-pr">#></span> Value Std.Error DF t-value p-value</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 100.99378 1.3890416 46 72.70753 0</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_parent_sink -3.07521 0.4018589 46 -7.65246 0</span> +<span class="r-out co"><span class="r-pr">#></span> Value Std.Error DF t-value p-value</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 101.18323 0.7900461 43 128.07257 0</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_parent_sink -3.08708 0.4171755 43 -7.39995 0</span> <span class="r-out co"><span class="r-pr">#></span> Correlation: </span> <span class="r-out co"><span class="r-pr">#></span> prnt_0</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_parent_sink 0.027 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k_parent_sink 0.031 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Standardized Within-Group Residuals:</span> -<span class="r-out co"><span class="r-pr">#></span> Min Q1 Med Q3 Max </span> -<span class="r-out co"><span class="r-pr">#></span> -1.9942823 -0.5622565 0.1791579 0.7165038 2.0704781 </span> +<span class="r-out co"><span class="r-pr">#></span> Min Q1 Med Q3 Max </span> +<span class="r-out co"><span class="r-pr">#></span> -2.38427070 -0.52059848 0.03593021 0.39987268 2.73188969 </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Number of Observations: 50</span> +<span class="r-out co"><span class="r-pr">#></span> Number of Observations: 47</span> <span class="r-out co"><span class="r-pr">#></span> Number of Groups: 3 </span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/augPred.html" class="external-link">augPred</a></span><span class="op">(</span><span class="va">m_nlme</span>, level <span class="op">=</span> <span class="fl">0</span><span class="op">:</span><span class="fl">1</span><span class="op">)</span>, layout <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/augPred.html" class="external-link">augPred</a></span><span class="op">(</span><span class="va">m_nlme</span>, level <span class="op">=</span> <span class="fl">0</span><span class="op">:</span><span class="fl">1</span><span class="op">)</span>, layout <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="nlme-1.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="co"># augPred does not work on fits with more than one state</span></span> -<span class="r-in"><span class="co"># variable</span></span> -<span class="r-in"><span class="co">#</span></span> -<span class="r-in"><span class="co"># The procedure is greatly simplified by the nlme.mmkin function</span></span> -<span class="r-in"><span class="va">f_nlme</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlme</span><span class="op">)</span></span> +<span class="r-in"><span><span class="co"># augPred does not work on fits with more than one state</span></span></span> +<span class="r-in"><span><span class="co"># variable</span></span></span> +<span class="r-in"><span><span class="co">#</span></span></span> +<span class="r-in"><span><span class="co"># The procedure is greatly simplified by the nlme.mmkin function</span></span></span> +<span class="r-in"><span><span class="va">f_nlme</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlme</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="nlme-2.png" alt="" width="700" height="433"></span> </code></pre></div> </div> @@ -199,7 +213,7 @@ A <code><a href="https://rdrr.io/pkg/nlme/man/groupedData.html" class="external- </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/dev/reference/nlme.mmkin.html b/docs/dev/reference/nlme.mmkin.html index 2bbadb88..8c069470 100644 --- a/docs/dev/reference/nlme.mmkin.html +++ b/docs/dev/reference/nlme.mmkin.html @@ -19,7 +19,7 @@ have been obtained by fitting the same model to a list of datasets."><meta name= </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.2</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -46,19 +46,25 @@ have been obtained by fitting the same model to a list of datasets."><meta name= <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -97,7 +103,7 @@ have been obtained by fitting the same model to a list of datasets.</p> <span> data <span class="op">=</span> <span class="st">"auto"</span>,</span> <span> fixed <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">as.list</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="fu"><a href="mean_degparms.html">mean_degparms</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>, <span class="kw">function</span><span class="op">(</span><span class="va">el</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/eval.html" class="external-link">eval</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/parse.html" class="external-link">parse</a></span><span class="op">(</span>text <span class="op">=</span></span> <span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="va">el</span>, <span class="fl">1</span>, sep <span class="op">=</span> <span class="st">"~"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>,</span> -<span> random <span class="op">=</span> <span class="fu">pdDiag</span><span class="op">(</span><span class="va">fixed</span><span class="op">)</span>,</span> +<span> random <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/pdDiag.html" class="external-link">pdDiag</a></span><span class="op">(</span><span class="va">fixed</span><span class="op">)</span>,</span> <span> <span class="va">groups</span>,</span> <span> start <span class="op">=</span> <span class="fu"><a href="mean_degparms.html">mean_degparms</a></span><span class="op">(</span><span class="va">model</span>, random <span class="op">=</span> <span class="cn">TRUE</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span> <span> correlation <span class="op">=</span> <span class="cn">NULL</span>,</span> diff --git a/docs/dev/reference/nobs.mkinfit.html b/docs/dev/reference/nobs.mkinfit.html index 0b6c963c..8c2d04f0 100644 --- a/docs/dev/reference/nobs.mkinfit.html +++ b/docs/dev/reference/nobs.mkinfit.html @@ -1,67 +1,12 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - <head> - <meta charset="utf-8"> -<meta http-equiv="X-UA-Compatible" content="IE=edge"> -<meta name="viewport" content="width=device-width, initial-scale=1.0"> - -<title>Number of observations on which an mkinfit object was fitted — nobs.mkinfit • mkin</title> - - -<!-- jquery --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script> -<!-- Bootstrap --> - -<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" 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data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -72,23 +17,21 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -98,48 +41,50 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Number of observations on which an mkinfit object was fitted</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/nobs.mkinfit.R'><code>R/nobs.mkinfit.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nobs.mkinfit.R" class="external-link"><code>R/nobs.mkinfit.R</code></a></small> <div class="hidden name"><code>nobs.mkinfit.Rd</code></div> </div> @@ -147,51 +92,49 @@ <p>Number of observations on which an mkinfit object was fitted</p> </div> - <pre class="usage"><span class='co'># S3 method for mkinfit</span> -<span class='fu'><a href='https://rdrr.io/r/stats/nobs.html'>nobs</a></span><span class='op'>(</span><span class='va'>object</span>, <span class='va'>...</span><span class='op'>)</span></pre> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/stats/nobs.html" class="external-link">nobs</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>object</dt> +<dd><p>An mkinfit object</p></dd> - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>object</th> - <td><p>An mkinfit object</p></td> - </tr> - <tr> - <th>...</th> - <td><p>For compatibility with the generic method</p></td> - </tr> - </table> - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> +<dt>...</dt> +<dd><p>For compatibility with the generic method</p></dd> - <p>The number of rows in the data included in the mkinfit object</p> +</dl></div> + <div id="value"> + <h2>Value</h2> + + +<p>The number of rows in the data included in the mkinfit object</p> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/parms.html b/docs/dev/reference/parms.html index 95db0593..b0385c8a 100644 --- a/docs/dev/reference/parms.html +++ b/docs/dev/reference/parms.html @@ -19,7 +19,7 @@ without considering the error structure that was assumed for the fit."><meta nam </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -61,7 +61,10 @@ without considering the error structure that was assumed for the fit."><meta nam <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/parplot.html b/docs/dev/reference/parplot.html index ffe93e6c..720c0b2a 100644 --- a/docs/dev/reference/parplot.html +++ b/docs/dev/reference/parplot.html @@ -19,7 +19,7 @@ or by their medians as proposed in the paper by Duchesne et al. (2021)."><meta n </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -61,7 +61,10 @@ or by their medians as proposed in the paper by Duchesne et al. (2021)."><meta n <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -100,6 +103,7 @@ or by their medians as proposed in the paper by Duchesne et al. (2021).</p> <span><span class="fu">parplot</span><span class="op">(</span></span> <span> <span class="va">object</span>,</span> <span> llmin <span class="op">=</span> <span class="op">-</span><span class="cn">Inf</span>,</span> +<span> llquant <span class="op">=</span> <span class="cn">NA</span>,</span> <span> scale <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"best"</span>, <span class="st">"median"</span><span class="op">)</span>,</span> <span> lpos <span class="op">=</span> <span class="st">"bottomleft"</span>,</span> <span> main <span class="op">=</span> <span class="st">""</span>,</span> @@ -121,8 +125,14 @@ or by their medians as proposed in the paper by Duchesne et al. (2021).</p> <dd><p>The minimum likelihood of objects to be shown</p></dd> +<dt>llquant</dt> +<dd><p>Fractional value for selecting only the fits with higher +likelihoods. Overrides 'llmin'.</p></dd> + + <dt>scale</dt> -<dd><p>By default, scale parameters using the best available fit. +<dd><p>By default, scale parameters using the best +available fit. If 'median', parameters are scaled using the median parameters from all fits.</p></dd> @@ -134,6 +144,12 @@ If 'median', parameters are scaled using the median parameters from all fits.</p <dd><p>Title of the plot</p></dd> </dl></div> + <div id="details"> + <h2>Details</h2> + <p>Starting values of degradation model parameters and error model parameters +are shown as green circles. The results obtained in the original run +are shown as red circles.</p> + </div> <div id="references"> <h2>References</h2> <p>Duchesne R, Guillemin A, Gandrillon O, Crauste F. Practical diff --git a/docs/dev/reference/plot.mixed.mmkin.html b/docs/dev/reference/plot.mixed.mmkin.html index b1c62721..4bd170a1 100644 --- a/docs/dev/reference/plot.mixed.mmkin.html +++ b/docs/dev/reference/plot.mixed.mmkin.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -59,7 +59,10 @@ <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/plot.mkinfit.html b/docs/dev/reference/plot.mkinfit.html index 764f0699..d125a606 100644 --- a/docs/dev/reference/plot.mkinfit.html +++ b/docs/dev/reference/plot.mkinfit.html @@ -19,7 +19,7 @@ observed data together with the solution of the fitted model."><meta name="robot </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.2</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -46,19 +46,25 @@ observed data together with the solution of the fitted model."><meta name="robot <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/plot.mmkin-1.png b/docs/dev/reference/plot.mmkin-1.png Binary files differindex 647dfb8a..235e33a7 100644 --- a/docs/dev/reference/plot.mmkin-1.png +++ b/docs/dev/reference/plot.mmkin-1.png diff --git a/docs/dev/reference/plot.mmkin-2.png b/docs/dev/reference/plot.mmkin-2.png Binary files differindex 1bc1c9db..7af84edf 100644 --- a/docs/dev/reference/plot.mmkin-2.png +++ b/docs/dev/reference/plot.mmkin-2.png diff --git a/docs/dev/reference/plot.mmkin-3.png b/docs/dev/reference/plot.mmkin-3.png Binary files differindex 50d6ffac..56bfac50 100644 --- a/docs/dev/reference/plot.mmkin-3.png +++ b/docs/dev/reference/plot.mmkin-3.png diff --git a/docs/dev/reference/plot.mmkin-4.png b/docs/dev/reference/plot.mmkin-4.png Binary files differindex e049fa16..5da05f40 100644 --- a/docs/dev/reference/plot.mmkin-4.png +++ b/docs/dev/reference/plot.mmkin-4.png diff --git a/docs/dev/reference/plot.mmkin-5.png b/docs/dev/reference/plot.mmkin-5.png Binary files differindex 2421995b..3ec224f4 100644 --- a/docs/dev/reference/plot.mmkin-5.png +++ b/docs/dev/reference/plot.mmkin-5.png diff --git a/docs/dev/reference/plot.mmkin.html b/docs/dev/reference/plot.mmkin.html index 9ca0df94..09f311c5 100644 --- a/docs/dev/reference/plot.mmkin.html +++ b/docs/dev/reference/plot.mmkin.html @@ -1,71 +1,16 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - 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<ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -102,200 +45,210 @@ the fit of at least one model to the same dataset is shown." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/plot.mmkin.R'><code>R/plot.mmkin.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/plot.mmkin.R" class="external-link"><code>R/plot.mmkin.R</code></a></small> <div class="hidden name"><code>plot.mmkin.Rd</code></div> </div> <div class="ref-description"> - <p>When x is a row selected from an mmkin object (<code><a href='[.mmkin.html'>[.mmkin</a></code>), the + <p>When x is a row selected from an mmkin object (<code><a href="Extract.mmkin.html">[.mmkin</a></code>), the same model fitted for at least one dataset is shown. When it is a column, the fit of at least one model to the same dataset is shown.</p> </div> - <pre class="usage"><span class='co'># S3 method for mmkin</span> -<span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span> - <span class='va'>x</span>, - main <span class='op'>=</span> <span class='st'>"auto"</span>, - legends <span class='op'>=</span> <span class='fl'>1</span>, - resplot <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"time"</span>, <span class='st'>"errmod"</span><span class='op'>)</span>, - ylab <span class='op'>=</span> <span class='st'>"Residue"</span>, - standardized <span class='op'>=</span> <span class='cn'>FALSE</span>, - show_errmin <span class='op'>=</span> <span class='cn'>TRUE</span>, - errmin_var <span class='op'>=</span> <span class='st'>"All data"</span>, - errmin_digits <span class='op'>=</span> <span class='fl'>3</span>, - cex <span class='op'>=</span> <span class='fl'>0.7</span>, - rel.height.middle <span class='op'>=</span> <span class='fl'>0.9</span>, - ymax <span class='op'>=</span> <span class='st'>"auto"</span>, - <span class='va'>...</span> -<span class='op'>)</span></pre> - - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>x</th> - <td><p>An object of class <code><a href='mmkin.html'>mmkin</a></code>, with either one row or one -column.</p></td> - </tr> - <tr> - <th>main</th> - <td><p>The main title placed on the outer margin of the plot.</p></td> - </tr> - <tr> - <th>legends</th> - <td><p>An index for the fits for which legends should be shown.</p></td> - </tr> - <tr> - <th>resplot</th> - <td><p>Should the residuals plotted against time, using -<code><a href='mkinresplot.html'>mkinresplot</a></code>, or as squared residuals against predicted -values, with the error model, using <code><a href='mkinerrplot.html'>mkinerrplot</a></code>.</p></td> - </tr> - <tr> - <th>ylab</th> - <td><p>Label for the y axis.</p></td> - </tr> - <tr> - <th>standardized</th> - <td><p>Should the residuals be standardized? This option -is passed to <code><a href='mkinresplot.html'>mkinresplot</a></code>, it only takes effect if -<code>resplot = "time"</code>.</p></td> - </tr> - <tr> - <th>show_errmin</th> - <td><p>Should the chi2 error level be shown on top of the plots -to the left?</p></td> - </tr> - <tr> - <th>errmin_var</th> - <td><p>The variable for which the FOCUS chi2 error value should -be shown.</p></td> - </tr> - <tr> - <th>errmin_digits</th> - <td><p>The number of significant digits for rounding the FOCUS -chi2 error percentage.</p></td> - </tr> - <tr> - <th>cex</th> - <td><p>Passed to the plot functions and <code><a href='https://rdrr.io/r/graphics/mtext.html'>mtext</a></code>.</p></td> - </tr> - <tr> - <th>rel.height.middle</th> - <td><p>The relative height of the middle plot, if more -than two rows of plots are shown.</p></td> - </tr> - <tr> - <th>ymax</th> - <td><p>Maximum y axis value for <code><a href='plot.mkinfit.html'>plot.mkinfit</a></code>.</p></td> - </tr> - <tr> - <th>...</th> - <td><p>Further arguments passed to <code><a href='plot.mkinfit.html'>plot.mkinfit</a></code> and -<code><a href='mkinresplot.html'>mkinresplot</a></code>.</p></td> - </tr> - </table> - - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> - - <p>The function is called for its side effect.</p> - <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2> - - <p>If the current plot device is a <code><a href='https://rdrr.io/pkg/tikzDevice/man/tikz.html'>tikz</a></code> device, then -latex is being used for the formatting of the chi2 error level.</p> - <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span> +<span> <span class="va">x</span>,</span> +<span> main <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> legends <span class="op">=</span> <span class="fl">1</span>,</span> +<span> resplot <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"time"</span>, <span class="st">"errmod"</span><span class="op">)</span>,</span> +<span> ylab <span class="op">=</span> <span class="st">"Residue"</span>,</span> +<span> standardized <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> show_errmin <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> errmin_var <span class="op">=</span> <span class="st">"All data"</span>,</span> +<span> errmin_digits <span class="op">=</span> <span class="fl">3</span>,</span> +<span> cex <span class="op">=</span> <span class="fl">0.7</span>,</span> +<span> rel.height.middle <span class="op">=</span> <span class="fl">0.9</span>,</span> +<span> ymax <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>x</dt> +<dd><p>An object of class <code><a href="mmkin.html">mmkin</a></code>, with either one row or one +column.</p></dd> + + +<dt>main</dt> +<dd><p>The main title placed on the outer margin of the plot.</p></dd> + + +<dt>legends</dt> +<dd><p>An index for the fits for which legends should be shown.</p></dd> + + +<dt>resplot</dt> +<dd><p>Should the residuals plotted against time, using +<code><a href="mkinresplot.html">mkinresplot</a></code>, or as squared residuals against predicted +values, with the error model, using <code><a href="mkinerrplot.html">mkinerrplot</a></code>.</p></dd> + + +<dt>ylab</dt> +<dd><p>Label for the y axis.</p></dd> + + +<dt>standardized</dt> +<dd><p>Should the residuals be standardized? This option +is passed to <code><a href="mkinresplot.html">mkinresplot</a></code>, it only takes effect if +<code>resplot = "time"</code>.</p></dd> + + +<dt>show_errmin</dt> +<dd><p>Should the chi2 error level be shown on top of the plots +to the left?</p></dd> + + +<dt>errmin_var</dt> +<dd><p>The variable for which the FOCUS chi2 error value should +be shown.</p></dd> + +<dt>errmin_digits</dt> +<dd><p>The number of significant digits for rounding the FOCUS +chi2 error percentage.</p></dd> + + +<dt>cex</dt> +<dd><p>Passed to the plot functions and <code><a href="https://rdrr.io/r/graphics/mtext.html" class="external-link">mtext</a></code>.</p></dd> + + +<dt>rel.height.middle</dt> +<dd><p>The relative height of the middle plot, if more +than two rows of plots are shown.</p></dd> + + +<dt>ymax</dt> +<dd><p>Maximum y axis value for <code><a href="plot.mkinfit.html">plot.mkinfit</a></code>.</p></dd> + + +<dt>...</dt> +<dd><p>Further arguments passed to <code><a href="plot.mkinfit.html">plot.mkinfit</a></code> and +<code><a href="mkinresplot.html">mkinresplot</a></code>.</p></dd> + +</dl></div> + <div id="value"> + <h2>Value</h2> + + +<p>The function is called for its side effect.</p> + </div> + <div id="details"> + <h2>Details</h2> + <p>If the current plot device is a <code><a href="https://rdrr.io/pkg/tikzDevice/man/tikz.html" class="external-link">tikz</a></code> device, then +latex is being used for the formatting of the chi2 error level.</p> + </div> + <div id="author"> + <h2>Author</h2> <p>Johannes Ranke</p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> - <span class='co'># \dontrun{</span> - <span class='co'># Only use one core not to offend CRAN checks</span> - <span class='va'>fits</span> <span class='op'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"FOMC"</span>, <span class='st'>"HS"</span><span class='op'>)</span>, - <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='st'>"FOCUS B"</span> <span class='op'>=</span> <span class='va'>FOCUS_2006_B</span>, <span class='st'>"FOCUS C"</span> <span class='op'>=</span> <span class='va'>FOCUS_2006_C</span><span class='op'>)</span>, <span class='co'># named list for titles</span> - cores <span class='op'>=</span> <span class='fl'>1</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='warning'>Warning: Optimisation did not converge:</span> -#> <span class='warning'>iteration limit reached without convergence (10)</span></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>fits</span><span class='op'>[</span>, <span class='st'>"FOCUS C"</span><span class='op'>]</span><span class='op'>)</span> -</div><div class='img'><img src='plot.mmkin-1.png' alt='' width='700' height='433' /></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>fits</span><span class='op'>[</span><span class='st'>"FOMC"</span>, <span class='op'>]</span><span class='op'>)</span> -</div><div class='img'><img src='plot.mmkin-2.png' alt='' width='700' height='433' /></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>fits</span><span class='op'>[</span><span class='st'>"FOMC"</span>, <span class='op'>]</span>, show_errmin <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span> -</div><div class='img'><img src='plot.mmkin-3.png' alt='' width='700' height='433' /></div><div class='input'> - <span class='co'># We can also plot a single fit, if we like the way plot.mmkin works, but then the plot</span> - <span class='co'># height should be smaller than the plot width (this is not possible for the html pages</span> - <span class='co'># generated by pkgdown, as far as I know).</span> - <span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>fits</span><span class='op'>[</span><span class='st'>"FOMC"</span>, <span class='st'>"FOCUS C"</span><span class='op'>]</span><span class='op'>)</span> <span class='co'># same as plot(fits[1, 2])</span> -</div><div class='img'><img src='plot.mmkin-4.png' alt='' width='700' height='433' /></div><div class='input'> - <span class='co'># Show the error models</span> - <span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>fits</span><span class='op'>[</span><span class='st'>"FOMC"</span>, <span class='op'>]</span>, resplot <span class='op'>=</span> <span class='st'>"errmod"</span><span class='op'>)</span> -</div><div class='img'><img src='plot.mmkin-5.png' alt='' width='700' height='433' /></div><div class='input'> <span class='co'># }</span> - -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span> <span class="co"># Only use one core not to offend CRAN checks</span></span></span> +<span class="r-in"><span> <span class="va">fits</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="st">"HS"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS B"</span> <span class="op">=</span> <span class="va">FOCUS_2006_B</span>, <span class="st">"FOCUS C"</span> <span class="op">=</span> <span class="va">FOCUS_2006_C</span><span class="op">)</span>, <span class="co"># named list for titles</span></span></span> +<span class="r-in"><span> cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span> +<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Optimisation did not converge:</span> +<span class="r-wrn co"><span class="r-pr">#></span> iteration limit reached without convergence (10)</span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits</span><span class="op">[</span>, <span class="st">"FOCUS C"</span><span class="op">]</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="plot.mmkin-1.png" alt="" width="700" height="433"></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="plot.mmkin-2.png" alt="" width="700" height="433"></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span>, show_errmin <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="plot.mmkin-3.png" alt="" width="700" height="433"></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># We can also plot a single fit, if we like the way plot.mmkin works, but then the plot</span></span></span> +<span class="r-in"><span> <span class="co"># height should be smaller than the plot width (this is not possible for the html pages</span></span></span> +<span class="r-in"><span> <span class="co"># generated by pkgdown, as far as I know).</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="st">"FOCUS C"</span><span class="op">]</span><span class="op">)</span> <span class="co"># same as plot(fits[1, 2])</span></span></span> +<span class="r-plt img"><img src="plot.mmkin-4.png" alt="" width="700" height="433"></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># Show the error models</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span>, resplot <span class="op">=</span> <span class="st">"errmod"</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="plot.mmkin-5.png" alt="" width="700" height="433"></span> +<span class="r-in"><span> <span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/plot.nafta.html b/docs/dev/reference/plot.nafta.html index c24fba99..82b46336 100644 --- a/docs/dev/reference/plot.nafta.html +++ b/docs/dev/reference/plot.nafta.html @@ -1,68 +1,13 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - <head> - <meta charset="utf-8"> -<meta http-equiv="X-UA-Compatible" content="IE=edge"> -<meta name="viewport" content="width=device-width, initial-scale=1.0"> - 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- - </head> - - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -73,23 +18,21 @@ function (SFO, then IORE, then DFOP)." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -99,48 +42,50 @@ function (SFO, then IORE, then DFOP)." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Plot the results of the three models used in the NAFTA scheme.</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/nafta.R'><code>R/nafta.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nafta.R" class="external-link"><code>R/nafta.R</code></a></small> <div class="hidden name"><code>plot.nafta.Rd</code></div> </div> @@ -149,65 +94,65 @@ function (SFO, then IORE, then DFOP)." /> function (SFO, then IORE, then DFOP).</p> </div> - <pre class="usage"><span class='co'># S3 method for nafta</span> -<span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>x</span>, legend <span class='op'>=</span> <span class='cn'>FALSE</span>, main <span class='op'>=</span> <span class='st'>"auto"</span>, <span class='va'>...</span><span class='op'>)</span></pre> - - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>x</th> - <td><p>An object of class <code><a href='nafta.html'>nafta</a></code>.</p></td> - </tr> - <tr> - <th>legend</th> - <td><p>Should a legend be added?</p></td> - </tr> - <tr> - <th>main</th> - <td><p>Possibility to override the main title of the plot.</p></td> - </tr> - <tr> - <th>...</th> - <td><p>Further arguments passed to <code><a href='plot.mmkin.html'>plot.mmkin</a></code>.</p></td> - </tr> - </table> - - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> - - <p>The function is called for its side effect.</p> - <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2> - - <p>Calls <code><a href='plot.mmkin.html'>plot.mmkin</a></code>.</p> - <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for nafta</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, legend <span class="op">=</span> <span class="cn">FALSE</span>, main <span class="op">=</span> <span class="st">"auto"</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>x</dt> +<dd><p>An object of class <code><a href="nafta.html">nafta</a></code>.</p></dd> + +<dt>legend</dt> +<dd><p>Should a legend be added?</p></dd> + + +<dt>main</dt> +<dd><p>Possibility to override the main title of the plot.</p></dd> + + +<dt>...</dt> +<dd><p>Further arguments passed to <code><a href="plot.mmkin.html">plot.mmkin</a></code>.</p></dd> + +</dl></div> + <div id="value"> + <h2>Value</h2> + + +<p>The function is called for its side effect.</p> + </div> + <div id="details"> + <h2>Details</h2> + <p>Calls <code><a href="plot.mmkin.html">plot.mmkin</a></code>.</p> + </div> + <div id="author"> + <h2>Author</h2> <p>Johannes Ranke</p> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/read_spreadsheet.html b/docs/dev/reference/read_spreadsheet.html index 70828765..d0ac47d4 100644 --- a/docs/dev/reference/read_spreadsheet.html +++ b/docs/dev/reference/read_spreadsheet.html @@ -22,7 +22,7 @@ factors can be given in columns named 'Temperature' and 'Moisture'."><meta name= </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -64,7 +64,10 @@ factors can be given in columns named 'Temperature' and 'Moisture'."><meta name= <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -103,7 +106,7 @@ factors can be given in columns named 'Temperature' and 'Moisture'.</p> <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">read_spreadsheet</span><span class="op">(</span></span> <span> <span class="va">path</span>,</span> <span> valid_datasets <span class="op">=</span> <span class="st">"all"</span>,</span> -<span> parent_only <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> parent_only <span class="op">=</span> <span class="cn">FALSE</span>,</span> <span> normalize <span class="op">=</span> <span class="cn">TRUE</span></span> <span><span class="op">)</span></span></code></pre></div> </div> diff --git a/docs/dev/reference/reexports.html b/docs/dev/reference/reexports.html index 0999e346..ad825391 100644 --- a/docs/dev/reference/reexports.html +++ b/docs/dev/reference/reexports.html @@ -28,7 +28,7 @@ intervals, nlme </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.2</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -55,19 +55,25 @@ intervals, nlme <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/residuals.mkinfit.html b/docs/dev/reference/residuals.mkinfit.html index 3f518ab7..009f790f 100644 --- a/docs/dev/reference/residuals.mkinfit.html +++ b/docs/dev/reference/residuals.mkinfit.html @@ -1,67 +1,12 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - <head> - <meta charset="utf-8"> -<meta http-equiv="X-UA-Compatible" content="IE=edge"> -<meta name="viewport" content="width=device-width, initial-scale=1.0"> - -<title>Extract residuals from an mkinfit model — residuals.mkinfit • mkin</title> - - -<!-- jquery --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script> -<!-- Bootstrap --> - 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- +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - </head> - - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -72,23 +17,21 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -98,48 +41,50 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Extract residuals from an mkinfit model</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/residuals.mkinfit.R'><code>R/residuals.mkinfit.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/residuals.mkinfit.R" class="external-link"><code>R/residuals.mkinfit.R</code></a></small> <div class="hidden name"><code>residuals.mkinfit.Rd</code></div> </div> @@ -147,60 +92,59 @@ <p>Extract residuals from an mkinfit model</p> </div> - <pre class="usage"><span class='co'># S3 method for mkinfit</span> -<span class='fu'><a href='https://rdrr.io/r/stats/residuals.html'>residuals</a></span><span class='op'>(</span><span class='va'>object</span>, standardized <span class='op'>=</span> <span class='cn'>FALSE</span>, <span class='va'>...</span><span class='op'>)</span></pre> - - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>object</th> - <td><p>A <code><a href='mkinfit.html'>mkinfit</a></code> object</p></td> - </tr> - <tr> - <th>standardized</th> - <td><p>Should the residuals be standardized by dividing by the -standard deviation obtained from the fitted error model?</p></td> - </tr> - <tr> - <th>...</th> - <td><p>Not used</p></td> - </tr> - </table> - - - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'><span class='va'>f</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='st'>"DFOP"</span>, <span class='va'>FOCUS_2006_C</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='fu'><a href='https://rdrr.io/r/stats/residuals.html'>residuals</a></span><span class='op'>(</span><span class='va'>f</span><span class='op'>)</span> -</div><div class='output co'>#> [1] 0.09726374 -0.13912142 -0.15351210 0.73388322 -0.08657004 -0.93204702 -#> [7] -0.03269080 1.45347823 -0.88423697</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/residuals.html'>residuals</a></span><span class='op'>(</span><span class='va'>f</span>, standardized <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -</div><div class='output co'>#> [1] 0.13969917 -0.19981904 -0.22048826 1.05407091 -0.12433989 -1.33869208 -#> [7] -0.04695355 2.08761977 -1.27002287</div></pre> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/stats/residuals.html" class="external-link">residuals</a></span><span class="op">(</span><span class="va">object</span>, standardized <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>object</dt> +<dd><p>A <code><a href="mkinfit.html">mkinfit</a></code> object</p></dd> + + +<dt>standardized</dt> +<dd><p>Should the residuals be standardized by dividing by the +standard deviation obtained from the fitted error model?</p></dd> + + +<dt>...</dt> +<dd><p>Not used</p></dd> + +</dl></div> + + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/residuals.html" class="external-link">residuals</a></span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] 0.09726374 -0.13912142 -0.15351210 0.73388322 -0.08657004 -0.93204702</span> +<span class="r-out co"><span class="r-pr">#></span> [7] -0.03269080 1.45347823 -0.88423697</span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/residuals.html" class="external-link">residuals</a></span><span class="op">(</span><span class="va">f</span>, standardized <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] 0.13969917 -0.19981904 -0.22048826 1.05407091 -0.12433989 -1.33869208</span> +<span class="r-out co"><span class="r-pr">#></span> [7] -0.04695355 2.08761977 -1.27002287</span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html index 73382cba..131b168b 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -19,7 +19,7 @@ Expectation Maximisation algorithm (SAEM)."><meta name="robots" content="noindex </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -113,7 +113,7 @@ Expectation Maximisation algorithm (SAEM).</p> <span> covariates <span class="op">=</span> <span class="cn">NULL</span>,</span> <span> covariate_models <span class="op">=</span> <span class="cn">NULL</span>,</span> <span> no_random_effect <span class="op">=</span> <span class="cn">NULL</span>,</span> -<span> error.init <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">0.1</span><span class="op">)</span>,</span> +<span> error.init <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">)</span>,</span> <span> nbiter.saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">300</span>, <span class="fl">100</span><span class="op">)</span>,</span> <span> control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>displayProgress <span class="op">=</span> <span class="cn">FALSE</span>, print <span class="op">=</span> <span class="cn">FALSE</span>, nbiter.saemix <span class="op">=</span> <span class="va">nbiter.saemix</span>,</span> <span> save <span class="op">=</span> <span class="cn">FALSE</span>, save.graphs <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> @@ -430,10 +430,10 @@ using <a href="mmkin.html">mmkin</a>.</p> <span class="r-plt img"><img src="saem-4.png" alt="" width="700" height="433"></span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> saemix version used for fitting: 3.2 </span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.2.0 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.2.2 </span> <span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Tue Nov 15 00:45:58 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Tue Nov 15 00:45:58 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Wed Dec 7 16:22:26 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Wed Dec 7 16:22:26 2022 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span> @@ -448,12 +448,12 @@ using <a href="mmkin.html">mmkin</a>.</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type analytical </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted in 9.189 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted in 8.508 s</span> <span class="r-out co"><span class="r-pr">#></span> Using 300, 100 iterations and 10 chains</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Variance model: Constant variance </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Mean of starting values for individual parameters:</span> +<span class="r-out co"><span class="r-pr">#></span> Starting values for degradation parameters:</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_A1 f_parent_qlogis log_k1 log_k2 </span> <span class="r-out co"><span class="r-pr">#></span> 93.8102 -5.3734 -0.9711 -1.8799 -4.2708 </span> <span class="r-out co"><span class="r-pr">#></span> g_qlogis </span> @@ -462,6 +462,19 @@ using <a href="mmkin.html">mmkin</a>.</p> <span class="r-out co"><span class="r-pr">#></span> Fixed degradation parameter values:</span> <span class="r-out co"><span class="r-pr">#></span> None</span> <span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Starting values for random effects (square root of initial entries in omega):</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_A1 f_parent_qlogis log_k1 log_k2 g_qlogis</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 4.941 0.000 0.0000 0.000 0.000 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_A1 0.000 2.551 0.0000 0.000 0.000 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis 0.000 0.000 0.7251 0.000 0.000 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> log_k1 0.000 0.000 0.0000 1.449 0.000 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> log_k2 0.000 0.000 0.0000 0.000 2.228 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> g_qlogis 0.000 0.000 0.0000 0.000 0.000 0.7814</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Starting values for error model parameters:</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 </span> +<span class="r-out co"><span class="r-pr">#></span> 1 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Results:</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Likelihood computed by importance sampling</span> diff --git a/docs/dev/reference/schaefer07_complex_case-1.png b/docs/dev/reference/schaefer07_complex_case-1.png Binary files differindex 96aab2dc..eee9e0cc 100644 --- a/docs/dev/reference/schaefer07_complex_case-1.png +++ b/docs/dev/reference/schaefer07_complex_case-1.png diff --git a/docs/dev/reference/schaefer07_complex_case.html b/docs/dev/reference/schaefer07_complex_case.html index 4ccad5c4..5ff62d34 100644 --- a/docs/dev/reference/schaefer07_complex_case.html +++ b/docs/dev/reference/schaefer07_complex_case.html @@ -1,69 +1,14 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - <head> - <meta charset="utf-8"> -<meta http-equiv="X-UA-Compatible" content="IE=edge"> -<meta name="viewport" content="width=device-width, initial-scale=1.0"> - -<title>Metabolism data set used for checking the software quality of KinGUI — schaefer07_complex_case • mkin</title> - - -<!-- jquery --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script> -<!-- Bootstrap --> - -<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous" /> - -<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script> - -<!-- bootstrap-toc --> -<link rel="stylesheet" href="../bootstrap-toc.css"> -<script src="../bootstrap-toc.js"></script> - 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The results from the fitting are also included." /> - - -<meta name="robots" content="noindex"> - -<!-- mathjax --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script> - -<!--[if lt IE 9]> + The results from the fitting are also included."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> - - - - </head> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -74,23 +19,21 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -100,44 +43,46 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Metabolism data set used for checking the software quality of KinGUI</h1> @@ -151,93 +96,109 @@ The results from the fitting are also included.</p> </div> - <pre class="usage"><span class='va'>schaefer07_complex_case</span></pre> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">schaefer07_complex_case</span></span></code></pre></div> + </div> + + <div id="format"> + <h2>Format</h2> + <p>The data set is a data frame with 8 observations on the following 6 variables.</p><dl><dt><code>time</code></dt> +<dd><p>a numeric vector</p></dd> + + <dt><code>parent</code></dt> +<dd><p>a numeric vector</p></dd> + <dt><code>A1</code></dt> +<dd><p>a numeric vector</p></dd> - <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2> + <dt><code>B1</code></dt> +<dd><p>a numeric vector</p></dd> + + <dt><code>C1</code></dt> +<dd><p>a numeric vector</p></dd> + + <dt><code>A2</code></dt> +<dd><p>a numeric vector</p></dd> - <p>The data set is a data frame with 8 observations on the following 6 variables.</p><dl> - <dt><code>time</code></dt><dd><p>a numeric vector</p></dd> - <dt><code>parent</code></dt><dd><p>a numeric vector</p></dd> - <dt><code>A1</code></dt><dd><p>a numeric vector</p></dd> - <dt><code>B1</code></dt><dd><p>a numeric vector</p></dd> - <dt><code>C1</code></dt><dd><p>a numeric vector</p></dd> - <dt><code>A2</code></dt><dd><p>a numeric vector</p></dd> </dl><p>The results are a data frame with 14 results for different parameter values</p> - <h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2> - + </div> + <div id="references"> + <h2>References</h2> <p>Schäfer D, Mikolasch B, Rainbird P and Harvey B (2007). KinGUI: a new kinetic software tool for evaluations according to FOCUS degradation kinetics. In: Del Re AAM, Capri E, Fragoulis G and Trevisan M (Eds.). Proceedings of the XIII Symposium Pesticide Chemistry, Piacenza, 2007, p. 916-923.</p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'><span class='va'>data</span> <span class='op'><-</span> <span class='fu'><a href='mkin_wide_to_long.html'>mkin_wide_to_long</a></span><span class='op'>(</span><span class='va'>schaefer07_complex_case</span>, time <span class='op'>=</span> <span class='st'>"time"</span><span class='op'>)</span> -<span class='va'>model</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span> - parent <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"A1"</span>, <span class='st'>"B1"</span>, <span class='st'>"C1"</span><span class='op'>)</span>, sink <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span>, - A1 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='st'>"A2"</span><span class='op'>)</span>, - B1 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span>, - C1 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span>, - A2 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span>, use_of_ff <span class='op'>=</span> <span class='st'>"max"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'> <span class='co'># \dontrun{</span> - <span class='va'>fit</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>model</span>, <span class='va'>data</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> - <span class='fu'><a href='https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html'>plot</a></span><span class='op'>(</span><span class='va'>fit</span><span class='op'>)</span> -</div><div class='img'><img src='schaefer07_complex_case-1.png' alt='' width='700' height='433' /></div><div class='input'> <span class='fu'><a href='endpoints.html'>endpoints</a></span><span class='op'>(</span><span class='va'>fit</span><span class='op'>)</span> -</div><div class='output co'>#> $ff -#> parent_A1 parent_B1 parent_C1 parent_sink A1_A2 A1_sink -#> 0.3809620 0.1954667 0.4235713 0.0000000 0.4479619 0.5520381 -#> -#> $distimes -#> DT50 DT90 -#> parent 13.95078 46.34350 -#> A1 49.75342 165.27728 -#> B1 37.26908 123.80520 -#> C1 11.23131 37.30961 -#> A2 28.50624 94.69567 -#> </div><div class='input'> <span class='co'># }</span> - <span class='co'># Compare with the results obtained in the original publication</span> - <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>schaefer07_complex_results</span><span class='op'>)</span> -</div><div class='output co'>#> compound parameter KinGUI ModelMaker deviation -#> 1 parent degradation rate 0.0496 0.0506 2.0 -#> 2 parent DT50 13.9900 13.6900 2.2 -#> 3 metabolite A1 formation fraction 0.3803 0.3696 2.9 -#> 4 metabolite A1 degradation rate 0.0139 0.0136 2.2 -#> 5 metabolite A1 DT50 49.9600 50.8900 1.8 -#> 6 metabolite B1 formation fraction 0.1866 0.1818 2.6 -#> 7 metabolite B1 degradation rate 0.0175 0.0172 1.7 -#> 8 metabolite B1 DT50 39.6100 40.2400 1.6 -#> 9 metabolite C1 formation fraction 0.4331 0.4486 3.5 -#> 10 metabolite C1 degradation rate 0.0638 0.0700 8.9 -#> 11 metabolite C1 DT50 10.8700 9.9000 9.8 -#> 12 metabolite A2 formation fraction 0.4529 0.4559 0.7 -#> 13 metabolite A2 degradation rate 0.0245 0.0244 0.4 -#> 14 metabolite A2 DT50 28.2400 28.4500 0.7</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">data</span> <span class="op"><-</span> <span class="fu"><a href="mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">schaefer07_complex_case</span>, time <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">model</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"A1"</span>, <span class="st">"B1"</span>, <span class="st">"C1"</span><span class="op">)</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> A1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"A2"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> B1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> C1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> A2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span> <span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span> <span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="schaefer07_complex_case-1.png" alt="" width="700" height="433"></span> +<span class="r-in"><span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> $ff</span> +<span class="r-out co"><span class="r-pr">#></span> parent_A1 parent_B1 parent_C1 parent_sink A1_A2 A1_sink </span> +<span class="r-out co"><span class="r-pr">#></span> 0.3809620 0.1954667 0.4235713 0.0000000 0.4479619 0.5520381 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> $distimes</span> +<span class="r-out co"><span class="r-pr">#></span> DT50 DT90</span> +<span class="r-out co"><span class="r-pr">#></span> parent 13.95078 46.34350</span> +<span class="r-out co"><span class="r-pr">#></span> A1 49.75342 165.27728</span> +<span class="r-out co"><span class="r-pr">#></span> B1 37.26908 123.80520</span> +<span class="r-out co"><span class="r-pr">#></span> C1 11.23131 37.30961</span> +<span class="r-out co"><span class="r-pr">#></span> A2 28.50624 94.69567</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-in"><span> <span class="co"># }</span></span></span> +<span class="r-in"><span> <span class="co"># Compare with the results obtained in the original publication</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">schaefer07_complex_results</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> compound parameter KinGUI ModelMaker deviation</span> +<span class="r-out co"><span class="r-pr">#></span> 1 parent degradation rate 0.0496 0.0506 2.0</span> +<span class="r-out co"><span class="r-pr">#></span> 2 parent DT50 13.9900 13.6900 2.2</span> +<span class="r-out co"><span class="r-pr">#></span> 3 metabolite A1 formation fraction 0.3803 0.3696 2.9</span> +<span class="r-out co"><span class="r-pr">#></span> 4 metabolite A1 degradation rate 0.0139 0.0136 2.2</span> +<span class="r-out co"><span class="r-pr">#></span> 5 metabolite A1 DT50 49.9600 50.8900 1.8</span> +<span class="r-out co"><span class="r-pr">#></span> 6 metabolite B1 formation fraction 0.1866 0.1818 2.6</span> +<span class="r-out co"><span class="r-pr">#></span> 7 metabolite B1 degradation rate 0.0175 0.0172 1.7</span> +<span class="r-out co"><span class="r-pr">#></span> 8 metabolite B1 DT50 39.6100 40.2400 1.6</span> +<span class="r-out co"><span class="r-pr">#></span> 9 metabolite C1 formation fraction 0.4331 0.4486 3.5</span> +<span class="r-out co"><span class="r-pr">#></span> 10 metabolite C1 degradation rate 0.0638 0.0700 8.9</span> +<span class="r-out co"><span class="r-pr">#></span> 11 metabolite C1 DT50 10.8700 9.9000 9.8</span> +<span class="r-out co"><span class="r-pr">#></span> 12 metabolite A2 formation fraction 0.4529 0.4559 0.7</span> +<span class="r-out co"><span class="r-pr">#></span> 13 metabolite A2 degradation rate 0.0245 0.0244 0.4</span> +<span class="r-out co"><span class="r-pr">#></span> 14 metabolite A2 DT50 28.2400 28.4500 0.7</span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/set_nd_nq.html b/docs/dev/reference/set_nd_nq.html index 6c6a5d46..fab0a72d 100644 --- a/docs/dev/reference/set_nd_nq.html +++ b/docs/dev/reference/set_nd_nq.html @@ -21,7 +21,7 @@ it automates the proposal of Boesten et al (2015)."><meta name="robots" content= </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -63,7 +63,10 @@ it automates the proposal of Boesten et al (2015)."><meta name="robots" content= <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/sigma_twocomp-1.png b/docs/dev/reference/sigma_twocomp-1.png Binary files differindex 6e61684e..0353b72c 100644 --- a/docs/dev/reference/sigma_twocomp-1.png +++ b/docs/dev/reference/sigma_twocomp-1.png diff --git a/docs/dev/reference/sigma_twocomp.html b/docs/dev/reference/sigma_twocomp.html index b7d295b2..292bf8e8 100644 --- a/docs/dev/reference/sigma_twocomp.html +++ b/docs/dev/reference/sigma_twocomp.html @@ -1,68 +1,13 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - <head> - <meta charset="utf-8"> -<meta http-equiv="X-UA-Compatible" content="IE=edge"> -<meta name="viewport" content="width=device-width, initial-scale=1.0"> - -<title>Two-component error model — sigma_twocomp • mkin</title> - - -<!-- jquery --> 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describing the standard deviation of the measurement error in +dependence of the measured value \(y\):"><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - - - </head> - - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -73,23 +18,21 @@ dependence of the measured value \(y\):" /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -99,48 +42,50 @@ dependence of the measured value \(y\):" /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Two-component error model</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/sigma_twocomp.R'><code>R/sigma_twocomp.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/sigma_twocomp.R" class="external-link"><code>R/sigma_twocomp.R</code></a></small> <div class="hidden name"><code>sigma_twocomp.Rd</code></div> </div> @@ -149,40 +94,43 @@ dependence of the measured value \(y\):" /> dependence of the measured value \(y\):</p> </div> - <pre class="usage"><span class='fu'>sigma_twocomp</span><span class='op'>(</span><span class='va'>y</span>, <span class='va'>sigma_low</span>, <span class='va'>rsd_high</span><span class='op'>)</span></pre> - - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>y</th> - <td><p>The magnitude of the observed value</p></td> - </tr> - <tr> - <th>sigma_low</th> - <td><p>The asymptotic minimum of the standard deviation for low -observed values</p></td> - </tr> - <tr> - <th>rsd_high</th> - <td><p>The coefficient describing the increase of the standard -deviation with the magnitude of the observed value</p></td> - </tr> - </table> - - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> - - <p>The standard deviation of the response variable.</p> - <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">sigma_twocomp</span><span class="op">(</span><span class="va">y</span>, <span class="va">sigma_low</span>, <span class="va">rsd_high</span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>y</dt> +<dd><p>The magnitude of the observed value</p></dd> + +<dt>sigma_low</dt> +<dd><p>The asymptotic minimum of the standard deviation for low +observed values</p></dd> + + +<dt>rsd_high</dt> +<dd><p>The coefficient describing the increase of the standard +deviation with the magnitude of the observed value</p></dd> + +</dl></div> + <div id="value"> + <h2>Value</h2> + + +<p>The standard deviation of the response variable.</p> + </div> + <div id="details"> + <h2>Details</h2> <p>$$\sigma = \sqrt{ \sigma_{low}^2 + y^2 * {rsd}_{high}^2}$$ sigma = sqrt(sigma_low^2 + y^2 * rsd_high^2)</p> <p>This is the error model used for example by Werner et al. (1978). The model proposed by Rocke and Lorenzato (1995) can be written in this form as well, but assumes approximate lognormal distribution of errors for high values of y.</p> - <h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2> - + </div> + <div id="references"> + <h2>References</h2> <p>Werner, Mario, Brooks, Samuel H., and Knott, Lancaster B. (1978) Additive, Multiplicative, and Mixed Analytical Errors. Clinical Chemistry 24(11), 1895-1898.</p> @@ -190,61 +138,62 @@ Additive, Multiplicative, and Mixed Analytical Errors. Clinical Chemistry measurement error in analytical chemistry. Technometrics 37(2), 176-184.</p> <p>Ranke J and Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical Degradation Data. <em>Environments</em> 6(12) 124 -doi: <a href='https://doi.org/10.3390/environments6120124'>10.3390/environments6120124</a> +<a href="https://doi.org/10.3390/environments6120124" class="external-link">doi:10.3390/environments6120124</a> .</p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'><span class='va'>times</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>3</span>, <span class='fl'>7</span>, <span class='fl'>14</span>, <span class='fl'>28</span>, <span class='fl'>60</span>, <span class='fl'>90</span>, <span class='fl'>120</span><span class='op'>)</span> -<span class='va'>d_pred</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a></span><span class='op'>(</span>time <span class='op'>=</span> <span class='va'>times</span>, parent <span class='op'>=</span> <span class='fl'>100</span> <span class='op'>*</span> <span class='fu'><a href='https://rdrr.io/r/base/Log.html'>exp</a></span><span class='op'>(</span><span class='op'>-</span> <span class='fl'>0.03</span> <span class='op'>*</span> <span class='va'>times</span><span class='op'>)</span><span class='op'>)</span> -<span class='fu'><a href='https://rdrr.io/r/base/Random.html'>set.seed</a></span><span class='op'>(</span><span class='fl'>123456</span><span class='op'>)</span> -<span class='va'>d_syn</span> <span class='op'><-</span> <span class='fu'><a href='add_err.html'>add_err</a></span><span class='op'>(</span><span class='va'>d_pred</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>y</span><span class='op'>)</span> <span class='fu'>sigma_twocomp</span><span class='op'>(</span><span class='va'>y</span>, <span class='fl'>1</span>, <span class='fl'>0.07</span><span class='op'>)</span>, - reps <span class='op'>=</span> <span class='fl'>2</span>, n <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span> -<span class='va'>f_nls</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/nls.html'>nls</a></span><span class='op'>(</span><span class='va'>value</span> <span class='op'>~</span> <span class='fu'><a href='https://rdrr.io/r/stats/SSasymp.html'>SSasymp</a></span><span class='op'>(</span><span class='va'>time</span>, <span class='fl'>0</span>, <span class='va'>parent_0</span>, <span class='va'>lrc</span><span class='op'>)</span>, data <span class='op'>=</span> <span class='va'>d_syn</span>, - start <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>parent_0 <span class='op'>=</span> <span class='fl'>100</span>, lrc <span class='op'>=</span> <span class='op'>-</span><span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span> -<span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://svn.r-project.org/R-packages/trunk/nlme/'>nlme</a></span><span class='op'>)</span> -<span class='va'>f_gnls</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/gnls.html'>gnls</a></span><span class='op'>(</span><span class='va'>value</span> <span class='op'>~</span> <span class='fu'><a href='https://rdrr.io/r/stats/SSasymp.html'>SSasymp</a></span><span class='op'>(</span><span class='va'>time</span>, <span class='fl'>0</span>, <span class='va'>parent_0</span>, <span class='va'>lrc</span><span class='op'>)</span>, - data <span class='op'>=</span> <span class='va'>d_syn</span>, na.action <span class='op'>=</span> <span class='va'>na.omit</span>, - start <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>parent_0 <span class='op'>=</span> <span class='fl'>100</span>, lrc <span class='op'>=</span> <span class='op'>-</span><span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span> -<span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/length.html'>length</a></span><span class='op'>(</span><span class='fu'>findFunction</span><span class='op'>(</span><span class='st'>"varConstProp"</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>></span> <span class='fl'>0</span><span class='op'>)</span> <span class='op'>{</span> - <span class='va'>f_gnls_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span><span class='op'>(</span><span class='va'>f_gnls</span>, weights <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/varConstProp.html'>varConstProp</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> - <span class='va'>f_gnls_tc_sf</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span><span class='op'>(</span><span class='va'>f_gnls_tc</span>, control <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>sigma <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span> -<span class='op'>}</span> -<span class='va'>f_mkin</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='va'>d_syn</span>, error_model <span class='op'>=</span> <span class='st'>"const"</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='va'>f_mkin_tc</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='va'>d_syn</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='fu'><a href='plot.mkinfit.html'>plot_res</a></span><span class='op'>(</span><span class='va'>f_mkin_tc</span>, standardized <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -</div><div class='img'><img src='sigma_twocomp-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f_nls</span>, <span class='va'>f_gnls</span>, <span class='va'>f_gnls_tc</span>, <span class='va'>f_gnls_tc_sf</span>, <span class='va'>f_mkin</span>, <span class='va'>f_mkin_tc</span><span class='op'>)</span> -</div><div class='output co'>#> df AIC -#> f_nls 3 114.4817 -#> f_gnls 3 114.4817 -#> f_gnls_tc 5 103.6447 -#> f_gnls_tc_sf 4 101.6447 -#> f_mkin 3 114.4817 -#> f_mkin_tc 4 101.6446</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">times</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">d_pred</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>time <span class="op">=</span> <span class="va">times</span>, parent <span class="op">=</span> <span class="fl">100</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">exp</a></span><span class="op">(</span><span class="op">-</span> <span class="fl">0.03</span> <span class="op">*</span> <span class="va">times</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123456</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">d_syn</span> <span class="op"><-</span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_pred</span>, <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="fu">sigma_twocomp</span><span class="op">(</span><span class="va">y</span>, <span class="fl">1</span>, <span class="fl">0.07</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> reps <span class="op">=</span> <span class="fl">2</span>, n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span></span> +<span class="r-in"><span><span class="va">f_nls</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/nls.html" class="external-link">nls</a></span><span class="op">(</span><span class="va">value</span> <span class="op">~</span> <span class="fu"><a href="https://rdrr.io/r/stats/SSasymp.html" class="external-link">SSasymp</a></span><span class="op">(</span><span class="va">time</span>, <span class="fl">0</span>, <span class="va">parent_0</span>, <span class="va">lrc</span><span class="op">)</span>, data <span class="op">=</span> <span class="va">d_syn</span>,</span></span> +<span class="r-in"><span> start <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>parent_0 <span class="op">=</span> <span class="fl">100</span>, lrc <span class="op">=</span> <span class="op">-</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://svn.r-project.org/R-packages/trunk/nlme/" class="external-link">nlme</a></span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">f_gnls</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/gnls.html" class="external-link">gnls</a></span><span class="op">(</span><span class="va">value</span> <span class="op">~</span> <span class="fu"><a href="https://rdrr.io/r/stats/SSasymp.html" class="external-link">SSasymp</a></span><span class="op">(</span><span class="va">time</span>, <span class="fl">0</span>, <span class="va">parent_0</span>, <span class="va">lrc</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> data <span class="op">=</span> <span class="va">d_syn</span>, na.action <span class="op">=</span> <span class="va">na.omit</span>,</span></span> +<span class="r-in"><span> start <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>parent_0 <span class="op">=</span> <span class="fl">100</span>, lrc <span class="op">=</span> <span class="op">-</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="fu">findFunction</span><span class="op">(</span><span class="st">"varConstProp"</span><span class="op">)</span><span class="op">)</span> <span class="op">></span> <span class="fl">0</span><span class="op">)</span> <span class="op">{</span></span></span> +<span class="r-in"><span> <span class="va">f_gnls_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_gnls</span>, weights <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/varConstProp.html" class="external-link">varConstProp</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">f_gnls_tc_sf</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_gnls_tc</span>, control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>sigma <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="op">}</span></span></span> +<span class="r-in"><span><span class="va">f_mkin</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">d_syn</span>, error_model <span class="op">=</span> <span class="st">"const"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">f_mkin_tc</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">d_syn</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">f_mkin_tc</span>, standardized <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="sigma_twocomp-1.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_nls</span>, <span class="va">f_gnls</span>, <span class="va">f_gnls_tc</span>, <span class="va">f_gnls_tc_sf</span>, <span class="va">f_mkin</span>, <span class="va">f_mkin_tc</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> df AIC</span> +<span class="r-out co"><span class="r-pr">#></span> f_nls 3 114.4817</span> +<span class="r-out co"><span class="r-pr">#></span> f_gnls 3 114.4817</span> +<span class="r-out co"><span class="r-pr">#></span> f_gnls_tc 5 103.6447</span> +<span class="r-out co"><span class="r-pr">#></span> f_gnls_tc_sf 4 101.6447</span> +<span class="r-out co"><span class="r-pr">#></span> f_mkin 3 114.4817</span> +<span class="r-out co"><span class="r-pr">#></span> f_mkin_tc 4 101.6446</span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/status.html b/docs/dev/reference/status.html index 4c856100..c3516f07 100644 --- a/docs/dev/reference/status.html +++ b/docs/dev/reference/status.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -59,7 +59,10 @@ <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/summary.mkinfit.html b/docs/dev/reference/summary.mkinfit.html index 3994a424..ad8432bf 100644 --- a/docs/dev/reference/summary.mkinfit.html +++ b/docs/dev/reference/summary.mkinfit.html @@ -21,7 +21,7 @@ values."><meta name="robots" content="noindex"><!-- mathjax --><script src="http </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -63,7 +63,10 @@ values."><meta name="robots" content="noindex"><!-- mathjax --><script src="http <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -211,10 +214,10 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, <h2>Examples</h2> <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> <span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_A</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.2.0 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.2.2 </span> <span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Mon Nov 14 21:06:31 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Mon Nov 14 21:06:31 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Nov 24 08:11:06 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Nov 24 08:11:06 2022 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - k_parent * parent</span> diff --git a/docs/dev/reference/summary.mmkin.html b/docs/dev/reference/summary.mmkin.html index 96c2d0e5..0c0248fb 100644 --- a/docs/dev/reference/summary.mmkin.html +++ b/docs/dev/reference/summary.mmkin.html @@ -18,7 +18,7 @@ and gives an overview of ill-defined parameters calculated by illparms."><meta n </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -60,7 +60,10 @@ and gives an overview of ill-defined parameters calculated by illparms."><meta n <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -132,7 +135,7 @@ and gives an overview of ill-defined parameters calculated by <a href="illparms. <span class="r-in"><span> quiet <span class="op">=</span> <span class="cn">TRUE</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span> <span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fits</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Error model: Constant variance </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted in 0.767 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted in 0.842 s</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Status:</span> <span class="r-out co"><span class="r-pr">#></span> dataset</span> diff --git a/docs/dev/reference/summary.nlme.mmkin.html b/docs/dev/reference/summary.nlme.mmkin.html index 067efcfe..dcf2bd3d 100644 --- a/docs/dev/reference/summary.nlme.mmkin.html +++ b/docs/dev/reference/summary.nlme.mmkin.html @@ -21,7 +21,7 @@ endpoints such as formation fractions and DT50 values. Optionally </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.2</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -48,19 +48,25 @@ endpoints such as formation fractions and DT50 values. Optionally <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -229,11 +235,11 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> <span class="r-in"><span><span class="va">f_nlme</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">)</span></span></span> <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Iteration 4, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_nlme</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> nlme version used for fitting: 3.1.158 </span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.1.2 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.1 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Wed Aug 10 15:27:32 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Wed Aug 10 15:27:32 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> nlme version used for fitting: 3.1.160 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.2.2 </span> +<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Nov 24 08:11:11 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Nov 24 08:11:11 2022 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - k_parent * parent</span> @@ -243,7 +249,7 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type analytical </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted in 0.534 s using 4 iterations</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted in 0.542 s using 4 iterations</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Variance model: Two-component variance function </span> <span class="r-out co"><span class="r-pr">#></span> </span> diff --git a/docs/dev/reference/summary.saem.mmkin.html b/docs/dev/reference/summary.saem.mmkin.html index d8a41356..3b5869f1 100644 --- a/docs/dev/reference/summary.saem.mmkin.html +++ b/docs/dev/reference/summary.saem.mmkin.html @@ -21,7 +21,7 @@ endpoints such as formation fractions and DT50 values. Optionally </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -63,7 +63,10 @@ endpoints such as formation fractions and DT50 values. Optionally <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -99,7 +102,7 @@ endpoints such as formation fractions and DT50 values. Optionally <div id="ref-usage"> <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for saem.mmkin</span></span> -<span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, data <span class="op">=</span> <span class="cn">FALSE</span>, verbose <span class="op">=</span> <span class="cn">FALSE</span>, distimes <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, data <span class="op">=</span> <span class="cn">FALSE</span>, verbose <span class="op">=</span> <span class="cn">FALSE</span>, distimes <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span> <span></span> <span><span class="co"># S3 method for summary.saem.mmkin</span></span> <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, verbose <span class="op">=</span> <span class="va">x</span><span class="op">$</span><span class="va">verbose</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> @@ -243,56 +246,58 @@ saemix authors for the parts inherited from saemix.</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Likelihood computed by importance sampling</span> <span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> -<span class="r-out co"><span class="r-pr">#></span> 828.1 822.7 -400.1</span> +<span class="r-out co"><span class="r-pr">#></span> 810.8 805.4 -391.4</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Fitted parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> estimate lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 100.74378 97.81291 103.67465</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_m1 -4.06168 -4.17104 -3.95231</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.92584 -1.31273 -0.53894</span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.81914 -3.60206 -2.03623</span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 -3.63916 -4.32672 -2.95161</span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.02927 -1.15247 1.09394</span> -<span class="r-out co"><span class="r-pr">#></span> a.1 0.86164 0.67928 1.04400</span> -<span class="r-out co"><span class="r-pr">#></span> b.1 0.07973 0.06437 0.09509</span> -<span class="r-out co"><span class="r-pr">#></span> SD.parent_0 0.73313 -7.46512 8.93137</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k_m1 0.06488 -0.06041 0.19017</span> -<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.41955 0.15206 0.68705</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 0.81750 0.29140 1.34361</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 0.75265 0.27939 1.22590</span> -<span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.34411 -1.70964 2.39786</span> +<span class="r-out co"><span class="r-pr">#></span> estimate lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 100.86947 97.81542 103.92353</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_m1 -4.06947 -4.16944 -3.96950</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.93256 -1.34200 -0.52312</span> +<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.37017 -2.72660 -2.01375</span> +<span class="r-out co"><span class="r-pr">#></span> log_k2 -4.06264 -4.21344 -3.91184</span> +<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.02174 -0.45898 0.41549</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 0.87598 0.67275 1.07922</span> +<span class="r-out co"><span class="r-pr">#></span> b.1 0.07949 0.06389 0.09509</span> +<span class="r-out co"><span class="r-pr">#></span> SD.parent_0 0.19170 -30.36286 30.74626</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_m1 0.01883 -0.28736 0.32502</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.44300 0.16391 0.72209</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 0.35320 0.09661 0.60978</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 0.13707 0.02359 0.25056</span> +<span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.37478 0.04490 0.70467</span> <span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> [1] "sd(parent_0)" "sd(log_k_m1)" "sd(g_qlogis)"</span> -<span class="r-in"><span><span class="va">f_saem_dfop_sfo_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>, covariance.model <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/diag.html" class="external-link">diag</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] "sd(parent_0)" "sd(log_k_m1)"</span> +<span class="r-in"><span><span class="va">f_saem_dfop_sfo_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>,</span></span> +<span class="r-in"><span> no_random_effect <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"parent_0"</span>, <span class="st">"log_k_m1"</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo_2</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/intervals.html" class="external-link">intervals</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo_2</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Approximate 95% confidence intervals</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Fixed effects:</span> <span class="r-out co"><span class="r-pr">#></span> lower est. upper</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 97.54844979 100.46239264 103.37633550</span> -<span class="r-out co"><span class="r-pr">#></span> k_m1 0.01575805 0.01729111 0.01897331</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.21014925 0.28626877 0.37680664</span> -<span class="r-out co"><span class="r-pr">#></span> k1 0.02651112 0.05601399 0.11834908</span> -<span class="r-out co"><span class="r-pr">#></span> k2 0.01326524 0.02649799 0.05293107</span> -<span class="r-out co"><span class="r-pr">#></span> g 0.31467778 0.51297098 0.70726363</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 98.36731429 101.42508066 104.48284703</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 0.01513234 0.01670094 0.01843214</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.20221431 0.27608850 0.36461630</span> +<span class="r-out co"><span class="r-pr">#></span> k1 0.06915073 0.09759718 0.13774560</span> +<span class="r-out co"><span class="r-pr">#></span> k2 0.01487068 0.01740389 0.02036863</span> +<span class="r-out co"><span class="r-pr">#></span> g 0.37365671 0.48384821 0.59563299</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Random effects:</span> -<span class="r-out co"><span class="r-pr">#></span> lower est. upper</span> -<span class="r-out co"><span class="r-pr">#></span> sd(f_parent_qlogis) 0.1658367 0.4471180 0.7283993</span> -<span class="r-out co"><span class="r-pr">#></span> sd(log_k1) 0.2768757 0.7929203 1.3089649</span> -<span class="r-out co"><span class="r-pr">#></span> sd(log_k2) 0.2693629 0.7566116 1.2438602</span> +<span class="r-out co"><span class="r-pr">#></span> lower est. upper</span> +<span class="r-out co"><span class="r-pr">#></span> sd(f_parent_qlogis) 0.16439770 0.4427585 0.7211193</span> +<span class="r-out co"><span class="r-pr">#></span> sd(log_k1) 0.08304243 0.3345213 0.5860002</span> +<span class="r-out co"><span class="r-pr">#></span> sd(log_k2) 0.03146410 0.1490210 0.2665779</span> +<span class="r-out co"><span class="r-pr">#></span> sd(g_qlogis) 0.06216385 0.4023430 0.7425221</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> lower est. upper</span> -<span class="r-out co"><span class="r-pr">#></span> a.1 0.70273100 0.88750764 1.07228428</span> -<span class="r-out co"><span class="r-pr">#></span> b.1 0.06781347 0.08328016 0.09874685</span> -<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo_2</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> a.1 0.67696663 0.87777355 1.07858048</span> +<span class="r-out co"><span class="r-pr">#></span> b.1 0.06363957 0.07878001 0.09392044</span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo_2</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> saemix version used for fitting: 3.2 </span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.2.0 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.2.2 </span> <span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Tue Nov 1 14:12:50 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Tue Nov 1 14:12:50 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Dec 15 14:47:14 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Dec 15 14:47:14 2022 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span> @@ -307,12 +312,12 @@ saemix authors for the parts inherited from saemix.</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type analytical </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted in 25.006 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted in 9.623 s</span> <span class="r-out co"><span class="r-pr">#></span> Using 300, 100 iterations and 10 chains</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Variance model: Two-component variance function </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Mean of starting values for individual parameters:</span> +<span class="r-out co"><span class="r-pr">#></span> Starting values for degradation parameters:</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 </span> <span class="r-out co"><span class="r-pr">#></span> 101.65645 -4.05368 -0.94311 -2.35943 -4.07006 </span> <span class="r-out co"><span class="r-pr">#></span> g_qlogis </span> @@ -321,237 +326,291 @@ saemix authors for the parts inherited from saemix.</p> <span class="r-out co"><span class="r-pr">#></span> Fixed degradation parameter values:</span> <span class="r-out co"><span class="r-pr">#></span> None</span> <span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Starting values for random effects (square root of initial entries in omega):</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 g_qlogis</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 6.742 0.0000 0.0000 0.0000 0.0000 0.000</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_m1 0.000 0.2236 0.0000 0.0000 0.0000 0.000</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis 0.000 0.0000 0.5572 0.0000 0.0000 0.000</span> +<span class="r-out co"><span class="r-pr">#></span> log_k1 0.000 0.0000 0.0000 0.8031 0.0000 0.000</span> +<span class="r-out co"><span class="r-pr">#></span> log_k2 0.000 0.0000 0.0000 0.0000 0.2931 0.000</span> +<span class="r-out co"><span class="r-pr">#></span> g_qlogis 0.000 0.0000 0.0000 0.0000 0.0000 0.807</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Starting values for error model parameters:</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 b.1 </span> +<span class="r-out co"><span class="r-pr">#></span> 1 1 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Results:</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Likelihood computed by importance sampling</span> -<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> -<span class="r-out co"><span class="r-pr">#></span> 825.6 821.3 -401.8</span> +<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> +<span class="r-out co"><span class="r-pr">#></span> 807 802.3 -391.5</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Optimised parameters:</span> <span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 100.46239 97.54845 103.37634</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_m1 -4.05756 -4.15040 -3.96472</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.91358 -1.32403 -0.50312</span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.88215 -3.63019 -2.13412</span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 -3.63069 -4.32261 -2.93876</span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis 0.05190 -0.77834 0.88213</span> -<span class="r-out co"><span class="r-pr">#></span> a.1 0.88751 0.70273 1.07228</span> -<span class="r-out co"><span class="r-pr">#></span> b.1 0.08328 0.06781 0.09875</span> -<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.44712 0.16584 0.72840</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 0.79292 0.27688 1.30896</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 0.75661 0.26936 1.24386</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 101.42508 98.36731 104.48285</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_m1 -4.09229 -4.19092 -3.99366</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.96395 -1.37251 -0.55538</span> +<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.32691 -2.67147 -1.98235</span> +<span class="r-out co"><span class="r-pr">#></span> log_k2 -4.05106 -4.20836 -3.89376</span> +<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.06463 -0.51656 0.38730</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 0.87777 0.67697 1.07858</span> +<span class="r-out co"><span class="r-pr">#></span> b.1 0.07878 0.06364 0.09392</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.44276 0.16440 0.72112</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 0.33452 0.08304 0.58600</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 0.14902 0.03146 0.26658</span> +<span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.40234 0.06216 0.74252</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Correlation: </span> <span class="r-out co"><span class="r-pr">#></span> parnt_0 lg_k_m1 f_prnt_ log_k1 log_k2 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_m1 -0.4102 </span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.2113 0.2439 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 0.1308 -0.1305 -0.0504 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 -0.0383 0.0592 0.0151 0.0001 </span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.0029 -0.0118 0.0131 -0.2547 -0.1942</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_m1 -0.4693 </span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.2378 0.2595 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k1 0.1720 -0.1593 -0.0669 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k2 0.0179 0.0594 0.0035 0.1995 </span> +<span class="r-out co"><span class="r-pr">#></span> g_qlogis 0.1073 -0.1060 -0.0322 -0.2299 -0.3168</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Random effects:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.4471 0.1658 0.7284</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 0.7929 0.2769 1.3090</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 0.7566 0.2694 1.2439</span> +<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.4428 0.16440 0.7211</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 0.3345 0.08304 0.5860</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 0.1490 0.03146 0.2666</span> +<span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.4023 0.06216 0.7425</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Variance model:</span> <span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> a.1 0.88751 0.70273 1.07228</span> -<span class="r-out co"><span class="r-pr">#></span> b.1 0.08328 0.06781 0.09875</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 0.87777 0.67697 1.07858</span> +<span class="r-out co"><span class="r-pr">#></span> b.1 0.07878 0.06364 0.09392</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Backtransformed parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 100.46239 97.54845 103.37634</span> -<span class="r-out co"><span class="r-pr">#></span> k_m1 0.01729 0.01576 0.01897</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.28627 0.21015 0.37681</span> -<span class="r-out co"><span class="r-pr">#></span> k1 0.05601 0.02651 0.11835</span> -<span class="r-out co"><span class="r-pr">#></span> k2 0.02650 0.01327 0.05293</span> -<span class="r-out co"><span class="r-pr">#></span> g 0.51297 0.31468 0.70726</span> +<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 101.4251 98.36731 104.48285</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 0.0167 0.01513 0.01843</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.2761 0.20221 0.36462</span> +<span class="r-out co"><span class="r-pr">#></span> k1 0.0976 0.06915 0.13775</span> +<span class="r-out co"><span class="r-pr">#></span> k2 0.0174 0.01487 0.02037</span> +<span class="r-out co"><span class="r-pr">#></span> g 0.4838 0.37366 0.59563</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Resulting formation fractions:</span> <span class="r-out co"><span class="r-pr">#></span> ff</span> -<span class="r-out co"><span class="r-pr">#></span> parent_m1 0.2863</span> -<span class="r-out co"><span class="r-pr">#></span> parent_sink 0.7137</span> +<span class="r-out co"><span class="r-pr">#></span> parent_m1 0.2761</span> +<span class="r-out co"><span class="r-pr">#></span> parent_sink 0.7239</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Estimated disappearance times:</span> <span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_k1 DT50_k2</span> -<span class="r-out co"><span class="r-pr">#></span> parent 17.44 65.15 19.61 12.37 26.16</span> -<span class="r-out co"><span class="r-pr">#></span> m1 40.09 133.17 NA NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> parent 15.54 94.33 28.4 7.102 39.83</span> +<span class="r-out co"><span class="r-pr">#></span> m1 41.50 137.87 NA NA NA</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Data:</span> -<span class="r-out co"><span class="r-pr">#></span> ds name time observed predicted residual std standardized</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 0 89.8 1.005e+02 -10.662393 8.4135 -1.267301</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 0 104.1 1.005e+02 3.637607 8.4135 0.432355</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 1 88.7 9.576e+01 -7.063498 8.0244 -0.880249</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 1 95.5 9.576e+01 -0.263498 8.0244 -0.032837</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 3 81.8 8.717e+01 -5.369491 7.3135 -0.734185</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 3 94.5 8.717e+01 7.330509 7.3135 1.002320</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 7 71.5 7.274e+01 -1.238672 6.1224 -0.202319</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 7 70.3 7.274e+01 -2.438672 6.1224 -0.398322</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 14 54.2 5.418e+01 0.022691 4.5984 0.004935</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 14 49.6 5.418e+01 -4.577309 4.5984 -0.995423</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 28 31.5 3.241e+01 -0.914545 2.8416 -0.321837</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 28 28.8 3.241e+01 -3.614545 2.8416 -1.271993</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 60 12.1 1.283e+01 -0.730904 1.3891 -0.526186</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 60 13.6 1.283e+01 0.769096 1.3891 0.553681</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 90 6.2 6.128e+00 0.071981 1.0238 0.070309</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 90 8.3 6.128e+00 2.171981 1.0238 2.121538</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 120 2.2 3.022e+00 -0.822164 0.9225 -0.891230</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 120 2.4 3.022e+00 -0.622164 0.9225 -0.674429</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 1 0.3 1.163e+00 -0.863423 0.8928 -0.967116</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 1 0.2 1.163e+00 -0.963423 0.8928 -1.079126</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 3 2.2 3.233e+00 -1.032930 0.9274 -1.113734</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 3 3.0 3.233e+00 -0.232930 0.9274 -0.251152</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 7 6.5 6.495e+00 0.005314 1.0393 0.005113</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 7 5.0 6.495e+00 -1.494686 1.0393 -1.438116</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 14 10.2 1.010e+01 0.096372 1.2230 0.078801</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 14 9.5 1.010e+01 -0.603628 1.2230 -0.493572</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 28 12.2 1.269e+01 -0.492073 1.3802 -0.356526</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 28 13.4 1.269e+01 0.707927 1.3802 0.512922</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 60 11.8 1.086e+01 0.944360 1.2669 0.745420</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 60 13.2 1.086e+01 2.344360 1.2669 1.850494</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 90 6.6 7.723e+00 -1.123088 1.0961 -1.024658</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 90 9.3 7.723e+00 1.576912 1.0961 1.438708</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 120 3.5 5.184e+00 -1.683936 0.9869 -1.706219</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 120 5.4 5.184e+00 0.216064 0.9869 0.218923</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 0 118.0 1.005e+02 17.537607 8.4135 2.084469</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 0 99.8 1.005e+02 -0.662393 8.4135 -0.078730</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 1 90.2 9.566e+01 -5.456414 8.0156 -0.680727</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 1 94.6 9.566e+01 -1.056414 8.0156 -0.131795</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 3 96.1 8.702e+01 9.082833 7.3009 1.244062</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 3 78.4 8.702e+01 -8.617167 7.3009 -1.180281</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 7 77.9 7.298e+01 4.919834 6.1423 0.800981</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 7 77.7 7.298e+01 4.719834 6.1423 0.768420</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 14 56.0 5.588e+01 0.124003 4.7372 0.026176</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 14 54.7 5.588e+01 -1.175997 4.7372 -0.248245</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 28 36.6 3.719e+01 -0.587869 3.2217 -0.182474</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 28 36.8 3.719e+01 -0.387869 3.2217 -0.120394</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 60 22.1 2.013e+01 1.973728 1.8966 1.040673</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 60 24.7 2.013e+01 4.573728 1.8966 2.411556</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 90 12.4 1.259e+01 -0.185933 1.3734 -0.135379</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 90 10.8 1.259e+01 -1.785933 1.3734 -1.300347</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 120 6.8 7.981e+00 -1.180542 1.1088 -1.064723</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 120 7.9 7.981e+00 -0.080542 1.1088 -0.072640</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 1 1.3 1.306e+00 -0.006246 0.8941 -0.006986</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 3 3.7 3.589e+00 0.110879 0.9365 0.118399</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 3 4.7 3.589e+00 1.110879 0.9365 1.186217</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 7 8.1 7.062e+00 1.038045 1.0647 0.974978</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 7 7.9 7.062e+00 0.838045 1.0647 0.787129</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 14 10.1 1.065e+01 -0.553713 1.2549 -0.441227</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 14 10.3 1.065e+01 -0.353713 1.2549 -0.281857</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 28 10.7 1.284e+01 -2.144854 1.3900 -1.543111</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 28 12.2 1.284e+01 -0.644854 1.3900 -0.463939</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 60 10.7 1.082e+01 -0.115278 1.2645 -0.091165</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 60 12.5 1.082e+01 1.684722 1.2645 1.332337</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 90 9.1 8.014e+00 1.085607 1.1105 0.977610</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 90 7.4 8.014e+00 -0.614393 1.1105 -0.553272</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 120 6.1 5.736e+00 0.363593 1.0079 0.360737</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 120 4.5 5.736e+00 -1.236407 1.0079 -1.226697</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 0 106.2 1.005e+02 5.737607 8.4135 0.681955</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 0 106.9 1.005e+02 6.437607 8.4135 0.765155</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 1 107.4 9.343e+01 13.972212 7.8311 1.784188</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 1 96.1 9.343e+01 2.672212 7.8311 0.341229</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 3 79.4 8.160e+01 -2.196297 6.8531 -0.320484</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 3 82.6 8.160e+01 1.003703 6.8531 0.146460</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 7 63.9 6.464e+01 -0.737220 5.4557 -0.135129</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 7 62.4 6.464e+01 -2.237220 5.4557 -0.410072</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 14 51.0 4.772e+01 3.278433 4.0722 0.805086</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 14 47.1 4.772e+01 -0.621567 4.0722 -0.152638</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 28 36.1 3.303e+01 3.070676 2.8903 1.062400</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 28 36.6 3.303e+01 3.570676 2.8903 1.235391</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 60 20.1 1.929e+01 0.808039 1.8355 0.440235</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 60 19.8 1.929e+01 0.508039 1.8355 0.276789</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 90 11.3 1.209e+01 -0.794443 1.3425 -0.591785</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 90 10.7 1.209e+01 -1.394443 1.3425 -1.038728</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 120 8.2 7.590e+00 0.610002 1.0896 0.559843</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 120 7.3 7.590e+00 -0.289998 1.0896 -0.266152</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 0 0.8 -4.263e-14 0.800000 0.8875 0.901401</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 1 1.8 1.692e+00 0.107665 0.8986 0.119811</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 1 2.3 1.692e+00 0.607665 0.8986 0.676214</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 3 4.2 4.455e+00 -0.255347 0.9619 -0.265449</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 3 4.1 4.455e+00 -0.355347 0.9619 -0.369404</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 7 6.8 8.124e+00 -1.324338 1.1160 -1.186685</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 7 10.1 8.124e+00 1.975662 1.1160 1.770309</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 14 11.4 1.104e+01 0.361860 1.2778 0.283196</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 14 12.8 1.104e+01 1.761860 1.2778 1.378852</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 28 11.5 1.177e+01 -0.272554 1.3225 -0.206097</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 28 10.6 1.177e+01 -1.172554 1.3225 -0.886648</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 60 7.5 9.242e+00 -1.741667 1.1747 -1.482591</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 60 8.6 9.242e+00 -0.641667 1.1747 -0.546218</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 90 7.3 6.837e+00 0.463318 1.0544 0.439398</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 90 8.1 6.837e+00 1.263318 1.0544 1.198095</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 120 5.3 4.906e+00 0.394322 0.9770 0.403595</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 120 3.8 4.906e+00 -1.105678 0.9770 -1.131677</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 0 104.7 1.005e+02 4.237607 8.4135 0.503670</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 0 88.3 1.005e+02 -12.162393 8.4135 -1.445587</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 1 94.2 9.723e+01 -3.029220 8.1458 -0.371877</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 1 94.6 9.723e+01 -2.629220 8.1458 -0.322772</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 3 78.1 9.114e+01 -13.041804 7.6420 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parent 60 23.5 2.110e+01 2.400596 1.9686 1.219459</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 90 11.8 1.183e+01 -0.034206 1.3263 -0.025791</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 90 12.1 1.183e+01 0.265794 1.3263 0.200408</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 120 7.0 6.985e+00 0.014647 1.0612 0.013803</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 120 6.2 6.985e+00 -0.785353 1.0612 -0.740078</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 0 1.6 -1.705e-13 1.600000 0.8875 1.802801</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 1 0.9 6.803e-01 0.219655 0.8893 0.246994</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 3 3.7 1.927e+00 1.773027 0.9019 1.965880</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 3 2.0 1.927e+00 0.073027 0.9019 0.080970</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 7 3.6 4.013e+00 -0.412926 0.9483 -0.435417</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 7 3.8 4.013e+00 -0.212926 0.9483 -0.224523</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 14 7.1 6.604e+00 0.495843 1.0441 0.474896</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 14 6.6 6.604e+00 -0.004157 1.0441 -0.003981</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 28 9.5 9.077e+00 0.422700 1.1658 0.362576</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 28 9.3 9.077e+00 0.222700 1.1658 0.191024</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 60 8.3 8.818e+00 -0.518498 1.1520 -0.450099</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 60 9.0 8.818e+00 0.181502 1.1520 0.157559</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 90 6.6 6.738e+00 -0.137785 1.0500 -0.131222</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 90 7.7 6.738e+00 0.962215 1.0500 0.916383</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 120 3.7 4.794e+00 -1.093754 0.9732 -1.123914</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 120 3.5 4.794e+00 -1.293754 0.9732 -1.329429</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 0 110.4 1.005e+02 9.937607 8.4135 1.181155</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 0 112.1 1.005e+02 11.637607 8.4135 1.383212</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 1 93.5 9.372e+01 -0.215694 7.8550 -0.027460</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 1 91.0 9.372e+01 -2.715694 7.8550 -0.345730</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 3 71.0 8.226e+01 -11.257156 6.9076 -1.629667</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 3 89.7 8.226e+01 7.442844 6.9076 1.077480</span> 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11.3 1.172e+01 -0.423434 1.3194 -0.320923</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 90 11.9 1.172e+01 0.176566 1.3194 0.133820</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 120 9.0 7.281e+00 1.719138 1.0749 1.599402</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 120 8.1 7.281e+00 0.819138 1.0749 0.762086</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 0 0.7 -2.842e-13 0.700000 0.8875 0.788726</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 1 3.0 3.252e+00 -0.252227 0.9279 -0.271821</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 1 2.6 3.252e+00 -0.652227 0.9279 -0.702895</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 3 5.1 8.615e+00 -3.515326 1.1413 -3.080237</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 3 7.5 8.615e+00 -1.115326 1.1413 -0.977283</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 7 16.5 1.588e+01 0.619041 1.5928 0.388661</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 7 19.0 1.588e+01 3.119041 1.5928 1.958272</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 14 22.9 2.189e+01 1.014705 2.0272 0.500543</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 14 23.2 2.189e+01 1.314705 2.0272 0.648529</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 28 22.2 2.369e+01 -1.487604 2.1632 -0.687701</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 28 24.4 2.369e+01 0.712396 2.1632 0.329332</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 60 15.5 1.869e+01 -3.193942 1.7920 -1.782295</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 60 19.8 1.869e+01 1.106058 1.7920 0.617206</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 90 14.9 1.380e+01 1.103454 1.4518 0.760041</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 90 14.2 1.380e+01 0.403454 1.4518 0.277892</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 120 10.9 9.864e+00 1.035963 1.2093 0.856637</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 120 10.4 9.864e+00 0.535963 1.2093 0.443187</span> +<span class="r-out co"><span class="r-pr">#></span> ds name time observed predicted residual std standardized</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 0 89.8 1.014e+02 -11.62508 8.0383 -1.44620</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 0 104.1 1.014e+02 2.67492 8.0383 0.33277</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 1 88.7 9.650e+01 -7.80311 7.6530 -1.01961</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 1 95.5 9.650e+01 -1.00311 7.6530 -0.13107</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 3 81.8 8.753e+01 -5.72638 6.9510 -0.82382</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 3 94.5 8.753e+01 6.97362 6.9510 1.00326</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 7 71.5 7.254e+01 -1.04133 5.7818 -0.18010</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 7 70.3 7.254e+01 -2.24133 5.7818 -0.38765</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 14 54.2 5.349e+01 0.71029 4.3044 0.16502</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 14 49.6 5.349e+01 -3.88971 4.3044 -0.90366</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 28 31.5 3.167e+01 -0.16616 2.6446 -0.06283</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 28 28.8 3.167e+01 -2.86616 2.6446 -1.08379</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 60 12.1 1.279e+01 -0.69287 1.3365 -0.51843</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 60 13.6 1.279e+01 0.80713 1.3365 0.60392</span> 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class="r-out co"><span class="r-pr">#></span> ds 1 m1 120 3.5 5.236e+00 -1.73617 0.9699 -1.79010</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 120 5.4 5.236e+00 0.16383 0.9699 0.16892</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 0 118.0 1.014e+02 16.57492 8.0383 2.06198</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 0 99.8 1.014e+02 -1.62508 8.0383 -0.20217</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 1 90.2 9.599e+01 -5.79045 7.6129 -0.76061</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 1 94.6 9.599e+01 -1.39045 7.6129 -0.18264</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 3 96.1 8.652e+01 9.57931 6.8724 1.39388</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 3 78.4 8.652e+01 -8.12069 6.8724 -1.18164</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 7 77.9 7.197e+01 5.93429 5.7370 1.03439</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 7 77.7 7.197e+01 5.73429 5.7370 0.99953</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 14 56.0 5.555e+01 0.44657 4.4637 0.10005</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 14 54.7 5.555e+01 -0.85343 4.4637 -0.19120</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 28 36.6 3.853e+01 -1.93170 3.1599 -0.61132</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 28 36.8 3.853e+01 -1.73170 3.1599 -0.54803</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 60 22.1 2.110e+01 1.00360 1.8795 0.53396</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 60 24.7 2.110e+01 3.60360 1.8795 1.91728</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 90 12.4 1.250e+01 -0.09712 1.3190 -0.07363</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 90 10.8 1.250e+01 -1.69712 1.3190 -1.28667</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 120 6.8 7.419e+00 -0.61913 1.0546 -0.58709</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 120 7.9 7.419e+00 0.48087 1.0546 0.45599</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 1 1.3 1.422e+00 -0.12194 0.8849 -0.13781</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 3 3.7 3.831e+00 -0.13149 0.9282 -0.14166</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 3 4.7 3.831e+00 0.86851 0.9282 0.93567</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 7 8.1 7.292e+00 0.80812 1.0490 0.77034</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 7 7.9 7.292e+00 0.60812 1.0490 0.57969</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 14 10.1 1.055e+01 -0.45332 1.2090 -0.37495</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 14 10.3 1.055e+01 -0.25332 1.2090 -0.20953</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 28 10.7 1.230e+01 -1.59960 1.3074 -1.22347</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 28 12.2 1.230e+01 -0.09960 1.3074 -0.07618</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 60 10.7 1.065e+01 0.05342 1.2141 0.04400</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 60 12.5 1.065e+01 1.85342 1.2141 1.52661</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 90 9.1 8.196e+00 0.90368 1.0897 0.82930</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 90 7.4 8.196e+00 -0.79632 1.0897 -0.73078</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 120 6.1 5.997e+00 0.10252 0.9969 0.10284</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 120 4.5 5.997e+00 -1.49748 0.9969 -1.50220</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 0 106.2 1.014e+02 4.77492 8.0383 0.59402</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 0 106.9 1.014e+02 5.47492 8.0383 0.68110</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 1 107.4 9.390e+01 13.49935 7.4494 1.81214</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 1 96.1 9.390e+01 2.19935 7.4494 0.29524</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 3 79.4 8.152e+01 -2.12307 6.4821 -0.32753</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 3 82.6 8.152e+01 1.07693 6.4821 0.16614</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 7 63.9 6.446e+01 -0.55834 5.1533 -0.10834</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 7 62.4 6.446e+01 -2.05834 5.1533 -0.39942</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 14 51.0 4.826e+01 2.74073 3.9019 0.70241</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 14 47.1 4.826e+01 -1.15927 3.9019 -0.29711</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 28 36.1 3.424e+01 1.86399 2.8364 0.65718</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 28 36.6 3.424e+01 2.36399 2.8364 0.83346</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 60 20.1 1.968e+01 0.42172 1.7815 0.23672</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 60 19.8 1.968e+01 0.12172 1.7815 0.06833</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 90 11.3 1.195e+01 -0.64633 1.2869 -0.50222</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 90 10.7 1.195e+01 -1.24633 1.2869 -0.96844</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 120 8.2 7.255e+00 0.94532 1.0474 0.90251</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 120 7.3 7.255e+00 0.04532 1.0474 0.04327</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 0 0.8 2.956e-11 0.80000 0.8778 0.91140</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 1 1.8 1.758e+00 0.04187 0.8886 0.04712</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 1 2.3 1.758e+00 0.54187 0.8886 0.60978</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 3 4.2 4.567e+00 -0.36697 0.9486 -0.38683</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 3 4.1 4.567e+00 -0.46697 0.9486 -0.49224</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 7 6.8 8.151e+00 -1.35124 1.0876 -1.24242</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 7 10.1 8.151e+00 1.94876 1.0876 1.79182</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 14 11.4 1.083e+01 0.57098 1.2240 0.46647</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 14 12.8 1.083e+01 1.97098 1.2240 1.61022</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 28 11.5 1.147e+01 0.03175 1.2597 0.02520</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 28 10.6 1.147e+01 -0.86825 1.2597 -0.68928</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 60 7.5 9.298e+00 -1.79834 1.1433 -1.57298</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 60 8.6 9.298e+00 -0.69834 1.1433 -0.61083</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 90 7.3 7.038e+00 0.26249 1.0382 0.25283</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 90 8.1 7.038e+00 1.06249 1.0382 1.02340</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 120 5.3 5.116e+00 0.18417 0.9659 0.19068</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 120 3.8 5.116e+00 -1.31583 0.9659 -1.36232</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 0 104.7 1.014e+02 3.27492 8.0383 0.40741</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 0 88.3 1.014e+02 -13.12508 8.0383 -1.63281</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 1 94.2 9.781e+01 -3.61183 7.7555 -0.46572</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 1 94.6 9.781e+01 -3.21183 7.7555 -0.41414</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 3 78.1 9.110e+01 -13.00467 7.2307 -1.79853</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 3 96.5 9.110e+01 5.39533 7.2307 0.74617</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 7 76.2 7.951e+01 -3.30511 6.3246 -0.52258</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 7 77.8 7.951e+01 -1.70511 6.3246 -0.26960</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 14 70.8 6.376e+01 7.03783 5.0993 1.38016</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 14 67.3 6.376e+01 3.53783 5.0993 0.69379</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 28 43.1 4.340e+01 -0.30456 3.5303 -0.08627</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 28 45.1 4.340e+01 1.69544 3.5303 0.48026</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 60 21.3 2.142e+01 -0.12077 1.9022 -0.06349</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 60 23.5 2.142e+01 2.07923 1.9022 1.09308</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 90 11.8 1.207e+01 -0.26813 1.2940 -0.20721</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 90 12.1 1.207e+01 0.03187 1.2940 0.02463</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 120 7.0 6.954e+00 0.04554 1.0347 0.04402</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 120 6.2 6.954e+00 -0.75446 1.0347 -0.72914</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 0 1.6 1.990e-13 1.60000 0.8778 1.82279</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 1 0.9 7.305e-01 0.16949 0.8797 0.19267</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 3 3.7 2.051e+00 1.64896 0.8925 1.84753</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 3 2.0 2.051e+00 -0.05104 0.8925 -0.05719</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 7 3.6 4.204e+00 -0.60375 0.9382 -0.64354</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 7 3.8 4.204e+00 -0.40375 0.9382 -0.43036</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 14 7.1 6.760e+00 0.34021 1.0267 0.33137</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 14 6.6 6.760e+00 -0.15979 1.0267 -0.15563</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 28 9.5 9.011e+00 0.48856 1.1289 0.43277</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 28 9.3 9.011e+00 0.28856 1.1289 0.25561</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 60 8.3 8.611e+00 -0.31077 1.1093 -0.28014</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 60 9.0 8.611e+00 0.38923 1.1093 0.35086</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 90 6.6 6.678e+00 -0.07753 1.0233 -0.07576</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 90 7.7 6.678e+00 1.02247 1.0233 0.99915</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 120 3.7 4.847e+00 -1.14679 0.9572 -1.19804</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 120 3.5 4.847e+00 -1.34679 0.9572 -1.40698</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 0 110.4 1.014e+02 8.97492 8.0383 1.11651</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 0 112.1 1.014e+02 10.67492 8.0383 1.32800</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 1 93.5 9.466e+01 -1.16118 7.5089 -0.15464</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 1 91.0 9.466e+01 -3.66118 7.5089 -0.48758</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 3 71.0 8.302e+01 -12.01844 6.5988 -1.82130</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 3 89.7 8.302e+01 6.68156 6.5988 1.01254</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 7 60.4 6.563e+01 -5.22574 5.2440 -0.99652</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 7 59.1 6.563e+01 -6.52574 5.2440 -1.24442</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 14 56.5 4.727e+01 9.22621 3.8263 2.41128</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 14 47.0 4.727e+01 -0.27379 3.8263 -0.07156</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 28 30.2 3.103e+01 -0.83405 2.5977 -0.32108</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 28 23.9 3.103e+01 -7.13405 2.5977 -2.74634</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 60 17.0 1.800e+01 -0.99696 1.6675 -0.59787</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 60 18.7 1.800e+01 0.70304 1.6675 0.42161</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 90 11.3 1.167e+01 -0.36809 1.2710 -0.28961</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 90 11.9 1.167e+01 0.23191 1.2710 0.18246</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 120 9.0 7.595e+00 1.40496 1.0623 1.32256</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 120 8.1 7.595e+00 0.50496 1.0623 0.47535</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 0 0.7 0.000e+00 0.70000 0.8778 0.79747</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 1 3.0 3.158e+00 -0.15799 0.9123 -0.17317</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 1 2.6 3.158e+00 -0.55799 0.9123 -0.61160</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 3 5.1 8.443e+00 -3.34286 1.1013 -3.03535</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 3 7.5 8.443e+00 -0.94286 1.1013 -0.85613</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 7 16.5 1.580e+01 0.69781 1.5232 0.45811</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 7 19.0 1.580e+01 3.19781 1.5232 2.09935</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 14 22.9 2.216e+01 0.73604 1.9543 0.37663</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 14 23.2 2.216e+01 1.03604 1.9543 0.53014</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 28 22.2 2.423e+01 -2.03128 2.1011 -0.96678</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 28 24.4 2.423e+01 0.16872 2.1011 0.08030</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 60 15.5 1.876e+01 -3.25610 1.7187 -1.89455</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 60 19.8 1.876e+01 1.04390 1.7187 0.60739</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 90 14.9 1.366e+01 1.23585 1.3890 0.88976</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 90 14.2 1.366e+01 0.53585 1.3890 0.38579</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 120 10.9 9.761e+00 1.13911 1.1670 0.97613</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 120 10.4 9.761e+00 0.63911 1.1670 0.54767</span> +<span class="r-in"><span><span class="co"># Add a correlation between random effects of g and k2</span></span></span> +<span class="r-in"><span><span class="va">cov_model_3</span> <span class="op"><-</span> <span class="va">f_saem_dfop_sfo_2</span><span class="op">$</span><span class="va">so</span><span class="op">@</span><span class="va">model</span><span class="op">@</span><span class="va">covariance.model</span></span></span> +<span class="r-in"><span><span class="va">cov_model_3</span><span class="op">[</span><span class="st">"log_k2"</span>, <span class="st">"g_qlogis"</span><span class="op">]</span> <span class="op"><-</span> <span class="fl">1</span></span></span> +<span class="r-in"><span><span class="va">cov_model_3</span><span class="op">[</span><span class="st">"g_qlogis"</span>, <span class="st">"log_k2"</span><span class="op">]</span> <span class="op"><-</span> <span class="fl">1</span></span></span> +<span class="r-in"><span><span class="va">f_saem_dfop_sfo_3</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>,</span></span> +<span class="r-in"><span> covariance.model <span class="op">=</span> <span class="va">cov_model_3</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/intervals.html" class="external-link">intervals</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo_3</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Approximate 95% confidence intervals</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Fixed effects:</span> +<span class="r-out co"><span class="r-pr">#></span> lower est. upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 98.39888363 101.48951337 104.58014311</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 0.01508704 0.01665986 0.01839665</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.20141557 0.27540583 0.36418131</span> +<span class="r-out co"><span class="r-pr">#></span> k1 0.07708759 0.10430866 0.14114200</span> +<span class="r-out co"><span class="r-pr">#></span> k2 0.01476621 0.01786384 0.02161129</span> +<span class="r-out co"><span class="r-pr">#></span> g 0.33679867 0.45083525 0.57028162</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Random effects:</span> +<span class="r-out co"><span class="r-pr">#></span> lower est. upper</span> +<span class="r-out co"><span class="r-pr">#></span> sd(f_parent_qlogis) 0.38085375 0.4441841 0.5075145</span> +<span class="r-out co"><span class="r-pr">#></span> sd(log_k1) 0.04774819 0.2660384 0.4843286</span> +<span class="r-out co"><span class="r-pr">#></span> sd(log_k2) -0.63842736 0.1977024 1.0338321</span> +<span class="r-out co"><span class="r-pr">#></span> sd(g_qlogis) 0.22711289 0.4502227 0.6733326</span> +<span class="r-out co"><span class="r-pr">#></span> corr(log_k2,g_qlogis) -0.83271473 -0.6176939 -0.4026730</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> lower est. upper</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 0.67347568 0.87437392 1.07527216</span> +<span class="r-out co"><span class="r-pr">#></span> b.1 0.06393032 0.07912417 0.09431802</span> +<span class="r-in"><span><span class="co"># The correlation does not improve the fit judged by AIC and BIC, although</span></span></span> +<span class="r-in"><span><span class="co"># the likelihood is higher with the additional parameter</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>, <span class="va">f_saem_dfop_sfo_2</span>, <span class="va">f_saem_dfop_sfo_3</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Data: 171 observations of 2 variable(s) grouped in 5 datasets</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik</span> +<span class="r-out co"><span class="r-pr">#></span> f_saem_dfop_sfo_2 12 806.96 802.27 -391.48</span> +<span class="r-out co"><span class="r-pr">#></span> f_saem_dfop_sfo_3 13 807.99 802.91 -391.00</span> +<span class="r-out co"><span class="r-pr">#></span> f_saem_dfop_sfo 14 810.83 805.36 -391.42</span> <span class="r-in"><span><span class="co"># }</span></span></span> <span class="r-in"><span></span></span> </code></pre></div> diff --git a/docs/dev/reference/summary_listing.html b/docs/dev/reference/summary_listing.html new file mode 100644 index 00000000..876412cc --- /dev/null +++ b/docs/dev/reference/summary_listing.html @@ -0,0 +1,147 @@ +<!DOCTYPE html> +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Display the output of a summary function according to the output format — summary_listing • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" 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href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Display the output of a summary function according to the output format — summary_listing"><meta property="og:description" content='This function is intended for use in a R markdown code chunk with the chunk +option results = "asis".'><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> +<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> +<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> +<![endif]--></head><body data-spy="scroll" 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data-bs-toggle="dropdown" aria-expanded="false"> + Articles + + <span class="caret"></span> + </a> + <ul class="dropdown-menu" role="menu"><li> + <a href="../articles/mkin.html">Introduction to mkin</a> + </li> + <li> + <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> + </li> + <li> + <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> + </li> + <li> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + </li> + <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> + <li> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> +<li> + <a href="../news/index.html">News</a> +</li> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> + <span class="fab fa-github fa-lg"></span> + + </a> +</li> + </ul></div><!--/.nav-collapse --> + </div><!--/.container --> +</div><!--/.navbar --> + + + + </header><div class="row"> + <div class="col-md-9 contents"> + <div class="page-header"> + <h1>Display the output of a summary function according to the output format</h1> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/summary_listing.R" class="external-link"><code>R/summary_listing.R</code></a></small> + <div class="hidden name"><code>summary_listing.Rd</code></div> + </div> + + <div class="ref-description"> + <p>This function is intended for use in a R markdown code chunk with the chunk +option <code>results = "asis"</code>.</p> + </div> + + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">summary_listing</span><span class="op">(</span><span class="va">object</span>, caption <span class="op">=</span> <span class="cn">NULL</span>, label <span class="op">=</span> <span class="cn">NULL</span>, clearpage <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span></span> +<span><span class="fu">tex_listing</span><span class="op">(</span><span class="va">object</span>, caption <span class="op">=</span> <span class="cn">NULL</span>, label <span class="op">=</span> <span class="cn">NULL</span>, clearpage <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span></span> +<span><span class="fu">html_listing</span><span class="op">(</span><span class="va">object</span>, caption <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>object</dt> +<dd><p>The object for which the summary is to be listed</p></dd> + + +<dt>caption</dt> +<dd><p>An optional caption</p></dd> + + +<dt>label</dt> +<dd><p>An optional label, ignored in html output</p></dd> + + +<dt>clearpage</dt> +<dd><p>Should a new page be started after the listing? Ignored in html output</p></dd> + +</dl></div> + + </div> + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> +</div> + + + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> +</div> + + </footer></div> + + + + + + + </body></html> + diff --git a/docs/dev/reference/synthetic_data_for_UBA_2014-1.png b/docs/dev/reference/synthetic_data_for_UBA_2014-1.png Binary files differindex 89975db5..132380a8 100644 --- a/docs/dev/reference/synthetic_data_for_UBA_2014-1.png +++ b/docs/dev/reference/synthetic_data_for_UBA_2014-1.png diff --git a/docs/dev/reference/synthetic_data_for_UBA_2014.html b/docs/dev/reference/synthetic_data_for_UBA_2014.html index 33a0ace2..729d991a 100644 --- a/docs/dev/reference/synthetic_data_for_UBA_2014.html +++ b/docs/dev/reference/synthetic_data_for_UBA_2014.html @@ -1,46 +1,5 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - <head> - <meta charset="utf-8"> -<meta http-equiv="X-UA-Compatible" content="IE=edge"> -<meta name="viewport" content="width=device-width, initial-scale=1.0"> - -<title>Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014 • mkin</title> - - -<!-- jquery --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script> -<!-- Bootstrap --> - -<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous" /> - -<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script> - -<!-- bootstrap-toc --> -<link rel="stylesheet" href="../bootstrap-toc.css"> -<script src="../bootstrap-toc.js"></script> - -<!-- Font Awesome icons --> -<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous" /> -<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous" /> - -<!-- clipboard.js --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script> - -<!-- headroom.js --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script> -<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script> - -<!-- pkgdown --> -<link href="../pkgdown.css" rel="stylesheet"> -<script src="../pkgdown.js"></script> - - - - -<meta property="og:title" content="Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014" /> -<meta property="og:description" content="The 12 datasets were generated using four different models and three different +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; 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The four models are either the SFO or the DFOP model with either two sequential or two parallel metabolites. Variance component 'a' is based on a normal distribution with standard deviation of 3, @@ -55,28 +14,14 @@ Initial concentrations for metabolites and all values where adding the variance in a value below the assumed limit of detection of 0.1 were set to NA. As an example, the first dataset has the title SFO_lin_a and is based on the SFO model with two sequential metabolites (linear pathway), with added variance component 'a'. -Compare also the code in the example section to see the degradation models." /> - - -<meta name="robots" content="noindex"> - -<!-- mathjax --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script> - -<!--[if lt IE 9]> +Compare also the code in the example section to see the degradation models."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> - - - - </head> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -87,23 +32,21 @@ Compare also the code in the example section to see the degradation models." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -113,44 +56,46 @@ Compare also the code in the example section to see the degradation models." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Synthetic datasets for one parent compound with two metabolites</h1> @@ -177,300 +122,308 @@ Compare also the code in the example section to see the degradation models." /> <p>Compare also the code in the example section to see the degradation models.</p> </div> - <pre class="usage"><span class='va'>synthetic_data_for_UBA_2014</span></pre> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">synthetic_data_for_UBA_2014</span></span></code></pre></div> + </div> + <div id="format"> + <h2>Format</h2> + <p>A list containing twelve datasets as an R6 class defined by <code><a href="mkinds.html">mkinds</a></code>, + each containing, among others, the following components</p><dl><dt><code>title</code></dt> +<dd><p>The name of the dataset, e.g. <code>SFO_lin_a</code></p></dd> - <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2> + <dt><code>data</code></dt> +<dd><p>A data frame with the data in the form expected by <code><a href="mkinfit.html">mkinfit</a></code></p></dd> - <p>A list containing twelve datasets as an R6 class defined by <code><a href='mkinds.html'>mkinds</a></code>, - each containing, among others, the following components</p><dl> - <dt><code>title</code></dt><dd><p>The name of the dataset, e.g. <code>SFO_lin_a</code></p></dd> - <dt><code>data</code></dt><dd><p>A data frame with the data in the form expected by <code><a href='mkinfit.html'>mkinfit</a></code></p></dd> -</dl> - - <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2> - +</dl></div> + <div id="source"> + <h2>Source</h2> <p>Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452</p> <p>Rocke, David M. und Lorenzato, Stefan (1995) A two-component model for measurement error in analytical chemistry. Technometrics 37(2), 176-184.</p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'><span class='co'># \dontrun{</span> -<span class='co'># The data have been generated using the following kinetic models</span> -<span class='va'>m_synth_SFO_lin</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='st'>"M1"</span><span class='op'>)</span>, - M1 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='st'>"M2"</span><span class='op'>)</span>, - M2 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span>, use_of_ff <span class='op'>=</span> <span class='st'>"max"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'> - -<span class='va'>m_synth_SFO_par</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"M1"</span>, <span class='st'>"M2"</span><span class='op'>)</span>, - sink <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span>, - M1 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span>, - M2 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span>, use_of_ff <span class='op'>=</span> <span class='st'>"max"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'> -<span class='va'>m_synth_DFOP_lin</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"DFOP"</span>, to <span class='op'>=</span> <span class='st'>"M1"</span><span class='op'>)</span>, - M1 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='st'>"M2"</span><span class='op'>)</span>, - M2 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span>, use_of_ff <span class='op'>=</span> <span class='st'>"max"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'> -<span class='va'>m_synth_DFOP_par</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"DFOP"</span>, to <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"M1"</span>, <span class='st'>"M2"</span><span class='op'>)</span>, - sink <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span>, - M1 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span>, - M2 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span>, use_of_ff <span class='op'>=</span> <span class='st'>"max"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'> -<span class='co'># The model predictions without intentional error were generated as follows</span> -<span class='va'>sampling_times</span> <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>3</span>, <span class='fl'>7</span>, <span class='fl'>14</span>, <span class='fl'>28</span>, <span class='fl'>60</span>, <span class='fl'>90</span>, <span class='fl'>120</span><span class='op'>)</span> - -<span class='va'>d_synth_SFO_lin</span> <span class='op'><-</span> <span class='fu'><a href='mkinpredict.html'>mkinpredict</a></span><span class='op'>(</span><span class='va'>m_synth_SFO_lin</span>, - <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>k_parent <span class='op'>=</span> <span class='fl'>0.7</span>, f_parent_to_M1 <span class='op'>=</span> <span class='fl'>0.8</span>, - k_M1 <span class='op'>=</span> <span class='fl'>0.3</span>, f_M1_to_M2 <span class='op'>=</span> <span class='fl'>0.7</span>, - k_M2 <span class='op'>=</span> <span class='fl'>0.02</span><span class='op'>)</span>, - <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fl'>100</span>, M1 <span class='op'>=</span> <span class='fl'>0</span>, M2 <span class='op'>=</span> <span class='fl'>0</span><span class='op'>)</span>, - <span class='va'>sampling_times</span><span class='op'>)</span> - -<span class='va'>d_synth_DFOP_lin</span> <span class='op'><-</span> <span class='fu'><a href='mkinpredict.html'>mkinpredict</a></span><span class='op'>(</span><span class='va'>m_synth_DFOP_lin</span>, - <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>k1 <span class='op'>=</span> <span class='fl'>0.2</span>, k2 <span class='op'>=</span> <span class='fl'>0.02</span>, g <span class='op'>=</span> <span class='fl'>0.5</span>, - f_parent_to_M1 <span class='op'>=</span> <span class='fl'>0.5</span>, k_M1 <span class='op'>=</span> <span class='fl'>0.3</span>, - f_M1_to_M2 <span class='op'>=</span> <span class='fl'>0.7</span>, k_M2 <span class='op'>=</span> <span class='fl'>0.02</span><span class='op'>)</span>, - <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fl'>100</span>, M1 <span class='op'>=</span> <span class='fl'>0</span>, M2 <span class='op'>=</span> <span class='fl'>0</span><span class='op'>)</span>, - <span class='va'>sampling_times</span><span class='op'>)</span> - -<span class='va'>d_synth_SFO_par</span> <span class='op'><-</span> <span class='fu'><a href='mkinpredict.html'>mkinpredict</a></span><span class='op'>(</span><span class='va'>m_synth_SFO_par</span>, - <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>k_parent <span class='op'>=</span> <span class='fl'>0.2</span>, - f_parent_to_M1 <span class='op'>=</span> <span class='fl'>0.8</span>, k_M1 <span class='op'>=</span> <span class='fl'>0.01</span>, - f_parent_to_M2 <span class='op'>=</span> <span class='fl'>0.2</span>, k_M2 <span class='op'>=</span> <span class='fl'>0.02</span><span class='op'>)</span>, - <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fl'>100</span>, M1 <span class='op'>=</span> <span class='fl'>0</span>, M2 <span class='op'>=</span> <span class='fl'>0</span><span class='op'>)</span>, - <span class='va'>sampling_times</span><span class='op'>)</span> - -<span class='va'>d_synth_DFOP_par</span> <span class='op'><-</span> <span class='fu'><a href='mkinpredict.html'>mkinpredict</a></span><span class='op'>(</span><span class='va'>m_synth_DFOP_par</span>, - <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>k1 <span class='op'>=</span> <span class='fl'>0.3</span>, k2 <span class='op'>=</span> <span class='fl'>0.02</span>, g <span class='op'>=</span> <span class='fl'>0.7</span>, - f_parent_to_M1 <span class='op'>=</span> <span class='fl'>0.6</span>, k_M1 <span class='op'>=</span> <span class='fl'>0.04</span>, - f_parent_to_M2 <span class='op'>=</span> <span class='fl'>0.4</span>, k_M2 <span class='op'>=</span> <span class='fl'>0.01</span><span class='op'>)</span>, - <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fl'>100</span>, M1 <span class='op'>=</span> <span class='fl'>0</span>, M2 <span class='op'>=</span> <span class='fl'>0</span><span class='op'>)</span>, - <span class='va'>sampling_times</span><span class='op'>)</span> - -<span class='co'># Construct names for datasets with errors</span> -<span class='va'>d_synth_names</span> <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste0</a></span><span class='op'>(</span><span class='st'>"d_synth_"</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"SFO_lin"</span>, <span class='st'>"SFO_par"</span>, - <span class='st'>"DFOP_lin"</span>, <span class='st'>"DFOP_par"</span><span class='op'>)</span><span class='op'>)</span> - -<span class='co'># Original function used or adding errors. The add_err function now published</span> -<span class='co'># with this package is a slightly generalised version where the names of</span> -<span class='co'># secondary compartments that should have an initial value of zero (M1 and M2</span> -<span class='co'># in this case) are not hardcoded any more.</span> -<span class='co'># add_err = function(d, sdfunc, LOD = 0.1, reps = 2, seed = 123456789)</span> -<span class='co'># {</span> -<span class='co'># set.seed(seed)</span> -<span class='co'># d_long = mkin_wide_to_long(d, time = "time")</span> -<span class='co'># d_rep = data.frame(lapply(d_long, rep, each = 2))</span> -<span class='co'># d_rep$value = rnorm(length(d_rep$value), d_rep$value, sdfunc(d_rep$value))</span> -<span class='co'>#</span> -<span class='co'># d_rep[d_rep$time == 0 & d_rep$name %in% c("M1", "M2"), "value"] <- 0</span> -<span class='co'># d_NA <- transform(d_rep, value = ifelse(value < LOD, NA, value))</span> -<span class='co'># d_NA$value <- round(d_NA$value, 1)</span> -<span class='co'># return(d_NA)</span> -<span class='co'># }</span> - -<span class='co'># The following is the simplified version of the two-component model of Rocke</span> -<span class='co'># and Lorenzato (1995)</span> -<span class='va'>sdfunc_twocomp</span> <span class='op'>=</span> <span class='kw'>function</span><span class='op'>(</span><span class='va'>value</span>, <span class='va'>sd_low</span>, <span class='va'>rsd_high</span><span class='op'>)</span> <span class='op'>{</span> - <span class='fu'><a href='https://rdrr.io/r/base/MathFun.html'>sqrt</a></span><span class='op'>(</span><span class='va'>sd_low</span><span class='op'>^</span><span class='fl'>2</span> <span class='op'>+</span> <span class='va'>value</span><span class='op'>^</span><span class='fl'>2</span> <span class='op'>*</span> <span class='va'>rsd_high</span><span class='op'>^</span><span class='fl'>2</span><span class='op'>)</span> -<span class='op'>}</span> - -<span class='co'># Add the errors.</span> -<span class='kw'>for</span> <span class='op'>(</span><span class='va'>d_synth_name</span> <span class='kw'>in</span> <span class='va'>d_synth_names</span><span class='op'>)</span> -<span class='op'>{</span> - <span class='va'>d_synth</span> <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/get.html'>get</a></span><span class='op'>(</span><span class='va'>d_synth_name</span><span class='op'>)</span> - <span class='fu'><a href='https://rdrr.io/r/base/assign.html'>assign</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste0</a></span><span class='op'>(</span><span class='va'>d_synth_name</span>, <span class='st'>"_a"</span><span class='op'>)</span>, <span class='fu'><a href='add_err.html'>add_err</a></span><span class='op'>(</span><span class='va'>d_synth</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>value</span><span class='op'>)</span> <span class='fl'>3</span><span class='op'>)</span><span class='op'>)</span> - <span class='fu'><a href='https://rdrr.io/r/base/assign.html'>assign</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste0</a></span><span class='op'>(</span><span class='va'>d_synth_name</span>, <span class='st'>"_b"</span><span class='op'>)</span>, <span class='fu'><a href='add_err.html'>add_err</a></span><span class='op'>(</span><span class='va'>d_synth</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>value</span><span class='op'>)</span> <span class='fl'>7</span><span class='op'>)</span><span class='op'>)</span> - <span class='fu'><a href='https://rdrr.io/r/base/assign.html'>assign</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste0</a></span><span class='op'>(</span><span class='va'>d_synth_name</span>, <span class='st'>"_c"</span><span class='op'>)</span>, <span class='fu'><a href='add_err.html'>add_err</a></span><span class='op'>(</span><span class='va'>d_synth</span>, - <span class='kw'>function</span><span class='op'>(</span><span class='va'>value</span><span class='op'>)</span> <span class='fu'>sdfunc_twocomp</span><span class='op'>(</span><span class='va'>value</span>, <span class='fl'>0.5</span>, <span class='fl'>0.07</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span> - -<span class='op'>}</span> - -<span class='va'>d_synth_err_names</span> <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span> - <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/rep.html'>rep</a></span><span class='op'>(</span><span class='va'>d_synth_names</span>, each <span class='op'>=</span> <span class='fl'>3</span><span class='op'>)</span>, <span class='va'>letters</span><span class='op'>[</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>3</span><span class='op'>]</span>, sep <span class='op'>=</span> <span class='st'>"_"</span><span class='op'>)</span> -<span class='op'>)</span> - -<span class='co'># This is just one example of an evaluation using the kinetic model used for</span> -<span class='co'># the generation of the data</span> - <span class='va'>fit</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>m_synth_SFO_lin</span>, <span class='va'>synthetic_data_for_UBA_2014</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span><span class='op'>$</span><span class='va'>data</span>, - quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> - <span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span><span class='op'>(</span><span class='va'>fit</span><span class='op'>)</span> -</div><div class='img'><img src='synthetic_data_for_UBA_2014-1.png' alt='' width='700' height='433' /></div><div class='input'> <span class='fu'><a href='https://rdrr.io/pkg/saemix/man/summary-methods.html'>summary</a></span><span class='op'>(</span><span class='va'>fit</span><span class='op'>)</span> -</div><div class='output co'>#> mkin version used for fitting: 1.0.3.9000 -#> R version used for fitting: 4.0.3 -#> Date of fit: Mon Feb 15 17:13:29 2021 -#> Date of summary: Mon Feb 15 17:13:29 2021 -#> -#> Equations: -#> d_parent/dt = - k_parent * parent -#> d_M1/dt = + f_parent_to_M1 * k_parent * parent - k_M1 * M1 -#> d_M2/dt = + f_M1_to_M2 * k_M1 * M1 - k_M2 * M2 -#> -#> Model predictions using solution type deSolve -#> -#> Fitted using 833 model solutions performed in 0.649 s -#> -#> Error model: Constant variance -#> -#> Error model algorithm: OLS -#> -#> Starting values for parameters to be optimised: -#> value type -#> parent_0 101.3500 state -#> k_parent 0.1000 deparm -#> k_M1 0.1001 deparm -#> k_M2 0.1002 deparm -#> f_parent_to_M1 0.5000 deparm -#> f_M1_to_M2 0.5000 deparm -#> -#> Starting values for the transformed parameters actually optimised: -#> value lower upper -#> parent_0 101.350000 -Inf Inf -#> log_k_parent -2.302585 -Inf Inf -#> log_k_M1 -2.301586 -Inf Inf -#> log_k_M2 -2.300587 -Inf Inf -#> f_parent_qlogis 0.000000 -Inf Inf -#> f_M1_qlogis 0.000000 -Inf Inf -#> -#> Fixed parameter values: -#> value type -#> M1_0 0 state -#> M2_0 0 state -#> -#> Results: -#> -#> AIC BIC logLik -#> 188.7274 200.3723 -87.36368 -#> -#> Optimised, transformed parameters with symmetric confidence intervals: -#> Estimate Std. Error Lower Upper -#> parent_0 102.1000 1.57000 98.8600 105.3000 -#> log_k_parent -0.3020 0.03885 -0.3812 -0.2229 -#> log_k_M1 -1.2070 0.07123 -1.3520 -1.0620 -#> log_k_M2 -3.9010 0.06571 -4.0350 -3.7670 -#> f_parent_qlogis 1.2010 0.23530 0.7216 1.6800 -#> f_M1_qlogis 0.9589 0.24890 0.4520 1.4660 -#> sigma 2.2730 0.25740 1.7490 2.7970 -#> -#> Parameter correlation: -#> parent_0 log_k_parent log_k_M1 log_k_M2 f_parent_qlogis -#> parent_0 1.000e+00 3.933e-01 -1.605e-01 2.819e-02 -4.624e-01 -#> log_k_parent 3.933e-01 1.000e+00 -4.082e-01 7.166e-02 -5.682e-01 -#> log_k_M1 -1.605e-01 -4.082e-01 1.000e+00 -3.929e-01 7.478e-01 -#> log_k_M2 2.819e-02 7.166e-02 -3.929e-01 1.000e+00 -2.658e-01 -#> f_parent_qlogis -4.624e-01 -5.682e-01 7.478e-01 -2.658e-01 1.000e+00 -#> f_M1_qlogis 1.614e-01 4.102e-01 -8.109e-01 5.419e-01 -8.605e-01 -#> sigma -2.900e-08 -8.030e-09 -2.741e-08 3.938e-08 -2.681e-08 -#> f_M1_qlogis sigma -#> parent_0 1.614e-01 -2.900e-08 -#> log_k_parent 4.102e-01 -8.030e-09 -#> log_k_M1 -8.109e-01 -2.741e-08 -#> log_k_M2 5.419e-01 3.938e-08 -#> f_parent_qlogis -8.605e-01 -2.681e-08 -#> f_M1_qlogis 1.000e+00 4.971e-08 -#> sigma 4.971e-08 1.000e+00 -#> -#> Backtransformed parameters: -#> Confidence intervals for internally transformed parameters are asymmetric. -#> t-test (unrealistically) based on the assumption of normal distribution -#> for estimators of untransformed parameters. -#> Estimate t value Pr(>t) Lower Upper -#> parent_0 102.10000 65.000 7.281e-36 98.86000 105.30000 -#> k_parent 0.73930 25.740 2.948e-23 0.68310 0.80020 -#> k_M1 0.29920 14.040 1.577e-15 0.25880 0.34590 -#> k_M2 0.02023 15.220 1.653e-16 0.01769 0.02312 -#> f_parent_to_M1 0.76870 18.370 7.295e-19 0.67300 0.84290 -#> f_M1_to_M2 0.72290 14.500 6.418e-16 0.61110 0.81240 -#> sigma 2.27300 8.832 2.161e-10 1.74900 2.79700 -#> -#> FOCUS Chi2 error levels in percent: -#> err.min n.optim df -#> All data 8.454 6 17 -#> parent 8.660 2 6 -#> M1 10.583 2 5 -#> M2 3.586 2 6 -#> -#> Resulting formation fractions: -#> ff -#> parent_M1 0.7687 -#> parent_sink 0.2313 -#> M1_M2 0.7229 -#> M1_sink 0.2771 -#> -#> Estimated disappearance times: -#> DT50 DT90 -#> parent 0.9376 3.114 -#> M1 2.3170 7.697 -#> M2 34.2689 113.839 -#> -#> Data: -#> time variable observed predicted residual -#> 0 parent 101.5 1.021e+02 -0.56248 -#> 0 parent 101.2 1.021e+02 -0.86248 -#> 1 parent 53.9 4.873e+01 5.17118 -#> 1 parent 47.5 4.873e+01 -1.22882 -#> 3 parent 10.4 1.111e+01 -0.70773 -#> 3 parent 7.6 1.111e+01 -3.50773 -#> 7 parent 1.1 5.772e-01 0.52283 -#> 7 parent 0.3 5.772e-01 -0.27717 -#> 14 parent 3.5 3.264e-03 3.49674 -#> 28 parent 3.2 1.045e-07 3.20000 -#> 90 parent 0.6 9.530e-10 0.60000 -#> 120 parent 3.5 -5.940e-10 3.50000 -#> 1 M1 36.4 3.479e+01 1.61088 -#> 1 M1 37.4 3.479e+01 2.61088 -#> 3 M1 34.3 3.937e+01 -5.07027 -#> 3 M1 39.8 3.937e+01 0.42973 -#> 7 M1 15.1 1.549e+01 -0.38715 -#> 7 M1 17.8 1.549e+01 2.31285 -#> 14 M1 5.8 1.995e+00 3.80469 -#> 14 M1 1.2 1.995e+00 -0.79531 -#> 60 M1 0.5 2.111e-06 0.50000 -#> 90 M1 3.2 -9.670e-10 3.20000 -#> 120 M1 1.5 7.670e-10 1.50000 -#> 120 M1 0.6 7.670e-10 0.60000 -#> 1 M2 4.8 4.455e+00 0.34517 -#> 3 M2 20.9 2.153e+01 -0.62527 -#> 3 M2 19.3 2.153e+01 -2.22527 -#> 7 M2 42.0 4.192e+01 0.07941 -#> 7 M2 43.1 4.192e+01 1.17941 -#> 14 M2 49.4 4.557e+01 3.83353 -#> 14 M2 44.3 4.557e+01 -1.26647 -#> 28 M2 34.6 3.547e+01 -0.87275 -#> 28 M2 33.0 3.547e+01 -2.47275 -#> 60 M2 18.8 1.858e+01 0.21837 -#> 60 M2 17.6 1.858e+01 -0.98163 -#> 90 M2 10.6 1.013e+01 0.47130 -#> 90 M2 10.8 1.013e+01 0.67130 -#> 120 M2 9.8 5.521e+00 4.27893 -#> 120 M2 3.3 5.521e+00 -2.22107</div><div class='input'><span class='co'># }</span> -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="co"># The data have been generated using the following kinetic models</span></span></span> +<span class="r-in"><span><span class="va">m_synth_SFO_lin</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M2"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">m_synth_SFO_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">m_synth_DFOP_lin</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="st">"M1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M2"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">m_synth_DFOP_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># The model predictions without intentional error were generated as follows</span></span></span> +<span class="r-in"><span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">d_synth_SFO_lin</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_synth_SFO_lin</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.7</span>, f_parent_to_M1 <span class="op">=</span> <span class="fl">0.8</span>,</span></span> +<span class="r-in"><span> k_M1 <span class="op">=</span> <span class="fl">0.3</span>, f_M1_to_M2 <span class="op">=</span> <span class="fl">0.7</span>,</span></span> +<span class="r-in"><span> k_M2 <span class="op">=</span> <span class="fl">0.02</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span>, M2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="va">sampling_times</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">d_synth_DFOP_lin</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_synth_DFOP_lin</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k1 <span class="op">=</span> <span class="fl">0.2</span>, k2 <span class="op">=</span> <span class="fl">0.02</span>, g <span class="op">=</span> <span class="fl">0.5</span>,</span></span> +<span class="r-in"><span> f_parent_to_M1 <span class="op">=</span> <span class="fl">0.5</span>, k_M1 <span class="op">=</span> <span class="fl">0.3</span>,</span></span> +<span class="r-in"><span> f_M1_to_M2 <span class="op">=</span> <span class="fl">0.7</span>, k_M2 <span class="op">=</span> <span class="fl">0.02</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span>, M2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="va">sampling_times</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">d_synth_SFO_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_synth_SFO_par</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.2</span>,</span></span> +<span class="r-in"><span> f_parent_to_M1 <span class="op">=</span> <span class="fl">0.8</span>, k_M1 <span class="op">=</span> <span class="fl">0.01</span>,</span></span> +<span class="r-in"><span> f_parent_to_M2 <span class="op">=</span> <span class="fl">0.2</span>, k_M2 <span class="op">=</span> <span class="fl">0.02</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span>, M2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="va">sampling_times</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">d_synth_DFOP_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_synth_DFOP_par</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k1 <span class="op">=</span> <span class="fl">0.3</span>, k2 <span class="op">=</span> <span class="fl">0.02</span>, g <span class="op">=</span> <span class="fl">0.7</span>,</span></span> +<span class="r-in"><span> f_parent_to_M1 <span class="op">=</span> <span class="fl">0.6</span>, k_M1 <span class="op">=</span> <span class="fl">0.04</span>,</span></span> +<span class="r-in"><span> f_parent_to_M2 <span class="op">=</span> <span class="fl">0.4</span>, k_M2 <span class="op">=</span> <span class="fl">0.01</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span>, M2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="va">sampling_times</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Construct names for datasets with errors</span></span></span> +<span class="r-in"><span><span class="va">d_synth_names</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="st">"d_synth_"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO_lin"</span>, <span class="st">"SFO_par"</span>,</span></span> +<span class="r-in"><span> <span class="st">"DFOP_lin"</span>, <span class="st">"DFOP_par"</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Original function used or adding errors. The add_err function now published</span></span></span> +<span class="r-in"><span><span class="co"># with this package is a slightly generalised version where the names of</span></span></span> +<span class="r-in"><span><span class="co"># secondary compartments that should have an initial value of zero (M1 and M2</span></span></span> +<span class="r-in"><span><span class="co"># in this case) are not hardcoded any more.</span></span></span> +<span class="r-in"><span><span class="co"># add_err = function(d, sdfunc, LOD = 0.1, reps = 2, seed = 123456789)</span></span></span> +<span class="r-in"><span><span class="co"># {</span></span></span> +<span class="r-in"><span><span class="co"># set.seed(seed)</span></span></span> +<span class="r-in"><span><span class="co"># d_long = mkin_wide_to_long(d, time = "time")</span></span></span> +<span class="r-in"><span><span class="co"># d_rep = data.frame(lapply(d_long, rep, each = 2))</span></span></span> +<span class="r-in"><span><span class="co"># d_rep$value = rnorm(length(d_rep$value), d_rep$value, sdfunc(d_rep$value))</span></span></span> +<span class="r-in"><span><span class="co">#</span></span></span> +<span class="r-in"><span><span class="co"># d_rep[d_rep$time == 0 & d_rep$name %in% c("M1", "M2"), "value"] <- 0</span></span></span> +<span class="r-in"><span><span class="co"># d_NA <- transform(d_rep, value = ifelse(value < LOD, NA, value))</span></span></span> +<span class="r-in"><span><span class="co"># d_NA$value <- round(d_NA$value, 1)</span></span></span> +<span class="r-in"><span><span class="co"># return(d_NA)</span></span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># The following is the simplified version of the two-component model of Rocke</span></span></span> +<span class="r-in"><span><span class="co"># and Lorenzato (1995)</span></span></span> +<span class="r-in"><span><span class="va">sdfunc_twocomp</span> <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">value</span>, <span class="va">sd_low</span>, <span class="va">rsd_high</span><span class="op">)</span> <span class="op">{</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">sd_low</span><span class="op">^</span><span class="fl">2</span> <span class="op">+</span> <span class="va">value</span><span class="op">^</span><span class="fl">2</span> <span class="op">*</span> <span class="va">rsd_high</span><span class="op">^</span><span class="fl">2</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="op">}</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Add the errors.</span></span></span> +<span class="r-in"><span><span class="kw">for</span> <span class="op">(</span><span class="va">d_synth_name</span> <span class="kw">in</span> <span class="va">d_synth_names</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="op">{</span></span></span> +<span class="r-in"><span> <span class="va">d_synth</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/get.html" class="external-link">get</a></span><span class="op">(</span><span class="va">d_synth_name</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">d_synth_name</span>, <span class="st">"_a"</span><span class="op">)</span>, <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_synth</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">d_synth_name</span>, <span class="st">"_b"</span><span class="op">)</span>, <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_synth</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">7</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">d_synth_name</span>, <span class="st">"_c"</span><span class="op">)</span>, <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_synth</span>,</span></span> +<span class="r-in"><span> <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fu">sdfunc_twocomp</span><span class="op">(</span><span class="va">value</span>, <span class="fl">0.5</span>, <span class="fl">0.07</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="op">}</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">d_synth_err_names</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="va">d_synth_names</span>, each <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">letters</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">]</span>, sep <span class="op">=</span> <span class="st">"_"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># This is just one example of an evaluation using the kinetic model used for</span></span></span> +<span class="r-in"><span><span class="co"># the generation of the data</span></span></span> +<span class="r-in"><span> <span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_SFO_lin</span>, <span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>,</span></span> +<span class="r-in"><span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="synthetic_data_for_UBA_2014-1.png" alt="" width="700" height="433"></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.2.2 </span> +<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Nov 24 08:11:54 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Nov 24 08:11:54 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Equations:</span> +<span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - k_parent * parent</span> +<span class="r-out co"><span class="r-pr">#></span> d_M1/dt = + f_parent_to_M1 * k_parent * parent - k_M1 * M1</span> +<span class="r-out co"><span class="r-pr">#></span> d_M2/dt = + f_M1_to_M2 * k_M1 * M1 - k_M2 * M2</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type deSolve </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Fitted using 833 model solutions performed in 0.574 s</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Error model: Constant variance </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Error model algorithm: OLS </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Starting values for parameters to be optimised:</span> +<span class="r-out co"><span class="r-pr">#></span> value type</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 101.3500 state</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent 0.1000 deparm</span> +<span class="r-out co"><span class="r-pr">#></span> k_M1 0.1001 deparm</span> +<span class="r-out co"><span class="r-pr">#></span> k_M2 0.1002 deparm</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_M1 0.5000 deparm</span> +<span class="r-out co"><span class="r-pr">#></span> f_M1_to_M2 0.5000 deparm</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Starting values for the transformed parameters actually optimised:</span> +<span class="r-out co"><span class="r-pr">#></span> value lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 101.350000 -Inf Inf</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_parent -2.302585 -Inf Inf</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M1 -2.301586 -Inf Inf</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M2 -2.300587 -Inf Inf</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis 0.000000 -Inf Inf</span> +<span class="r-out co"><span class="r-pr">#></span> f_M1_qlogis 0.000000 -Inf Inf</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Fixed parameter values:</span> +<span class="r-out co"><span class="r-pr">#></span> value type</span> +<span class="r-out co"><span class="r-pr">#></span> M1_0 0 state</span> +<span class="r-out co"><span class="r-pr">#></span> M2_0 0 state</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Results:</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> +<span class="r-out co"><span class="r-pr">#></span> 188.7274 200.3723 -87.36368</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Optimised, transformed parameters with symmetric confidence intervals:</span> +<span class="r-out co"><span class="r-pr">#></span> Estimate Std. Error Lower Upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 102.1000 1.57000 98.8600 105.3000</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_parent -0.3020 0.03885 -0.3812 -0.2229</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M1 -1.2070 0.07123 -1.3520 -1.0620</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M2 -3.9010 0.06571 -4.0350 -3.7670</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis 1.2010 0.23530 0.7216 1.6800</span> +<span class="r-out co"><span class="r-pr">#></span> f_M1_qlogis 0.9589 0.24890 0.4520 1.4660</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 2.2730 0.25740 1.7490 2.7970</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Parameter correlation:</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_parent log_k_M1 log_k_M2 f_parent_qlogis</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 1.000e+00 3.933e-01 -1.605e-01 2.819e-02 -4.624e-01</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_parent 3.933e-01 1.000e+00 -4.082e-01 7.166e-02 -5.682e-01</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M1 -1.605e-01 -4.082e-01 1.000e+00 -3.929e-01 7.478e-01</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M2 2.819e-02 7.166e-02 -3.929e-01 1.000e+00 -2.658e-01</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -4.624e-01 -5.682e-01 7.478e-01 -2.658e-01 1.000e+00</span> +<span class="r-out co"><span class="r-pr">#></span> f_M1_qlogis 1.614e-01 4.102e-01 -8.109e-01 5.419e-01 -8.605e-01</span> +<span class="r-out co"><span class="r-pr">#></span> sigma -2.900e-08 -8.030e-09 -2.741e-08 3.938e-08 -2.681e-08</span> +<span class="r-out co"><span class="r-pr">#></span> f_M1_qlogis sigma</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 1.614e-01 -2.900e-08</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_parent 4.102e-01 -8.030e-09</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M1 -8.109e-01 -2.741e-08</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M2 5.419e-01 3.938e-08</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -8.605e-01 -2.681e-08</span> +<span class="r-out co"><span class="r-pr">#></span> f_M1_qlogis 1.000e+00 4.971e-08</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 4.971e-08 1.000e+00</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Backtransformed parameters:</span> +<span class="r-out co"><span class="r-pr">#></span> Confidence intervals for internally transformed parameters are asymmetric.</span> +<span class="r-out co"><span class="r-pr">#></span> t-test (unrealistically) based on the assumption of normal distribution</span> +<span class="r-out co"><span class="r-pr">#></span> for estimators of untransformed parameters.</span> +<span class="r-out co"><span class="r-pr">#></span> Estimate t value Pr(>t) Lower Upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 102.10000 65.000 7.281e-36 98.86000 105.30000</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent 0.73930 25.740 2.948e-23 0.68310 0.80020</span> +<span class="r-out co"><span class="r-pr">#></span> k_M1 0.29920 14.040 1.577e-15 0.25880 0.34590</span> +<span class="r-out co"><span class="r-pr">#></span> k_M2 0.02023 15.220 1.653e-16 0.01769 0.02312</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_M1 0.76870 18.370 7.295e-19 0.67300 0.84290</span> +<span class="r-out co"><span class="r-pr">#></span> f_M1_to_M2 0.72290 14.500 6.418e-16 0.61110 0.81240</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 2.27300 8.832 2.161e-10 1.74900 2.79700</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> FOCUS Chi2 error levels in percent:</span> +<span class="r-out co"><span class="r-pr">#></span> err.min n.optim df</span> +<span class="r-out co"><span class="r-pr">#></span> All data 8.454 6 17</span> +<span class="r-out co"><span class="r-pr">#></span> parent 8.660 2 6</span> +<span class="r-out co"><span class="r-pr">#></span> M1 10.583 2 5</span> +<span class="r-out co"><span class="r-pr">#></span> M2 3.586 2 6</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Resulting formation fractions:</span> +<span class="r-out co"><span class="r-pr">#></span> ff</span> +<span class="r-out co"><span class="r-pr">#></span> parent_M1 0.7687</span> +<span class="r-out co"><span class="r-pr">#></span> parent_sink 0.2313</span> +<span class="r-out co"><span class="r-pr">#></span> M1_M2 0.7229</span> +<span class="r-out co"><span class="r-pr">#></span> M1_sink 0.2771</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Estimated disappearance times:</span> +<span class="r-out co"><span class="r-pr">#></span> DT50 DT90</span> +<span class="r-out co"><span class="r-pr">#></span> parent 0.9376 3.114</span> +<span class="r-out co"><span class="r-pr">#></span> M1 2.3170 7.697</span> +<span class="r-out co"><span class="r-pr">#></span> M2 34.2689 113.839</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Data:</span> +<span class="r-out co"><span class="r-pr">#></span> time variable observed predicted residual</span> +<span class="r-out co"><span class="r-pr">#></span> 0 parent 101.5 1.021e+02 -0.56248</span> +<span class="r-out co"><span class="r-pr">#></span> 0 parent 101.2 1.021e+02 -0.86248</span> +<span class="r-out co"><span class="r-pr">#></span> 1 parent 53.9 4.873e+01 5.17118</span> +<span class="r-out co"><span class="r-pr">#></span> 1 parent 47.5 4.873e+01 -1.22882</span> +<span class="r-out co"><span class="r-pr">#></span> 3 parent 10.4 1.111e+01 -0.70773</span> +<span class="r-out co"><span class="r-pr">#></span> 3 parent 7.6 1.111e+01 -3.50773</span> +<span class="r-out co"><span class="r-pr">#></span> 7 parent 1.1 5.772e-01 0.52283</span> +<span class="r-out co"><span class="r-pr">#></span> 7 parent 0.3 5.772e-01 -0.27717</span> +<span class="r-out co"><span class="r-pr">#></span> 14 parent 3.5 3.264e-03 3.49674</span> +<span class="r-out co"><span class="r-pr">#></span> 28 parent 3.2 1.045e-07 3.20000</span> +<span class="r-out co"><span class="r-pr">#></span> 90 parent 0.6 9.530e-10 0.60000</span> +<span class="r-out co"><span class="r-pr">#></span> 120 parent 3.5 -5.940e-10 3.50000</span> +<span class="r-out co"><span class="r-pr">#></span> 1 M1 36.4 3.479e+01 1.61088</span> +<span class="r-out co"><span class="r-pr">#></span> 1 M1 37.4 3.479e+01 2.61088</span> +<span class="r-out co"><span class="r-pr">#></span> 3 M1 34.3 3.937e+01 -5.07027</span> +<span class="r-out co"><span class="r-pr">#></span> 3 M1 39.8 3.937e+01 0.42973</span> +<span class="r-out co"><span class="r-pr">#></span> 7 M1 15.1 1.549e+01 -0.38715</span> +<span class="r-out co"><span class="r-pr">#></span> 7 M1 17.8 1.549e+01 2.31285</span> +<span class="r-out co"><span class="r-pr">#></span> 14 M1 5.8 1.995e+00 3.80469</span> +<span class="r-out co"><span class="r-pr">#></span> 14 M1 1.2 1.995e+00 -0.79531</span> +<span class="r-out co"><span class="r-pr">#></span> 60 M1 0.5 2.111e-06 0.50000</span> +<span class="r-out co"><span class="r-pr">#></span> 90 M1 3.2 -9.670e-10 3.20000</span> +<span class="r-out co"><span class="r-pr">#></span> 120 M1 1.5 7.670e-10 1.50000</span> +<span class="r-out co"><span class="r-pr">#></span> 120 M1 0.6 7.670e-10 0.60000</span> +<span class="r-out co"><span class="r-pr">#></span> 1 M2 4.8 4.455e+00 0.34517</span> +<span class="r-out co"><span class="r-pr">#></span> 3 M2 20.9 2.153e+01 -0.62527</span> +<span class="r-out co"><span class="r-pr">#></span> 3 M2 19.3 2.153e+01 -2.22527</span> +<span class="r-out co"><span class="r-pr">#></span> 7 M2 42.0 4.192e+01 0.07941</span> +<span class="r-out co"><span class="r-pr">#></span> 7 M2 43.1 4.192e+01 1.17941</span> +<span class="r-out co"><span class="r-pr">#></span> 14 M2 49.4 4.557e+01 3.83353</span> +<span class="r-out co"><span class="r-pr">#></span> 14 M2 44.3 4.557e+01 -1.26647</span> +<span class="r-out co"><span class="r-pr">#></span> 28 M2 34.6 3.547e+01 -0.87275</span> +<span class="r-out co"><span class="r-pr">#></span> 28 M2 33.0 3.547e+01 -2.47275</span> +<span class="r-out co"><span class="r-pr">#></span> 60 M2 18.8 1.858e+01 0.21837</span> +<span class="r-out co"><span class="r-pr">#></span> 60 M2 17.6 1.858e+01 -0.98163</span> +<span class="r-out co"><span class="r-pr">#></span> 90 M2 10.6 1.013e+01 0.47130</span> +<span class="r-out co"><span class="r-pr">#></span> 90 M2 10.8 1.013e+01 0.67130</span> +<span class="r-out co"><span class="r-pr">#></span> 120 M2 9.8 5.521e+00 4.27893</span> +<span class="r-out co"><span class="r-pr">#></span> 120 M2 3.3 5.521e+00 -2.22107</span> +<span class="r-in"><span><span class="co"># }</span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" 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href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -99,44 +42,46 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Three experimental datasets from two water sediment systems and one soil</h1> @@ -149,113 +94,126 @@ software packages (Ranke, 2014).</p> </div> - <pre class="usage"><span class='va'>test_data_from_UBA_2014</span></pre> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">test_data_from_UBA_2014</span></span></code></pre></div> + </div> + <div id="format"> + <h2>Format</h2> + <p>A list containing three datasets as an R6 class defined by <code><a href="mkinds.html">mkinds</a></code>. + Each dataset has, among others, the following components</p><dl><dt><code>title</code></dt> +<dd><p>The name of the dataset, e.g. <code>UBA_2014_WS_river</code></p></dd> - <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2> + <dt><code>data</code></dt> +<dd><p>A data frame with the data in the form expected by <code><a href="mkinfit.html">mkinfit</a></code></p></dd> - <p>A list containing three datasets as an R6 class defined by <code><a href='mkinds.html'>mkinds</a></code>. - Each dataset has, among others, the following components</p><dl> - <dt><code>title</code></dt><dd><p>The name of the dataset, e.g. <code>UBA_2014_WS_river</code></p></dd> - <dt><code>data</code></dt><dd><p>A data frame with the data in the form expected by <code><a href='mkinfit.html'>mkinfit</a></code></p></dd> -</dl> - - <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2> - +</dl></div> + <div id="source"> + <h2>Source</h2> <p>Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452</p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> <span class='co'># \dontrun{</span> - <span class='co'># This is a level P-II evaluation of the dataset according to the FOCUS kinetics</span> - <span class='co'># guidance. Due to the strong correlation of the parameter estimates, the</span> - <span class='co'># covariance matrix is not returned. Note that level P-II evaluations are</span> - <span class='co'># generally considered deprecated due to the frequent occurrence of such</span> - <span class='co'># large parameter correlations, among other reasons (e.g. the adequacy of the</span> - <span class='co'># model).</span> - <span class='va'>m_ws</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent_w <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"parent_s"</span><span class='op'>)</span>, - parent_s <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"parent_w"</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'> <span class='va'>f_river</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>m_ws</span>, <span class='va'>test_data_from_UBA_2014</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span><span class='op'>$</span><span class='va'>data</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'> <span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span><span class='op'>(</span><span class='va'>f_river</span><span class='op'>)</span> -</div><div class='img'><img src='test_data_from_UBA_2014-1.png' alt='' width='700' height='433' /></div><div class='input'> - <span class='fu'><a href='https://rdrr.io/pkg/saemix/man/summary-methods.html'>summary</a></span><span class='op'>(</span><span class='va'>f_river</span><span class='op'>)</span><span class='op'>$</span><span class='va'>bpar</span> -</div><div class='output co'>#> <span class='warning'>Warning: Could not calculate correlation; no covariance matrix</span></div><div class='output co'>#> Estimate se_notrans t value Pr(>t) Lower Upper -#> parent_w_0 95.91998118 NA NA NA NA NA -#> k_parent_w 0.41145375 NA NA NA NA NA -#> k_parent_s 0.04663944 NA NA NA NA NA -#> f_parent_w_to_parent_s 0.12467894 NA NA NA NA NA -#> f_parent_s_to_parent_w 0.50000000 NA NA NA NA NA -#> sigma 3.13612618 NA NA NA NA NA</div><div class='input'> <span class='fu'><a href='mkinerrmin.html'>mkinerrmin</a></span><span class='op'>(</span><span class='va'>f_river</span><span class='op'>)</span> -</div><div class='output co'>#> err.min n.optim df -#> All data 0.1090929 5 6 -#> parent_w 0.0817436 3 3 -#> parent_s 0.1619965 2 3</div><div class='input'> - <span class='co'># This is the evaluation used for the validation of software packages</span> - <span class='co'># in the expertise from 2014</span> - <span class='va'>m_soil</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"M1"</span>, <span class='st'>"M2"</span><span class='op'>)</span><span class='op'>)</span>, - M1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"M3"</span><span class='op'>)</span>, - M2 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"M3"</span><span class='op'>)</span>, - M3 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span>, - use_of_ff <span class='op'>=</span> <span class='st'>"max"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'> - <span class='va'>f_soil</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>m_soil</span>, <span class='va'>test_data_from_UBA_2014</span><span class='op'>[[</span><span class='fl'>3</span><span class='op'>]</span><span class='op'>]</span><span class='op'>$</span><span class='va'>data</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'> <span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span><span class='op'>(</span><span class='va'>f_soil</span>, lpos <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"topright"</span>, <span class='st'>"topright"</span>, <span class='st'>"topright"</span>, <span class='st'>"bottomright"</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='img'><img src='test_data_from_UBA_2014-2.png' alt='' width='700' height='433' /></div><div class='input'> <span class='fu'><a href='https://rdrr.io/pkg/saemix/man/summary-methods.html'>summary</a></span><span class='op'>(</span><span class='va'>f_soil</span><span class='op'>)</span><span class='op'>$</span><span class='va'>bpar</span> -</div><div class='output co'>#> Estimate se_notrans t value Pr(>t) Lower -#> parent_0 76.55425650 0.859186399 89.1008710 1.113861e-26 74.755959418 -#> k_parent 0.12081956 0.004601918 26.2541722 1.077359e-16 0.111561575 -#> k_M1 0.84258615 0.806160102 1.0451846 1.545268e-01 0.113779609 -#> k_M2 0.04210880 0.017083034 2.4649483 1.170188e-02 0.018013857 -#> k_M3 0.01122918 0.007245856 1.5497385 6.885052e-02 0.002909431 -#> f_parent_to_M1 0.32240200 0.240783943 1.3389680 9.819076e-02 NA -#> f_parent_to_M2 0.16099855 0.033691952 4.7785464 6.531136e-05 NA -#> f_M1_to_M3 0.27921507 0.269423780 1.0363416 1.565267e-01 0.022978205 -#> f_M2_to_M3 0.55641252 0.595119966 0.9349586 1.807707e-01 0.008002509 -#> sigma 1.14005399 0.149696423 7.6157731 1.727024e-07 0.826735778 -#> Upper -#> parent_0 78.35255358 -#> k_parent 0.13084582 -#> k_M1 6.23970702 -#> k_M2 0.09843260 -#> k_M3 0.04333992 -#> f_parent_to_M1 NA -#> f_parent_to_M2 NA -#> f_M1_to_M3 0.86450775 -#> f_M2_to_M3 0.99489895 -#> sigma 1.45337221</div><div class='input'> <span class='fu'><a href='mkinerrmin.html'>mkinerrmin</a></span><span class='op'>(</span><span class='va'>f_soil</span><span class='op'>)</span> -</div><div class='output co'>#> err.min n.optim df -#> All data 0.09649963 9 20 -#> parent 0.04721283 2 6 -#> M1 0.26551208 2 5 -#> M2 0.20327575 2 5 -#> M3 0.05196550 3 4</div><div class='input'> <span class='co'># }</span> -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span> <span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span> <span class="co"># This is a level P-II evaluation of the dataset according to the FOCUS kinetics</span></span></span> +<span class="r-in"><span> <span class="co"># guidance. Due to the strong correlation of the parameter estimates, the</span></span></span> +<span class="r-in"><span> <span class="co"># covariance matrix is not returned. Note that level P-II evaluations are</span></span></span> +<span class="r-in"><span> <span class="co"># generally considered deprecated due to the frequent occurrence of such</span></span></span> +<span class="r-in"><span> <span class="co"># large parameter correlations, among other reasons (e.g. the adequacy of the</span></span></span> +<span class="r-in"><span> <span class="co"># model).</span></span></span> +<span class="r-in"><span> <span class="va">m_ws</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent_w <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"parent_s"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> parent_s <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"parent_w"</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span> <span class="va">f_river</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_ws</span>, <span class="va">test_data_from_UBA_2014</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span> +<span class="r-in"><span> <span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">f_river</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="test_data_from_UBA_2014-1.png" alt="" width="700" height="433"></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_river</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></span> +<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Could not calculate correlation; no covariance matrix</span> +<span class="r-out co"><span class="r-pr">#></span> Estimate se_notrans t value Pr(>t) Lower Upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_w_0 95.91998118 NA NA NA NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent_w 0.41145375 NA NA NA NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent_s 0.04663944 NA NA NA NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_w_to_parent_s 0.12467894 NA NA NA NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_s_to_parent_w 0.50000000 NA NA NA NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 3.13612618 NA NA NA NA NA</span> +<span class="r-in"><span> <span class="fu"><a href="mkinerrmin.html">mkinerrmin</a></span><span class="op">(</span><span class="va">f_river</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> err.min n.optim df</span> +<span class="r-out co"><span class="r-pr">#></span> All data 0.1090929 5 6</span> +<span class="r-out co"><span class="r-pr">#></span> parent_w 0.0817436 3 3</span> +<span class="r-out co"><span class="r-pr">#></span> parent_s 0.1619965 2 3</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># This is the evaluation used for the validation of software packages</span></span></span> +<span class="r-in"><span> <span class="co"># in the expertise from 2014</span></span></span> +<span class="r-in"><span> <span class="va">m_soil</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M3"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M3"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M3 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="va">f_soil</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_soil</span>, <span class="va">test_data_from_UBA_2014</span><span class="op">[[</span><span class="fl">3</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span> +<span class="r-in"><span> <span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">f_soil</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"topright"</span>, <span class="st">"topright"</span>, <span class="st">"bottomright"</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="test_data_from_UBA_2014-2.png" alt="" width="700" height="433"></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_soil</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Estimate se_notrans t value Pr(>t) Lower</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 76.55425650 0.859186399 89.1008710 1.113861e-26 74.755959418</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent 0.12081956 0.004601918 26.2541722 1.077359e-16 0.111561575</span> +<span class="r-out co"><span class="r-pr">#></span> k_M1 0.84258615 0.806160102 1.0451846 1.545268e-01 0.113779609</span> +<span class="r-out co"><span class="r-pr">#></span> k_M2 0.04210880 0.017083034 2.4649483 1.170188e-02 0.018013857</span> +<span class="r-out co"><span class="r-pr">#></span> k_M3 0.01122918 0.007245856 1.5497385 6.885052e-02 0.002909431</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_M1 0.32240200 0.240783943 1.3389680 9.819076e-02 NA</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_M2 0.16099855 0.033691952 4.7785464 6.531136e-05 NA</span> +<span class="r-out co"><span class="r-pr">#></span> f_M1_to_M3 0.27921507 0.269423780 1.0363416 1.565267e-01 0.022978205</span> +<span class="r-out co"><span class="r-pr">#></span> f_M2_to_M3 0.55641252 0.595119966 0.9349586 1.807707e-01 0.008002509</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 1.14005399 0.149696423 7.6157731 1.727024e-07 0.826735778</span> +<span class="r-out co"><span class="r-pr">#></span> Upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 78.35255358</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent 0.13084582</span> +<span class="r-out co"><span class="r-pr">#></span> k_M1 6.23970702</span> +<span class="r-out co"><span class="r-pr">#></span> k_M2 0.09843260</span> +<span class="r-out co"><span class="r-pr">#></span> k_M3 0.04333992</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_M1 NA</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_M2 NA</span> +<span class="r-out co"><span class="r-pr">#></span> f_M1_to_M3 0.86450775</span> +<span class="r-out co"><span class="r-pr">#></span> f_M2_to_M3 0.99489895</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 1.45337221</span> +<span class="r-in"><span> <span class="fu"><a href="mkinerrmin.html">mkinerrmin</a></span><span class="op">(</span><span class="va">f_soil</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> err.min n.optim df</span> +<span class="r-out co"><span class="r-pr">#></span> All data 0.09649963 9 20</span> +<span class="r-out co"><span class="r-pr">#></span> parent 0.04721283 2 6</span> +<span class="r-out co"><span class="r-pr">#></span> M1 0.26551208 2 5</span> +<span class="r-out co"><span class="r-pr">#></span> M2 0.20327575 2 5</span> +<span class="r-out co"><span class="r-pr">#></span> M3 0.05196550 3 4</span> +<span class="r-in"><span> <span class="co"># }</span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/reference/tex_listing.html b/docs/dev/reference/tex_listing.html index c82138b7..03bd83f2 100644 --- a/docs/dev/reference/tex_listing.html +++ b/docs/dev/reference/tex_listing.html @@ -18,7 +18,7 @@ option results = "asis".'><meta name="robots" content="noindex"><!-- mathjax --> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.0</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> @@ -60,7 +60,10 @@ option results = "asis".'><meta name="robots" content="noindex"><!-- mathjax --> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> diff --git a/docs/dev/reference/transform_odeparms.html b/docs/dev/reference/transform_odeparms.html index 75d6a1f9..a7a01043 100644 --- a/docs/dev/reference/transform_odeparms.html +++ b/docs/dev/reference/transform_odeparms.html @@ -1,72 +1,17 @@ -<!-- Generated by pkgdown: do not edit by hand --> <!DOCTYPE html> -<html lang="en"> - <head> - <meta charset="utf-8"> -<meta http-equiv="X-UA-Compatible" content="IE=edge"> -<meta name="viewport" content="width=device-width, initial-scale=1.0"> - -<title>Functions to transform and backtransform kinetic parameters for fitting — transform_odeparms • mkin</title> - - -<!-- jquery --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" 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For kinetic rate constants and other parameters that can only take on positive values, a simple log transformation is used. For compositional parameters, such as the formations fractions that should always sum up to 1 and can not be negative, -the ilr transformation is used." /> - - -<meta name="robots" content="noindex"> - -<!-- mathjax --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script> - -<!--[if lt IE 9]> +the ilr transformation is used."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> - +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - - </head> - - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -77,23 +22,21 @@ the ilr transformation is used." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.2.2</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -103,48 +46,50 @@ the ilr transformation is used." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Functions to transform and backtransform kinetic parameters for fitting</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/transform_odeparms.R'><code>R/transform_odeparms.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/transform_odeparms.R" class="external-link"><code>R/transform_odeparms.R</code></a></small> <div class="hidden name"><code>transform_odeparms.Rd</code></div> </div> @@ -154,205 +99,221 @@ restricted values to the full scale of real numbers. For kinetic rate constants and other parameters that can only take on positive values, a simple log transformation is used. For compositional parameters, such as the formations fractions that should always sum up to 1 and can not be negative, -the <a href='ilr.html'>ilr</a> transformation is used.</p> +the <a href="ilr.html">ilr</a> transformation is used.</p> + </div> + + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">transform_odeparms</span><span class="op">(</span></span> +<span> <span class="va">parms</span>,</span> +<span> <span class="va">mkinmod</span>,</span> +<span> transform_rates <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> transform_fractions <span class="op">=</span> <span class="cn">TRUE</span></span> +<span><span class="op">)</span></span> +<span></span> +<span><span class="fu">backtransform_odeparms</span><span class="op">(</span></span> +<span> <span class="va">transparms</span>,</span> +<span> <span class="va">mkinmod</span>,</span> +<span> transform_rates <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> transform_fractions <span class="op">=</span> <span class="cn">TRUE</span></span> +<span><span class="op">)</span></span></code></pre></div> </div> - <pre class="usage"><span class='fu'>transform_odeparms</span><span class='op'>(</span> - <span class='va'>parms</span>, - <span class='va'>mkinmod</span>, - transform_rates <span class='op'>=</span> <span class='cn'>TRUE</span>, - transform_fractions <span class='op'>=</span> <span class='cn'>TRUE</span> -<span class='op'>)</span> + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>parms</dt> +<dd><p>Parameters of kinetic models as used in the differential +equations.</p></dd> -<span class='fu'>backtransform_odeparms</span><span class='op'>(</span> - <span class='va'>transparms</span>, - <span class='va'>mkinmod</span>, - transform_rates <span class='op'>=</span> <span class='cn'>TRUE</span>, - transform_fractions <span class='op'>=</span> <span class='cn'>TRUE</span> -<span class='op'>)</span></pre> - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>parms</th> - <td><p>Parameters of kinetic models as used in the differential -equations.</p></td> - </tr> - <tr> - <th>mkinmod</th> - <td><p>The kinetic model of class <a href='mkinmod.html'>mkinmod</a>, containing +<dt>mkinmod</dt> +<dd><p>The kinetic model of class <a href="mkinmod.html">mkinmod</a>, containing the names of the model variables that are needed for grouping the -formation fractions before <a href='ilr.html'>ilr</a> transformation, the parameter -names and the information if the pathway to sink is included in the model.</p></td> - </tr> - <tr> - <th>transform_rates</th> - <td><p>Boolean specifying if kinetic rate constants should +formation fractions before <a href="ilr.html">ilr</a> transformation, the parameter +names and the information if the pathway to sink is included in the model.</p></dd> + + +<dt>transform_rates</dt> +<dd><p>Boolean specifying if kinetic rate constants should be transformed in the model specification used in the fitting for better compliance with the assumption of normal distribution of the estimator. If TRUE, also alpha and beta parameters of the FOMC model are log-transformed, as well as k1 and k2 rate constants for the DFOP and HS -models and the break point tb of the HS model.</p></td> - </tr> - <tr> - <th>transform_fractions</th> - <td><p>Boolean specifying if formation fractions +models and the break point tb of the HS model.</p></dd> + + +<dt>transform_fractions</dt> +<dd><p>Boolean specifying if formation fractions constants should be transformed in the model specification used in the fitting for better compliance with the assumption of normal distribution of the estimator. The default (TRUE) is to do transformations. The g parameter of the DFOP model is also seen as a fraction. If a single fraction is transformed (g parameter of DFOP or only a single target variable e.g. a single metabolite plus a pathway to sink), a -logistic transformation is used <code><a href='https://rdrr.io/r/stats/Logistic.html'>stats::qlogis()</a></code>. In other cases, i.e. if +logistic transformation is used <code><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">stats::qlogis()</a></code>. In other cases, i.e. if two or more formation fractions need to be transformed whose sum cannot -exceed one, the <a href='ilr.html'>ilr</a> transformation is used.</p></td> - </tr> - <tr> - <th>transparms</th> - <td><p>Transformed parameters of kinetic models as used in the -fitting procedure.</p></td> - </tr> - </table> +exceed one, the <a href="ilr.html">ilr</a> transformation is used.</p></dd> + - <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> +<dt>transparms</dt> +<dd><p>Transformed parameters of kinetic models as used in the +fitting procedure.</p></dd> - <p>A vector of transformed or backtransformed parameters</p> - <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2> +</dl></div> + <div id="value"> + <h2>Value</h2> + +<p>A vector of transformed or backtransformed parameters</p> + </div> + <div id="details"> + <h2>Details</h2> <p>The transformation of sets of formation fractions is fragile, as it supposes the same ordering of the components in forward and backward transformation. -This is no problem for the internal use in <a href='mkinfit.html'>mkinfit</a>.</p> - <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2> - +This is no problem for the internal use in <a href="mkinfit.html">mkinfit</a>.</p> + </div> + <div id="author"> + <h2>Author</h2> <p>Johannes Ranke</p> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'> -<span class='va'>SFO_SFO</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span> - parent <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='st'>"m1"</span>, sink <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>, - m1 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span>, use_of_ff <span class='op'>=</span> <span class='st'>"min"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'> -<span class='co'># Fit the model to the FOCUS example dataset D using defaults</span> -<span class='va'>FOCUS_D</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/saemix/man/subset.html'>subset</a></span><span class='op'>(</span><span class='va'>FOCUS_2006_D</span>, <span class='va'>value</span> <span class='op'>!=</span> <span class='fl'>0</span><span class='op'>)</span> <span class='co'># remove zero values to avoid warning</span> -<span class='va'>fit</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>SFO_SFO</span>, <span class='va'>FOCUS_D</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='va'>fit.s</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/saemix/man/summary-methods.html'>summary</a></span><span class='op'>(</span><span class='va'>fit</span><span class='op'>)</span> -<span class='co'># Transformed and backtransformed parameters</span> -<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>fit.s</span><span class='op'>$</span><span class='va'>par</span>, <span class='fl'>3</span><span class='op'>)</span> -</div><div class='output co'>#> Estimate Std. Error Lower Upper -#> parent_0 99.60 1.5702 96.40 102.79 -#> log_k_parent_sink -3.04 0.0763 -3.19 -2.88 -#> log_k_parent_m1 -2.98 0.0403 -3.06 -2.90 -#> log_k_m1_sink -5.25 0.1332 -5.52 -4.98 -#> sigma 3.13 0.3585 2.40 3.85</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>fit.s</span><span class='op'>$</span><span class='va'>bpar</span>, <span class='fl'>3</span><span class='op'>)</span> -</div><div class='output co'>#> Estimate se_notrans t value Pr(>t) Lower Upper -#> parent_0 99.59848 1.57022 63.43 2.30e-36 96.40384 102.7931 -#> k_parent_sink 0.04792 0.00365 13.11 6.13e-15 0.04103 0.0560 -#> k_parent_m1 0.05078 0.00205 24.80 3.27e-23 0.04678 0.0551 -#> k_m1_sink 0.00526 0.00070 7.51 6.16e-09 0.00401 0.0069 -#> sigma 3.12550 0.35852 8.72 2.24e-10 2.39609 3.8549</div><div class='input'> -<span class='co'># \dontrun{</span> -<span class='co'># Compare to the version without transforming rate parameters (does not work</span> -<span class='co'># with analytical solution, we get NA values for m1 in predictions)</span> -<span class='va'>fit.2</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>SFO_SFO</span>, <span class='va'>FOCUS_D</span>, transform_rates <span class='op'>=</span> <span class='cn'>FALSE</span>, - solution_type <span class='op'>=</span> <span class='st'>"deSolve"</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='va'>fit.2.s</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/saemix/man/summary-methods.html'>summary</a></span><span class='op'>(</span><span class='va'>fit.2</span><span class='op'>)</span> -<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>fit.2.s</span><span class='op'>$</span><span class='va'>par</span>, <span class='fl'>3</span><span class='op'>)</span> -</div><div class='output co'>#> Estimate Std. Error Lower Upper -#> parent_0 99.59848 1.57022 96.40384 1.03e+02 -#> k_parent_sink 0.04792 0.00365 0.04049 5.54e-02 -#> k_parent_m1 0.05078 0.00205 0.04661 5.49e-02 -#> k_m1_sink 0.00526 0.00070 0.00384 6.69e-03 -#> sigma 3.12550 0.35852 2.39609 3.85e+00</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>fit.2.s</span><span class='op'>$</span><span class='va'>bpar</span>, <span class='fl'>3</span><span class='op'>)</span> -</div><div class='output co'>#> Estimate se_notrans t value Pr(>t) Lower Upper -#> parent_0 99.59848 1.57022 63.43 2.30e-36 96.40384 1.03e+02 -#> k_parent_sink 0.04792 0.00365 13.11 6.13e-15 0.04049 5.54e-02 -#> k_parent_m1 0.05078 0.00205 24.80 3.27e-23 0.04661 5.49e-02 -#> k_m1_sink 0.00526 0.00070 7.51 6.16e-09 0.00384 6.69e-03 -#> sigma 3.12550 0.35852 8.72 2.24e-10 2.39609 3.85e+00</div><div class='input'><span class='co'># }</span> - -<span class='va'>initials</span> <span class='op'><-</span> <span class='va'>fit</span><span class='op'>$</span><span class='va'>start</span><span class='op'>$</span><span class='va'>value</span> -<span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span><span class='op'>(</span><span class='va'>initials</span><span class='op'>)</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/colnames.html'>rownames</a></span><span class='op'>(</span><span class='va'>fit</span><span class='op'>$</span><span class='va'>start</span><span class='op'>)</span> -<span class='va'>transformed</span> <span class='op'><-</span> <span class='va'>fit</span><span class='op'>$</span><span class='va'>start_transformed</span><span class='op'>$</span><span class='va'>value</span> -<span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span><span class='op'>(</span><span class='va'>transformed</span><span class='op'>)</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/colnames.html'>rownames</a></span><span class='op'>(</span><span class='va'>fit</span><span class='op'>$</span><span class='va'>start_transformed</span><span class='op'>)</span> -<span class='fu'>transform_odeparms</span><span class='op'>(</span><span class='va'>initials</span>, <span class='va'>SFO_SFO</span><span class='op'>)</span> -</div><div class='output co'>#> parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink -#> 100.750000 -2.302585 -2.301586 -2.300587 </div><div class='input'><span class='fu'>backtransform_odeparms</span><span class='op'>(</span><span class='va'>transformed</span>, <span class='va'>SFO_SFO</span><span class='op'>)</span> -</div><div class='output co'>#> parent_0 k_parent_sink k_parent_m1 k_m1_sink -#> 100.7500 0.1000 0.1001 0.1002 </div><div class='input'> -<span class='co'># \dontrun{</span> -<span class='co'># The case of formation fractions (this is now the default)</span> -<span class='va'>SFO_SFO.ff</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span> - parent <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='st'>"m1"</span>, sink <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>, - m1 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span>, - use_of_ff <span class='op'>=</span> <span class='st'>"max"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'> -<span class='va'>fit.ff</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>SFO_SFO.ff</span>, <span class='va'>FOCUS_D</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='va'>fit.ff.s</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/saemix/man/summary-methods.html'>summary</a></span><span class='op'>(</span><span class='va'>fit.ff</span><span class='op'>)</span> -<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>fit.ff.s</span><span class='op'>$</span><span class='va'>par</span>, <span class='fl'>3</span><span class='op'>)</span> -</div><div class='output co'>#> Estimate Std. Error Lower Upper -#> parent_0 99.5985 1.5702 96.404 102.79 -#> log_k_parent -2.3157 0.0409 -2.399 -2.23 -#> log_k_m1 -5.2475 0.1332 -5.518 -4.98 -#> f_parent_qlogis 0.0579 0.0893 -0.124 0.24 -#> sigma 3.1255 0.3585 2.396 3.85</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>fit.ff.s</span><span class='op'>$</span><span class='va'>bpar</span>, <span class='fl'>3</span><span class='op'>)</span> -</div><div class='output co'>#> Estimate se_notrans t value Pr(>t) Lower Upper -#> parent_0 99.59848 1.57022 63.43 2.30e-36 96.40383 102.7931 -#> k_parent 0.09870 0.00403 24.47 4.96e-23 0.09082 0.1073 -#> k_m1 0.00526 0.00070 7.51 6.16e-09 0.00401 0.0069 -#> f_parent_to_m1 0.51448 0.02230 23.07 3.10e-22 0.46912 0.5596 -#> sigma 3.12550 0.35852 8.72 2.24e-10 2.39609 3.8549</div><div class='input'><span class='va'>initials</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"f_parent_to_m1"</span> <span class='op'>=</span> <span class='fl'>0.5</span><span class='op'>)</span> -<span class='va'>transformed</span> <span class='op'><-</span> <span class='fu'>transform_odeparms</span><span class='op'>(</span><span class='va'>initials</span>, <span class='va'>SFO_SFO.ff</span><span class='op'>)</span> -<span class='fu'>backtransform_odeparms</span><span class='op'>(</span><span class='va'>transformed</span>, <span class='va'>SFO_SFO.ff</span><span class='op'>)</span> -</div><div class='output co'>#> f_parent_to_m1 -#> 0.5 </div><div class='input'> -<span class='co'># And without sink</span> -<span class='va'>SFO_SFO.ff.2</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span> - parent <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='st'>"m1"</span>, sink <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span>, - m1 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span>, - use_of_ff <span class='op'>=</span> <span class='st'>"max"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'> - -<span class='va'>fit.ff.2</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>SFO_SFO.ff.2</span>, <span class='va'>FOCUS_D</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='va'>fit.ff.2.s</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/saemix/man/summary-methods.html'>summary</a></span><span class='op'>(</span><span class='va'>fit.ff.2</span><span class='op'>)</span> -<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>fit.ff.2.s</span><span class='op'>$</span><span class='va'>par</span>, <span class='fl'>3</span><span class='op'>)</span> -</div><div class='output co'>#> Estimate Std. Error Lower Upper -#> parent_0 84.79 3.012 78.67 90.91 -#> log_k_parent -2.76 0.082 -2.92 -2.59 -#> log_k_m1 -4.21 0.123 -4.46 -3.96 -#> sigma 8.22 0.943 6.31 10.14</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>fit.ff.2.s</span><span class='op'>$</span><span class='va'>bpar</span>, <span class='fl'>3</span><span class='op'>)</span> -</div><div class='output co'>#> Estimate se_notrans t value Pr(>t) Lower Upper -#> parent_0 84.7916 3.01203 28.15 1.92e-25 78.6704 90.913 -#> k_parent 0.0635 0.00521 12.19 2.91e-14 0.0538 0.075 -#> k_m1 0.0148 0.00182 8.13 8.81e-10 0.0115 0.019 -#> sigma 8.2229 0.94323 8.72 1.73e-10 6.3060 10.140</div><div class='input'><span class='co'># }</span> - -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span>, sink <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"min"</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Fit the model to the FOCUS example dataset D using defaults</span></span></span> +<span class="r-in"><span><span class="va">FOCUS_D</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span> <span class="co"># remove zero values to avoid warning</span></span></span> +<span class="r-in"><span><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">fit.s</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># Transformed and backtransformed parameters</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.s</span><span class="op">$</span><span class="va">par</span>, <span class="fl">3</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Estimate Std. Error Lower Upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 99.60 1.5702 96.40 102.79</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_parent_sink -3.04 0.0763 -3.19 -2.88</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_parent_m1 -2.98 0.0403 -3.06 -2.90</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_m1_sink -5.25 0.1332 -5.52 -4.98</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 3.13 0.3585 2.40 3.85</span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.s</span><span class="op">$</span><span class="va">bpar</span>, <span class="fl">3</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Estimate se_notrans t value Pr(>t) Lower Upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 99.59848 1.57022 63.43 2.30e-36 96.40384 102.7931</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent_sink 0.04792 0.00365 13.11 6.13e-15 0.04103 0.0560</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent_m1 0.05078 0.00205 24.80 3.27e-23 0.04678 0.0551</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1_sink 0.00526 0.00070 7.51 6.16e-09 0.00401 0.0069</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 3.12550 0.35852 8.72 2.24e-10 2.39609 3.8549</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="co"># Compare to the version without transforming rate parameters (does not work</span></span></span> +<span class="r-in"><span><span class="co"># with analytical solution, we get NA values for m1 in predictions)</span></span></span> +<span class="r-in"><span><span class="va">fit.2</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, transform_rates <span class="op">=</span> <span class="cn">FALSE</span>,</span></span> +<span class="r-in"><span> solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">fit.2.s</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.2</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.2.s</span><span class="op">$</span><span class="va">par</span>, <span class="fl">3</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Estimate Std. Error Lower Upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 99.59848 1.57022 96.40384 1.03e+02</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent_sink 0.04792 0.00365 0.04049 5.54e-02</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent_m1 0.05078 0.00205 0.04661 5.49e-02</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1_sink 0.00526 0.00070 0.00384 6.69e-03</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 3.12550 0.35852 2.39609 3.85e+00</span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.2.s</span><span class="op">$</span><span class="va">bpar</span>, <span class="fl">3</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Estimate se_notrans t value Pr(>t) Lower Upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 99.59848 1.57022 63.43 2.30e-36 96.40384 1.03e+02</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent_sink 0.04792 0.00365 13.11 6.13e-15 0.04049 5.54e-02</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent_m1 0.05078 0.00205 24.80 3.27e-23 0.04661 5.49e-02</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1_sink 0.00526 0.00070 7.51 6.16e-09 0.00384 6.69e-03</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 3.12550 0.35852 8.72 2.24e-10 2.39609 3.85e+00</span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">initials</span> <span class="op"><-</span> <span class="va">fit</span><span class="op">$</span><span class="va">start</span><span class="op">$</span><span class="va">value</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">initials</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="va">fit</span><span class="op">$</span><span class="va">start</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">transformed</span> <span class="op"><-</span> <span class="va">fit</span><span class="op">$</span><span class="va">start_transformed</span><span class="op">$</span><span class="va">value</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">transformed</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="va">fit</span><span class="op">$</span><span class="va">start_transformed</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu">transform_odeparms</span><span class="op">(</span><span class="va">initials</span>, <span class="va">SFO_SFO</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink </span> +<span class="r-out co"><span class="r-pr">#></span> 100.750000 -2.302585 -2.301586 -2.300587 </span> +<span class="r-in"><span><span class="fu">backtransform_odeparms</span><span class="op">(</span><span class="va">transformed</span>, <span class="va">SFO_SFO</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 k_parent_sink k_parent_m1 k_m1_sink </span> +<span class="r-out co"><span class="r-pr">#></span> 100.7500 0.1000 0.1001 0.1002 </span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="co"># The case of formation fractions (this is now the default)</span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO.ff</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span>, sink <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">fit.ff</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO.ff</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">fit.ff.s</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.ff</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.ff.s</span><span class="op">$</span><span class="va">par</span>, <span class="fl">3</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Estimate Std. Error Lower Upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 99.5985 1.5702 96.404 102.79</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_parent -2.3157 0.0409 -2.399 -2.23</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_m1 -5.2475 0.1332 -5.518 -4.98</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis 0.0579 0.0893 -0.124 0.24</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 3.1255 0.3585 2.396 3.85</span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.ff.s</span><span class="op">$</span><span class="va">bpar</span>, <span class="fl">3</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Estimate se_notrans t value Pr(>t) Lower Upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 99.59848 1.57022 63.43 2.30e-36 96.40383 102.7931</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent 0.09870 0.00403 24.47 4.96e-23 0.09082 0.1073</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 0.00526 0.00070 7.51 6.16e-09 0.00401 0.0069</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.51448 0.02230 23.07 3.10e-22 0.46912 0.5596</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 3.12550 0.35852 8.72 2.24e-10 2.39609 3.8549</span> +<span class="r-in"><span><span class="va">initials</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"f_parent_to_m1"</span> <span class="op">=</span> <span class="fl">0.5</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">transformed</span> <span class="op"><-</span> <span class="fu">transform_odeparms</span><span class="op">(</span><span class="va">initials</span>, <span class="va">SFO_SFO.ff</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu">backtransform_odeparms</span><span class="op">(</span><span class="va">transformed</span>, <span class="va">SFO_SFO.ff</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 </span> +<span class="r-out co"><span class="r-pr">#></span> 0.5 </span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># And without sink</span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO.ff.2</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">fit.ff.2</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO.ff.2</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">fit.ff.2.s</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.ff.2</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.ff.2.s</span><span class="op">$</span><span class="va">par</span>, <span class="fl">3</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Estimate Std. Error Lower Upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 84.79 3.012 78.67 90.91</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_parent -2.76 0.082 -2.92 -2.59</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_m1 -4.21 0.123 -4.46 -3.96</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 8.22 0.943 6.31 10.14</span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.ff.2.s</span><span class="op">$</span><span class="va">bpar</span>, <span class="fl">3</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Estimate se_notrans t value Pr(>t) Lower Upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 84.7916 3.01203 28.15 1.92e-25 78.6704 90.913</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent 0.0635 0.00521 12.19 2.91e-14 0.0538 0.075</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 0.0148 0.00182 8.13 8.81e-10 0.0115 0.019</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 8.2229 0.94323 8.72 1.73e-10 6.3060 10.140</span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - 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The fitted degradation +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Update an mkinfit model with different arguments — update.mkinfit • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Update an mkinfit model with different arguments — update.mkinfit"><meta property="og:description" content="This function will return an updated mkinfit object. The fitted degradation model parameters from the old fit are used as starting values for the updated fit. Values specified as 'parms.ini' and/or 'state.ini' will -override these starting values." /> - - -<meta name="robots" content="noindex"> - -<!-- mathjax --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script> - -<!--[if lt IE 9]> +override these starting values."><meta name="robots" content="noindex"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> - 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<li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -101,48 +44,50 @@ override these starting values." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> </li> - </ul> -</li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Update an mkinfit model with different arguments</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/update.mkinfit.R'><code>R/update.mkinfit.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/update.mkinfit.R" class="external-link"><code>R/update.mkinfit.R</code></a></small> <div class="hidden name"><code>update.mkinfit.Rd</code></div> </div> @@ -153,66 +98,67 @@ updated fit. Values specified as 'parms.ini' and/or 'state.ini' will override these starting values.</p> </div> - <pre class="usage"><span class='co'># S3 method for mkinfit</span> -<span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span><span class='op'>(</span><span class='va'>object</span>, <span class='va'>...</span>, evaluate <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span></pre> - - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>object</th> - <td><p>An mkinfit object to be updated</p></td> - </tr> - <tr> - <th>...</th> - <td><p>Arguments to <code><a href='mkinfit.html'>mkinfit</a></code> that should replace + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span>, evaluate <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>object</dt> +<dd><p>An mkinfit object to be updated</p></dd> + + +<dt>...</dt> +<dd><p>Arguments to <code><a href="mkinfit.html">mkinfit</a></code> that should replace the arguments from the original call. Arguments set to NULL will -remove arguments given in the original call</p></td> - </tr> - <tr> - <th>evaluate</th> - <td><p>Should the call be evaluated or returned as a call</p></td> - </tr> - </table> - - - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'><span class='co'># \dontrun{</span> -<span class='va'>fit</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='fu'><a href='https://rdrr.io/pkg/saemix/man/subset.html'>subset</a></span><span class='op'>(</span><span class='va'>FOCUS_2006_D</span>, <span class='va'>value</span> <span class='op'>!=</span> <span class='fl'>0</span><span class='op'>)</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='fu'><a href='parms.html'>parms</a></span><span class='op'>(</span><span class='va'>fit</span><span class='op'>)</span> -</div><div class='output co'>#> parent_0 k_parent sigma -#> 99.44423885 0.09793574 3.39632469 </div><div class='input'><span class='fu'><a href='plot.mkinfit.html'>plot_err</a></span><span class='op'>(</span><span class='va'>fit</span><span class='op'>)</span> -</div><div class='img'><img src='update.mkinfit-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='va'>fit_2</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span><span class='op'>(</span><span class='va'>fit</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span><span class='op'>)</span> -<span class='fu'><a href='parms.html'>parms</a></span><span class='op'>(</span><span class='va'>fit_2</span><span class='op'>)</span> -</div><div class='output co'>#> parent_0 k_parent sigma_low rsd_high -#> 1.008549e+02 1.005665e-01 3.752222e-03 6.763434e-02 </div><div class='input'><span class='fu'><a href='plot.mkinfit.html'>plot_err</a></span><span class='op'>(</span><span class='va'>fit_2</span><span class='op'>)</span> -</div><div class='img'><img src='update.mkinfit-2.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># }</span> -</div></pre> +remove arguments given in the original call</p></dd> + + +<dt>evaluate</dt> +<dd><p>Should the call be evaluated or returned as a call</p></dd> + +</dl></div> + + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="parms.html">parms</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 k_parent sigma </span> +<span class="r-out co"><span class="r-pr">#></span> 99.44423885 0.09793574 3.39632469 </span> +<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_err</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="update.mkinfit-1.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="va">fit_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">fit</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="parms.html">parms</a></span><span class="op">(</span><span class="va">fit_2</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 k_parent sigma_low rsd_high </span> +<span class="r-out co"><span class="r-pr">#></span> 1.008549e+02 1.005665e-01 3.752222e-03 6.763434e-02 </span> +<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_err</a></span><span class="op">(</span><span class="va">fit_2</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="update.mkinfit-2.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="co"># }</span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> diff --git a/docs/dev/sitemap.xml b/docs/dev/sitemap.xml index 04cf230e..b3542d0b 100644 --- a/docs/dev/sitemap.xml +++ b/docs/dev/sitemap.xml @@ -4,6 +4,9 @@ <loc>https://pkgdown.jrwb.de/mkin/404.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/articles/2022_wp_1.1_dmta_parent.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/articles/FOCUS_D.html</loc> </url> <url> @@ -16,6 +19,15 @@ <loc>https://pkgdown.jrwb.de/mkin/articles/mkin.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html</loc> + </url> + <url> + <loc>https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html</loc> + </url> + <url> + <loc>https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/articles/twa.html</loc> </url> <url> @@ -121,6 +133,9 @@ <loc>https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/reference/ds_mixed.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/reference/endpoints.html</loc> </url> <url> @@ -136,6 +151,9 @@ <loc>https://pkgdown.jrwb.de/mkin/reference/get_deg_func.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/reference/illparms.html</loc> </url> <url> @@ -304,6 +322,9 @@ <loc>https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/reference/summary_listing.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/reference/synthetic_data_for_UBA_2014.html</loc> </url> <url> diff --git a/docs/index.html b/docs/index.html index 67e1702a..bb14906d 100644 --- a/docs/index.html +++ b/docs/index.html @@ -44,7 +44,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.1</span> </span> </div> @@ -73,19 +73,25 @@ <a href="articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul> </li> diff --git a/docs/news/index.html b/docs/news/index.html index faaab288..f6883766 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.1</span> </span> </div> @@ -44,19 +44,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -82,13 +88,27 @@ </div> <div class="section level2"> -<h2 class="page-header" data-toc-text="1.1.2" id="mkin-112">mkin 1.1.2<a class="anchor" aria-label="anchor" href="#mkin-112"></a></h2> -<ul><li><p>‘R/mhmkin.R’: New method for performing multiple hierarchical mkin fits in one function call, optionally in parallel.</p></li> -<li><p>‘R/saem.R’: Implement and test saemix transformations for FOMC and HS. Also, error out if saemix transformations are requested but not supported.</p></li> -<li><p>‘R/saem.R’: ‘logLik’ and ‘update’ methods for ‘saem.mmkin’ objects.</p></li> -<li><p>‘R/convergence.R’: New generic to show convergence information with methods for ‘mmkin’ and ‘mhmkin’ objects.</p></li> +<h2 class="page-header" data-toc-text="1.2.1" id="mkin-121-unreleased">mkin 1.2.1 (unreleased)<a class="anchor" aria-label="anchor" href="#mkin-121-unreleased"></a></h2> +<ul><li><p>‘{data,R}/ds_mixed.rda’: Include the test data in the package instead of generating it in ‘tests/testthat/setup_script.R’. Refactor the generating code to make it consistent and update tests.</p></li> +<li><p>‘tests/testthat/setup_script.R’: Excluded another ill-defined random effect for the DFOP fit with ‘saem’, in an attempt to avoid a platform dependence that surfaced on Fedora systems on the CRAN check farm</p></li> +<li><p>‘tests/testthat/test_mixed.R’: Round parameters found by saemix to two significant digits before printing, to also help to avoid platform dependence of tests</p></li> +<li><p>‘R/saem.R’: Fix a bug that prevented that ‘error.ini’ is passed to ‘saemix_model’, and set default to c(1, 1) to avoid changing test results</p></li> +<li><p>‘R/parplot.R’: Show initial values for error model parameters</p></li> +<li><p>‘R/loglik.mkinfit.R’: Add ‘nobs’ attribute to the resulting ‘logLik’ object, in order to make test_AIC.R succeed on current R-devel</p></li> +</ul></div> + <div class="section level2"> +<h2 class="page-header" data-toc-text="1.2.0" id="mkin-120-2022-11-17">mkin 1.2.0 (2022-11-17)<a class="anchor" aria-label="anchor" href="#mkin-120-2022-11-17"></a></h2> +<ul><li><p>‘R/saem.R’: ‘logLik’, ‘update’ and ‘anova’ methods for ‘saem.mmkin’ objects.</p></li> +<li><p>‘R/saem.R’: Automatic estimation of start parameters for random effects for the case of mkin transformations, nicely improving convergence and reducing problems with iterative ODE solutions.</p></li> +<li><p>‘R/status.R’: New generic to show status information for fit array objects with methods for ‘mmkin’, ‘mhmkin’ and ‘multistart’ objects.</p></li> +<li><p>‘R/mhmkin.R’: New method for performing multiple hierarchical mkin fits in one function call, optionally in parallel.</p></li> +<li><p>‘R/mhmkin.R’: ‘anova.mhmkin’ for conveniently comparing the resulting fits.</p></li> <li><p>‘R/illparms.R’: New generic to show ill-defined parameters with methods for ‘mkinfit’, ‘mmkin’, ‘saem.mmkin’ and ‘mhmkin’ objects.</p></li> +<li><p>‘R/multistart.R’: New method for testing multiple start parameters for hierarchical model fits, with function ‘llhist’ and new generic ‘parplot’ for diagnostics, and new generics ‘which.best’ and ‘best’ for extracting the fit with the highest likelihood</p></li> <li><p>‘R/summary.mmkin.R’: Summary method for mmkin objects.</p></li> +<li><p>‘R/saem.R’: Implement and test saemix transformations for FOMC and HS. Also, error out if saemix transformations are requested but not supported.</p></li> +<li><p>‘R/read_spreadsheet.R’: Conveniently read in data from a spreadsheet file.</p></li> +<li><p>‘R/tex_listings.R’: Conveniently include summaries of fit objects in R markdown documents that are compiled to LaTeX.</p></li> </ul></div> <div class="section level2"> <h2 class="page-header" data-toc-text="1.1.1" id="mkin-111-2022-07-12">mkin 1.1.1 (2022-07-12)<a class="anchor" aria-label="anchor" href="#mkin-111-2022-07-12"></a></h2> diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 7db87468..7cf87069 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -11,7 +11,9 @@ articles: benchmarks: web_only/benchmarks.html compiled_models: web_only/compiled_models.html dimethenamid_2018: web_only/dimethenamid_2018.html -last_built: 2022-08-10T13:56Z + multistart: web_only/multistart.html + saem_benchmarks: web_only/saem_benchmarks.html +last_built: 2022-11-18T18:28Z urls: reference: https://pkgdown.jrwb.de/mkin/reference article: https://pkgdown.jrwb.de/mkin/articles diff --git a/docs/reference/AIC.mmkin.html b/docs/reference/AIC.mmkin.html index c46824e6..48e3b7e2 100644 --- a/docs/reference/AIC.mmkin.html +++ b/docs/reference/AIC.mmkin.html @@ -18,7 +18,7 @@ same dataset."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -45,19 +45,25 @@ same dataset."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -89,11 +95,11 @@ same dataset.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># S3 method for mmkin</span> -<span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span>, k <span class="op">=</span> <span class="fl">2</span><span class="op">)</span> - -<span class="co"># S3 method for mmkin</span> -<span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">BIC</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span>, k <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">BIC</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> @@ -101,14 +107,21 @@ same dataset.</p> <dl><dt>object</dt> <dd><p>An object of class <code><a href="mmkin.html">mmkin</a></code>, containing only one column.</p></dd> + + <dt>...</dt> <dd><p>For compatibility with the generic method</p></dd> + + <dt>k</dt> <dd><p>As in the generic method</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>As in the generic method (a numeric value for single fits, or a + + +<p>As in the generic method (a numeric value for single fits, or a dataframe if there are several fits in the column).</p> </div> <div id="author"> @@ -118,52 +131,50 @@ dataframe if there are several fits in the column).</p> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"> <span class="co"># skip, as it takes > 10 s on winbuilder</span></span> -<span class="r-in"> <span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS A"</span> <span class="op">=</span> <span class="va">FOCUS_2006_A</span>,</span> -<span class="r-in"> <span class="st">"FOCUS C"</span> <span class="op">=</span> <span class="va">FOCUS_2006_C</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Optimisation did not converge:</span> -<span class="r-wrn co"><span class="r-pr">#></span> false convergence (8)</span> -<span class="r-in"> <span class="co"># We get a warning because the FOMC model does not converge for the</span></span> -<span class="r-in"> <span class="co"># FOCUS A dataset, as it is well described by SFO</span></span> -<span class="r-in"></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="st">"FOCUS A"</span><span class="op">]</span><span class="op">)</span> <span class="co"># We get a single number for a single fit</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># skip, as it takes > 10 s on winbuilder</span></span></span> +<span class="r-in"><span> <span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS A"</span> <span class="op">=</span> <span class="va">FOCUS_2006_A</span>,</span></span> +<span class="r-in"><span> <span class="st">"FOCUS C"</span> <span class="op">=</span> <span class="va">FOCUS_2006_C</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="co"># We get a warning because the FOMC model does not converge for the</span></span></span> +<span class="r-in"><span> <span class="co"># FOCUS A dataset, as it is well described by SFO</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="st">"FOCUS A"</span><span class="op">]</span><span class="op">)</span> <span class="co"># We get a single number for a single fit</span></span></span> <span class="r-out co"><span class="r-pr">#></span> [1] 55.28197</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[[</span><span class="st">"SFO"</span>, <span class="st">"FOCUS A"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span> <span class="co"># or when extracting an mkinfit object</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[[</span><span class="st">"SFO"</span>, <span class="st">"FOCUS A"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span> <span class="co"># or when extracting an mkinfit object</span></span></span> <span class="r-out co"><span class="r-pr">#></span> [1] 55.28197</span> -<span class="r-in"></span> -<span class="r-in"> <span class="co"># For FOCUS A, the models fit almost equally well, so the higher the number</span></span> -<span class="r-in"> <span class="co"># of parameters, the higher (worse) the AIC</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="st">"FOCUS A"</span><span class="op">]</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># For FOCUS A, the models fit almost equally well, so the higher the number</span></span></span> +<span class="r-in"><span> <span class="co"># of parameters, the higher (worse) the AIC</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="st">"FOCUS A"</span><span class="op">]</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> df AIC</span> <span class="r-out co"><span class="r-pr">#></span> SFO 3 55.28197</span> -<span class="r-out co"><span class="r-pr">#></span> FOMC 4 57.28198</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC 4 57.28222</span> <span class="r-out co"><span class="r-pr">#></span> DFOP 5 59.28197</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="st">"FOCUS A"</span><span class="op">]</span>, k <span class="op">=</span> <span class="fl">0</span><span class="op">)</span> <span class="co"># If we do not penalize additional parameters, we get nearly the same</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="st">"FOCUS A"</span><span class="op">]</span>, k <span class="op">=</span> <span class="fl">0</span><span class="op">)</span> <span class="co"># If we do not penalize additional parameters, we get nearly the same</span></span></span> <span class="r-out co"><span class="r-pr">#></span> df AIC</span> <span class="r-out co"><span class="r-pr">#></span> SFO 3 49.28197</span> -<span class="r-out co"><span class="r-pr">#></span> FOMC 4 49.28198</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC 4 49.28222</span> <span class="r-out co"><span class="r-pr">#></span> DFOP 5 49.28197</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">BIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="st">"FOCUS A"</span><span class="op">]</span><span class="op">)</span> <span class="co"># Comparing the BIC gives a very similar picture</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">BIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="st">"FOCUS A"</span><span class="op">]</span><span class="op">)</span> <span class="co"># Comparing the BIC gives a very similar picture</span></span></span> <span class="r-out co"><span class="r-pr">#></span> df BIC</span> <span class="r-out co"><span class="r-pr">#></span> SFO 3 55.52030</span> -<span class="r-out co"><span class="r-pr">#></span> FOMC 4 57.59975</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC 4 57.59999</span> <span class="r-out co"><span class="r-pr">#></span> DFOP 5 59.67918</span> -<span class="r-in"></span> -<span class="r-in"> <span class="co"># For FOCUS C, the more complex models fit better</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="st">"FOCUS C"</span><span class="op">]</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># For FOCUS C, the more complex models fit better</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="st">"FOCUS C"</span><span class="op">]</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> df AIC</span> <span class="r-out co"><span class="r-pr">#></span> SFO 3 59.29336</span> <span class="r-out co"><span class="r-pr">#></span> FOMC 4 44.68652</span> <span class="r-out co"><span class="r-pr">#></span> DFOP 5 29.02372</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">BIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="st">"FOCUS C"</span><span class="op">]</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">BIC</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="st">"FOCUS C"</span><span class="op">]</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> df BIC</span> <span class="r-out co"><span class="r-pr">#></span> SFO 3 59.88504</span> <span class="r-out co"><span class="r-pr">#></span> FOMC 4 45.47542</span> <span class="r-out co"><span class="r-pr">#></span> DFOP 5 30.00984</span> -<span class="r-in"> </span> -<span class="r-in"></span> +<span class="r-in"><span> </span></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -178,7 +189,7 @@ dataframe if there are several fits in the column).</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/CAKE_export.html b/docs/reference/CAKE_export.html index 4a0b599b..f1edaab2 100644 --- a/docs/reference/CAKE_export.html +++ b/docs/reference/CAKE_export.html @@ -18,7 +18,7 @@ specified as well."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/aj </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -45,19 +45,25 @@ specified as well."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/aj <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -89,21 +95,21 @@ specified as well.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">CAKE_export</span><span class="op">(</span> - <span class="va">ds</span>, - map <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="st">"Parent"</span><span class="op">)</span>, - links <span class="op">=</span> <span class="cn">NA</span>, - filename <span class="op">=</span> <span class="st">"CAKE_export.csf"</span>, - path <span class="op">=</span> <span class="st">"."</span>, - overwrite <span class="op">=</span> <span class="cn">FALSE</span>, - study <span class="op">=</span> <span class="st">"Degradinol aerobic soil degradation"</span>, - description <span class="op">=</span> <span class="st">""</span>, - time_unit <span class="op">=</span> <span class="st">"days"</span>, - res_unit <span class="op">=</span> <span class="st">"% AR"</span>, - comment <span class="op">=</span> <span class="st">""</span>, - date <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Sys.time.html" class="external-link">Sys.Date</a></span><span class="op">(</span><span class="op">)</span>, - optimiser <span class="op">=</span> <span class="st">"IRLS"</span> -<span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">CAKE_export</span><span class="op">(</span></span> +<span> <span class="va">ds</span>,</span> +<span> map <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="st">"Parent"</span><span class="op">)</span>,</span> +<span> links <span class="op">=</span> <span class="cn">NA</span>,</span> +<span> filename <span class="op">=</span> <span class="st">"CAKE_export.csf"</span>,</span> +<span> path <span class="op">=</span> <span class="st">"."</span>,</span> +<span> overwrite <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> study <span class="op">=</span> <span class="st">"Degradinol aerobic soil degradation"</span>,</span> +<span> description <span class="op">=</span> <span class="st">""</span>,</span> +<span> time_unit <span class="op">=</span> <span class="st">"days"</span>,</span> +<span> res_unit <span class="op">=</span> <span class="st">"% AR"</span>,</span> +<span> comment <span class="op">=</span> <span class="st">""</span>,</span> +<span> date <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Sys.time.html" class="external-link">Sys.Date</a></span><span class="op">(</span><span class="op">)</span>,</span> +<span> optimiser <span class="op">=</span> <span class="st">"IRLS"</span></span> +<span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> @@ -111,38 +117,65 @@ specified as well.</p> <dl><dt>ds</dt> <dd><p>A named list of datasets in long format as compatible with <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd> + + <dt>map</dt> <dd><p>A character vector with CAKE compartment names (Parent, A1, ...), named with the names used in the list of datasets.</p></dd> + + <dt>links</dt> <dd><p>An optional character vector of target compartments, named with the names of the source compartments. In order to make this easier, the names are used as in the datasets supplied.</p></dd> + + <dt>filename</dt> <dd><p>Where to write the result. Should end in .csf in order to be compatible with CAKE.</p></dd> + + <dt>path</dt> <dd><p>An optional path to the output file.</p></dd> + + <dt>overwrite</dt> <dd><p>If TRUE, existing files are overwritten.</p></dd> + + <dt>study</dt> <dd><p>The name of the study.</p></dd> + + <dt>description</dt> <dd><p>An optional description.</p></dd> + + <dt>time_unit</dt> <dd><p>The time unit for the residue data.</p></dd> + + <dt>res_unit</dt> <dd><p>The unit used for the residues.</p></dd> + + <dt>comment</dt> <dd><p>An optional comment.</p></dd> + + <dt>date</dt> <dd><p>The date of file creation.</p></dd> + + <dt>optimiser</dt> <dd><p>Can be OLS or IRLS.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>The function is called for its side effect.</p> + + +<p>The function is called for its side effect.</p> </div> <div id="author"> <h2>Author</h2> @@ -161,7 +194,7 @@ compatible with CAKE.</p></dd> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/D24_2014.html b/docs/reference/D24_2014.html index 6e2ec0ba..a22c2f73 100644 --- a/docs/reference/D24_2014.html +++ b/docs/reference/D24_2014.html @@ -22,7 +22,7 @@ constrained by data protection regulations."><!-- mathjax --><script src="https: </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -49,19 +49,25 @@ constrained by data protection regulations."><!-- mathjax --><script src="https: <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -97,7 +103,7 @@ constrained by data protection regulations.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="va">D24_2014</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">D24_2014</span></span></code></pre></div> </div> <div id="format"> @@ -124,7 +130,7 @@ specific pieces of information in the comments.</p> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">D24_2014</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">D24_2014</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> <mkindsg> holding 5 mkinds objects</span> <span class="r-out co"><span class="r-pr">#></span> Title $title: Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 </span> <span class="r-out co"><span class="r-pr">#></span> Occurrence of observed compounds $observed_n:</span> @@ -145,8 +151,8 @@ specific pieces of information in the comments.</p> <span class="r-out co"><span class="r-pr">#></span> RefSol 03-G 0.5 20</span> <span class="r-out co"><span class="r-pr">#></span> Site E1 0.5 20</span> <span class="r-out co"><span class="r-pr">#></span> Site I2 0.5 20</span> -<span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">D24_2014</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">D24_2014</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> <mkinds> with $title: Mississippi </span> <span class="r-out co"><span class="r-pr">#></span> Observed compounds $observed: D24 </span> <span class="r-out co"><span class="r-pr">#></span> Sampling times $sampling_times:</span> @@ -166,11 +172,11 @@ specific pieces of information in the comments.</p> <span class="r-out co"><span class="r-pr">#></span> 11 183 11.3</span> <span class="r-out co"><span class="r-pr">#></span> 12 273 9.9</span> <span class="r-out co"><span class="r-pr">#></span> 13 365 6.3</span> -<span class="r-in"><span class="va">m_D24</span> <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>D24 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"DCP"</span><span class="op">)</span>,</span> -<span class="r-in"> DCP <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"DCA"</span><span class="op">)</span>,</span> -<span class="r-in"> DCA <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span><span class="va">m_D24</span> <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>D24 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"DCP"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> DCP <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"DCA"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> DCA <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">m_D24</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">m_D24</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> <mkinmod> model generated with</span> <span class="r-out co"><span class="r-pr">#></span> Use of formation fractions $use_of_ff: max </span> <span class="r-out co"><span class="r-pr">#></span> Specification $spec:</span> @@ -186,11 +192,11 @@ specific pieces of information in the comments.</p> <span class="r-out co"><span class="r-pr">#></span> d_D24/dt = - k_D24 * D24</span> <span class="r-out co"><span class="r-pr">#></span> d_DCP/dt = + f_D24_to_DCP * k_D24 * D24 - k_DCP * DCP</span> <span class="r-out co"><span class="r-pr">#></span> d_DCA/dt = + f_DCP_to_DCA * k_DCP * DCP - k_DCA * DCA</span> -<span class="r-in"><span class="va">m_D24_2</span> <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>D24 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="st">"DCP"</span><span class="op">)</span>,</span> -<span class="r-in"> DCP <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"DCA"</span><span class="op">)</span>,</span> -<span class="r-in"> DCA <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span><span class="va">m_D24_2</span> <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>D24 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="st">"DCP"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> DCP <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"DCA"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> DCA <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">m_D24_2</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">m_D24_2</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> <mkinmod> model generated with</span> <span class="r-out co"><span class="r-pr">#></span> Use of formation fractions $use_of_ff: max </span> <span class="r-out co"><span class="r-pr">#></span> Specification $spec:</span> @@ -209,7 +215,7 @@ specific pieces of information in the comments.</p> <span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2</span> <span class="r-out co"><span class="r-pr">#></span> * time))) * D24 - k_DCP * DCP</span> <span class="r-out co"><span class="r-pr">#></span> d_DCA/dt = + f_DCP_to_DCA * k_DCP * DCP - k_DCA * DCA</span> -<span class="r-in"><span class="co"># }</span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> </code></pre></div> </div> </div> @@ -224,7 +230,7 @@ specific pieces of information in the comments.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/DFOP.solution.html b/docs/reference/DFOP.solution.html index 41a7d256..4a8cb640 100644 --- a/docs/reference/DFOP.solution.html +++ b/docs/reference/DFOP.solution.html @@ -18,7 +18,7 @@ two exponential decline functions."><!-- mathjax --><script src="https://cdnjs.c </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -45,19 +45,25 @@ two exponential decline functions."><!-- mathjax --><script src="https://cdnjs.c <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -89,26 +95,37 @@ two exponential decline functions.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">DFOP.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">k1</span>, <span class="va">k2</span>, <span class="va">g</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">DFOP.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">k1</span>, <span class="va">k2</span>, <span class="va">g</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>t</dt> <dd><p>Time.</p></dd> + + <dt>parent_0</dt> <dd><p>Starting value for the response variable at time zero.</p></dd> + + <dt>k1</dt> <dd><p>First kinetic constant.</p></dd> + + <dt>k2</dt> <dd><p>Second kinetic constant.</p></dd> + + <dt>g</dt> <dd><p>Fraction of the starting value declining according to the first kinetic constant.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>The value of the response variable at time <code>t</code>.</p> + + +<p>The value of the response variable at time <code>t</code>.</p> </div> <div id="references"> <h2>References</h2> @@ -136,10 +153,10 @@ Version 1.1, 18 December 2014 <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">DFOP.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">5</span>, <span class="fl">0.5</span>, <span class="fl">0.3</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">4</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span><span class="op">)</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">DFOP.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">5</span>, <span class="fl">0.5</span>, <span class="fl">0.3</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">4</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="DFOP.solution-1.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -154,7 +171,7 @@ Version 1.1, 18 December 2014 </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/Extract.mmkin.html b/docs/reference/Extract.mmkin.html index e00391e8..1f528615 100644 --- a/docs/reference/Extract.mmkin.html +++ b/docs/reference/Extract.mmkin.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -44,19 +44,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -95,19 +101,30 @@ <h2>Arguments</h2> <dl><dt>x</dt> <dd><p>An <code><a href="mmkin.html">mmkin</a> object</code></p></dd> + + <dt>i</dt> <dd><p>Row index selecting the fits for specific models</p></dd> + + <dt>j</dt> <dd><p>Column index selecting the fits to specific datasets</p></dd> + + <dt>...</dt> <dd><p>Not used, only there to satisfy the generic method definition</p></dd> + + <dt>drop</dt> <dd><p>If FALSE, the method always returns an mmkin object, otherwise either a list of mkinfit objects or a single mkinfit object.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>An object of class <code><a href="mmkin.html">mmkin</a></code>.</p> + + +<p>An object of class <code><a href="mmkin.html">mmkin</a></code>.</p> </div> <div id="author"> <h2>Author</h2> @@ -116,11 +133,11 @@ either a list of mkinfit objects or a single mkinfit object.</p></dd> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"> <span class="co"># Only use one core, to pass R CMD check --as-cran</span></span> -<span class="r-in"> <span class="va">fits</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>B <span class="op">=</span> <span class="va">FOCUS_2006_B</span>, C <span class="op">=</span> <span class="va">FOCUS_2006_C</span><span class="op">)</span>,</span> -<span class="r-in"> cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"> <span class="va">fits</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># Only use one core, to pass R CMD check --as-cran</span></span></span> +<span class="r-in"><span> <span class="va">fits</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>B <span class="op">=</span> <span class="va">FOCUS_2006_B</span>, C <span class="op">=</span> <span class="va">FOCUS_2006_C</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">fits</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span></span></span> <span class="r-out co"><span class="r-pr">#></span> <mmkin> object</span> <span class="r-out co"><span class="r-pr">#></span> Status of individual fits:</span> <span class="r-out co"><span class="r-pr">#></span> </span> @@ -129,7 +146,7 @@ either a list of mkinfit objects or a single mkinfit object.</p></dd> <span class="r-out co"><span class="r-pr">#></span> FOMC OK OK</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> OK: No warnings</span> -<span class="r-in"> <span class="va">fits</span><span class="op">[</span>, <span class="st">"B"</span><span class="op">]</span></span> +<span class="r-in"><span> <span class="va">fits</span><span class="op">[</span>, <span class="st">"B"</span><span class="op">]</span></span></span> <span class="r-out co"><span class="r-pr">#></span> <mmkin> object</span> <span class="r-out co"><span class="r-pr">#></span> Status of individual fits:</span> <span class="r-out co"><span class="r-pr">#></span> </span> @@ -139,7 +156,7 @@ either a list of mkinfit objects or a single mkinfit object.</p></dd> <span class="r-out co"><span class="r-pr">#></span> FOMC OK</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> OK: No warnings</span> -<span class="r-in"> <span class="va">fits</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="st">"B"</span><span class="op">]</span></span> +<span class="r-in"><span> <span class="va">fits</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="st">"B"</span><span class="op">]</span></span></span> <span class="r-out co"><span class="r-pr">#></span> <mmkin> object</span> <span class="r-out co"><span class="r-pr">#></span> Status of individual fits:</span> <span class="r-out co"><span class="r-pr">#></span> </span> @@ -148,14 +165,14 @@ either a list of mkinfit objects or a single mkinfit object.</p></dd> <span class="r-out co"><span class="r-pr">#></span> SFO OK</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> OK: No warnings</span> -<span class="r-in"></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span></span> -<span class="r-in"> <span class="co"># This extracts an mkinfit object with lots of components</span></span> -<span class="r-in"> <span class="va">fits</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="st">"B"</span><span class="op">]</span><span class="op">]</span></span> -<span class="r-in"> <span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> <span class="co"># This extracts an mkinfit object with lots of components</span></span></span> +<span class="r-in"><span> <span class="va">fits</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="st">"B"</span><span class="op">]</span><span class="op">]</span></span></span> +<span class="r-in"><span> <span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> $par</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 log_alpha log_beta sigma </span> -<span class="r-out co"><span class="r-pr">#></span> 99.666193 2.549849 5.050586 1.890202 </span> +<span class="r-out co"><span class="r-pr">#></span> 99.666192 2.549850 5.050586 1.890202 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> $objective</span> <span class="r-out co"><span class="r-pr">#></span> [1] 28.58291</span> @@ -168,7 +185,7 @@ either a list of mkinfit objects or a single mkinfit object.</p></dd> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> $evaluations</span> <span class="r-out co"><span class="r-pr">#></span> function gradient </span> -<span class="r-out co"><span class="r-pr">#></span> 25 72 </span> +<span class="r-out co"><span class="r-pr">#></span> 25 78 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> $message</span> <span class="r-out co"><span class="r-pr">#></span> [1] "both X-convergence and relative convergence (5)"</span> @@ -187,7 +204,7 @@ either a list of mkinfit objects or a single mkinfit object.</p></dd> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html b/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html index 93602958..e6052063 100644 --- a/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html +++ b/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html @@ -21,7 +21,7 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -48,19 +48,25 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -95,7 +101,7 @@ in this fit.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="va">FOCUS_2006_DFOP_ref_A_to_B</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">FOCUS_2006_DFOP_ref_A_to_B</span></span></code></pre></div> </div> <div id="format"> @@ -137,7 +143,7 @@ in this fit.</p> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">FOCUS_2006_DFOP_ref_A_to_B</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">FOCUS_2006_DFOP_ref_A_to_B</span><span class="op">)</span></span></span> </code></pre></div> </div> </div> @@ -152,7 +158,7 @@ in this fit.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html b/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html index 31c14505..76c72fd5 100644 --- a/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html +++ b/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html @@ -21,7 +21,7 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -48,19 +48,25 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -95,7 +101,7 @@ in this fit.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="va">FOCUS_2006_FOMC_ref_A_to_F</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">FOCUS_2006_FOMC_ref_A_to_F</span></span></code></pre></div> </div> <div id="format"> @@ -134,7 +140,7 @@ in this fit.</p> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">FOCUS_2006_FOMC_ref_A_to_F</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">FOCUS_2006_FOMC_ref_A_to_F</span><span class="op">)</span></span></span> </code></pre></div> </div> </div> @@ -149,7 +155,7 @@ in this fit.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/FOCUS_2006_HS_ref_A_to_F.html b/docs/reference/FOCUS_2006_HS_ref_A_to_F.html index 83ab4e56..de4908e6 100644 --- a/docs/reference/FOCUS_2006_HS_ref_A_to_F.html +++ b/docs/reference/FOCUS_2006_HS_ref_A_to_F.html @@ -21,7 +21,7 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -48,19 +48,25 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -95,7 +101,7 @@ in this fit.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="va">FOCUS_2006_HS_ref_A_to_F</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">FOCUS_2006_HS_ref_A_to_F</span></span></code></pre></div> </div> <div id="format"> @@ -137,7 +143,7 @@ in this fit.</p> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">FOCUS_2006_HS_ref_A_to_F</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">FOCUS_2006_HS_ref_A_to_F</span><span class="op">)</span></span></span> </code></pre></div> </div> </div> @@ -152,7 +158,7 @@ in this fit.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html b/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html index f47cba8d..1ba63264 100644 --- a/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html +++ b/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html @@ -21,7 +21,7 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -48,19 +48,25 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -95,7 +101,7 @@ in this fit.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="va">FOCUS_2006_SFO_ref_A_to_F</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">FOCUS_2006_SFO_ref_A_to_F</span></span></code></pre></div> </div> <div id="format"> @@ -131,7 +137,7 @@ in this fit.</p> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">FOCUS_2006_SFO_ref_A_to_F</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">FOCUS_2006_SFO_ref_A_to_F</span><span class="op">)</span></span></span> </code></pre></div> </div> </div> @@ -146,7 +152,7 @@ in this fit.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/FOCUS_2006_datasets.html b/docs/reference/FOCUS_2006_datasets.html index aeeaf723..385c0f2b 100644 --- a/docs/reference/FOCUS_2006_datasets.html +++ b/docs/reference/FOCUS_2006_datasets.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -44,19 +44,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -87,12 +93,12 @@ </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="va">FOCUS_2006_A</span> - <span class="va">FOCUS_2006_B</span> - <span class="va">FOCUS_2006_C</span> - <span class="va">FOCUS_2006_D</span> - <span class="va">FOCUS_2006_E</span> - <span class="va">FOCUS_2006_F</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">FOCUS_2006_A</span></span> +<span> <span class="va">FOCUS_2006_B</span></span> +<span> <span class="va">FOCUS_2006_C</span></span> +<span> <span class="va">FOCUS_2006_D</span></span> +<span> <span class="va">FOCUS_2006_E</span></span> +<span> <span class="va">FOCUS_2006_F</span></span></code></pre></div> </div> <div id="format"> @@ -119,7 +125,7 @@ <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="va">FOCUS_2006_C</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">FOCUS_2006_C</span></span></span> <span class="r-out co"><span class="r-pr">#></span> name time value</span> <span class="r-out co"><span class="r-pr">#></span> 1 parent 0 85.1</span> <span class="r-out co"><span class="r-pr">#></span> 2 parent 1 57.9</span> @@ -144,7 +150,7 @@ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/FOMC.solution.html b/docs/reference/FOMC.solution.html index 7274cadc..e288c955 100644 --- a/docs/reference/FOMC.solution.html +++ b/docs/reference/FOMC.solution.html @@ -18,7 +18,7 @@ a decreasing rate constant."><!-- mathjax --><script src="https://cdnjs.cloudfla </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -45,19 +45,25 @@ a decreasing rate constant."><!-- mathjax --><script src="https://cdnjs.cloudfla <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -89,24 +95,33 @@ a decreasing rate constant.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">FOMC.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">alpha</span>, <span class="va">beta</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">FOMC.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">alpha</span>, <span class="va">beta</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>t</dt> <dd><p>Time.</p></dd> + + <dt>parent_0</dt> <dd><p>Starting value for the response variable at time zero.</p></dd> + + <dt>alpha</dt> <dd><p>Shape parameter determined by coefficient of variation of rate constant values.</p></dd> + + <dt>beta</dt> <dd><p>Location parameter.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>The value of the response variable at time <code>t</code>.</p> + + +<p>The value of the response variable at time <code>t</code>.</p> </div> <div id="details"> <h2>Details</h2> @@ -117,8 +132,8 @@ in the original equation.</p> <div id="note"> <h2>Note</h2> <p>The solution of the FOMC kinetic model reduces to the -<code><a href="SFO.solution.html">SFO.solution</a></code> for large values of <code>alpha</code> and -<code>beta</code> with \(k = \frac{\beta}{\alpha}\).</p> +<code><a href="SFO.solution.html">SFO.solution</a></code> for large values of <code>alpha</code> and <code>beta</code> +with \(k = \frac{\beta}{\alpha}\).</p> </div> <div id="references"> <h2>References</h2> @@ -149,10 +164,10 @@ Technology</em> <b>24</b>, 1032-1038</p> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">FOMC.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">10</span>, <span class="fl">2</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">2</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">100</span><span class="op">)</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">FOMC.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">10</span>, <span class="fl">2</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">2</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">100</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="FOMC.solution-1.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -167,7 +182,7 @@ Technology</em> <b>24</b>, 1032-1038</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/HS.solution.html b/docs/reference/HS.solution.html index 03b30958..21bd919a 100644 --- a/docs/reference/HS.solution.html +++ b/docs/reference/HS.solution.html @@ -18,7 +18,7 @@ between them."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -45,19 +45,25 @@ between them."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -89,27 +95,38 @@ between them.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">HS.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">k1</span>, <span class="va">k2</span>, <span class="va">tb</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">HS.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">k1</span>, <span class="va">k2</span>, <span class="va">tb</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>t</dt> <dd><p>Time.</p></dd> + + <dt>parent_0</dt> <dd><p>Starting value for the response variable at time zero.</p></dd> + + <dt>k1</dt> <dd><p>First kinetic constant.</p></dd> + + <dt>k2</dt> <dd><p>Second kinetic constant.</p></dd> + + <dt>tb</dt> <dd><p>Break point. Before this time, exponential decline according to <code>k1</code> is calculated, after this time, exponential decline proceeds according to <code>k2</code>.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>The value of the response variable at time <code>t</code>.</p> + + +<p>The value of the response variable at time <code>t</code>.</p> </div> <div id="references"> <h2>References</h2> @@ -137,10 +154,10 @@ Version 1.1, 18 December 2014 <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">HS.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">2</span>, <span class="fl">0.3</span>, <span class="fl">0.5</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">2</span>, ylim<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span><span class="op">)</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">HS.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">2</span>, <span class="fl">0.3</span>, <span class="fl">0.5</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">2</span>, ylim<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>,<span class="fl">100</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="HS.solution-1.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -155,7 +172,7 @@ Version 1.1, 18 December 2014 </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/IORE.solution.html b/docs/reference/IORE.solution.html index 0116760d..57e2e1f4 100644 --- a/docs/reference/IORE.solution.html +++ b/docs/reference/IORE.solution.html @@ -18,7 +18,7 @@ a concentration dependent rate constant."><!-- mathjax --><script src="https://c </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -45,19 +45,25 @@ a concentration dependent rate constant."><!-- mathjax --><script src="https://c <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -89,24 +95,33 @@ a concentration dependent rate constant.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">IORE.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">k__iore</span>, <span class="va">N</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">IORE.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">k__iore</span>, <span class="va">N</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>t</dt> <dd><p>Time.</p></dd> + + <dt>parent_0</dt> <dd><p>Starting value for the response variable at time zero.</p></dd> + + <dt>k__iore</dt> <dd><p>Rate constant. Note that this depends on the concentration units used.</p></dd> + + <dt>N</dt> <dd><p>Exponent describing the nonlinearity of the rate equation</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>The value of the response variable at time <code>t</code>.</p> + + +<p>The value of the response variable at time <code>t</code>.</p> </div> <div id="note"> <h2>Note</h2> @@ -133,29 +148,29 @@ for Evaluating and Calculating Degradation Kinetics in Environmental Media</p> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">IORE.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.2</span>, <span class="fl">1.3</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">2</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">100</span><span class="op">)</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">IORE.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.2</span>, <span class="fl">1.3</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">2</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">100</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="IORE.solution-1.png" alt="" width="700" height="433"></span> -<span class="r-in"> <span class="co"># \dontrun{</span></span> -<span class="r-in"> <span class="va">fit.fomc</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"> <span class="va">fit.iore</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"IORE"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"> <span class="va">fit.iore.deS</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"IORE"</span>, <span class="va">FOCUS_2006_C</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="va">fit.fomc</span><span class="op">$</span><span class="va">par</span>, <span class="va">fit.iore</span><span class="op">$</span><span class="va">par</span>, <span class="va">fit.iore.deS</span><span class="op">$</span><span class="va">par</span>, </span> -<span class="r-in"> row.names <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"model par"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span> <span class="va">fit.fomc</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">fit.iore</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"IORE"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">fit.iore.deS</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"IORE"</span>, <span class="va">FOCUS_2006_C</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="va">fit.fomc</span><span class="op">$</span><span class="va">par</span>, <span class="va">fit.iore</span><span class="op">$</span><span class="va">par</span>, <span class="va">fit.iore.deS</span><span class="op">$</span><span class="va">par</span>,</span></span> +<span class="r-in"><span> row.names <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"model par"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> fit.fomc.par fit.iore.par fit.iore.deS.par</span> -<span class="r-out co"><span class="r-pr">#></span> model par 1 85.87489063 85.874890 85.874891</span> +<span class="r-out co"><span class="r-pr">#></span> model par 1 85.87489063 85.874890 85.874890</span> <span class="r-out co"><span class="r-pr">#></span> model par 2 0.05192238 -4.826631 -4.826631</span> <span class="r-out co"><span class="r-pr">#></span> model par 3 0.65096665 1.949403 1.949403</span> <span class="r-out co"><span class="r-pr">#></span> model par 4 1.85744396 1.857444 1.857444</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span>fomc <span class="op">=</span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fit.fomc</span><span class="op">)</span><span class="op">$</span><span class="va">distimes</span>, iore <span class="op">=</span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fit.iore</span><span class="op">)</span><span class="op">$</span><span class="va">distimes</span>, </span> -<span class="r-in"> iore.deS <span class="op">=</span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fit.iore</span><span class="op">)</span><span class="op">$</span><span class="va">distimes</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span>fomc <span class="op">=</span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fit.fomc</span><span class="op">)</span><span class="op">$</span><span class="va">distimes</span>, iore <span class="op">=</span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fit.iore</span><span class="op">)</span><span class="op">$</span><span class="va">distimes</span>,</span></span> +<span class="r-in"><span> iore.deS <span class="op">=</span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fit.iore</span><span class="op">)</span><span class="op">$</span><span class="va">distimes</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back</span> <span class="r-out co"><span class="r-pr">#></span> fomc 1.785233 15.1479 4.559973</span> <span class="r-out co"><span class="r-pr">#></span> iore 1.785233 15.1479 4.559973</span> <span class="r-out co"><span class="r-pr">#></span> iore.deS 1.785233 15.1479 4.559973</span> -<span class="r-in"> <span class="co"># }</span></span> -<span class="r-in"></span> +<span class="r-in"><span> <span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -170,7 +185,7 @@ for Evaluating and Calculating Degradation Kinetics in Environmental Media</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/NAFTA_SOP_2015.html b/docs/reference/NAFTA_SOP_2015.html index 06b5bd6f..3d00e9f6 100644 --- a/docs/reference/NAFTA_SOP_2015.html +++ b/docs/reference/NAFTA_SOP_2015.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -44,19 +44,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -87,8 +93,8 @@ </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="va">NAFTA_SOP_Appendix_B</span> - <span class="va">NAFTA_SOP_Appendix_D</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">NAFTA_SOP_Appendix_B</span></span> +<span> <span class="va">NAFTA_SOP_Appendix_D</span></span></code></pre></div> </div> <div id="format"> @@ -118,12 +124,12 @@ <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"> <span class="va">nafta_evaluation</span> <span class="op"><-</span> <span class="fu"><a href="nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Appendix_D</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span> <span class="va">nafta_evaluation</span> <span class="op"><-</span> <span class="fu"><a href="nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Appendix_D</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span> <span class="r-msg co"><span class="r-pr">#></span> The representative half-life of the IORE model is longer than the one corresponding</span> <span class="r-msg co"><span class="r-pr">#></span> to the terminal degradation rate found with the DFOP model.</span> <span class="r-msg co"><span class="r-pr">#></span> The representative half-life obtained from the DFOP model may be used</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">nafta_evaluation</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">nafta_evaluation</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Sums of squares:</span> <span class="r-out co"><span class="r-pr">#></span> SFO IORE DFOP </span> <span class="r-out co"><span class="r-pr">#></span> 1378.6832 615.7730 517.8836 </span> @@ -162,7 +168,7 @@ <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Representative half-life:</span> <span class="r-out co"><span class="r-pr">#></span> [1] 841.41</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">nafta_evaluation</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">nafta_evaluation</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="NAFTA_SOP_2015-1.png" alt="" width="700" height="433"></span> </code></pre></div> </div> @@ -178,7 +184,7 @@ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/NAFTA_SOP_Attachment.html b/docs/reference/NAFTA_SOP_Attachment.html index a9f4e36f..04f38b78 100644 --- a/docs/reference/NAFTA_SOP_Attachment.html +++ b/docs/reference/NAFTA_SOP_Attachment.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -44,19 +44,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -87,7 +93,7 @@ </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="va">NAFTA_SOP_Attachment</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">NAFTA_SOP_Attachment</span></span></code></pre></div> </div> <div id="format"> @@ -109,10 +115,10 @@ <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"> <span class="va">nafta_att_p5a</span> <span class="op"><-</span> <span class="fu"><a href="nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p5a"</span><span class="op">]</span><span class="op">]</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span> <span class="va">nafta_att_p5a</span> <span class="op"><-</span> <span class="fu"><a href="nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p5a"</span><span class="op">]</span><span class="op">]</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span> <span class="r-msg co"><span class="r-pr">#></span> The half-life obtained from the IORE model may be used</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">nafta_att_p5a</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">nafta_att_p5a</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Sums of squares:</span> <span class="r-out co"><span class="r-pr">#></span> SFO IORE DFOP </span> <span class="r-out co"><span class="r-pr">#></span> 465.21753 56.27506 32.06401 </span> @@ -151,7 +157,7 @@ <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Representative half-life:</span> <span class="r-out co"><span class="r-pr">#></span> [1] 321.51</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">nafta_att_p5a</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">nafta_att_p5a</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="NAFTA_SOP_Attachment-1.png" alt="" width="700" height="433"></span> </code></pre></div> </div> @@ -167,7 +173,7 @@ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/Rplot001.png b/docs/reference/Rplot001.png Binary files differindex 17a35806..b3448db0 100644 --- a/docs/reference/Rplot001.png +++ b/docs/reference/Rplot001.png diff --git a/docs/reference/Rplot002.png b/docs/reference/Rplot002.png Binary files differindex f06a860e..27feab09 100644 --- a/docs/reference/Rplot002.png +++ b/docs/reference/Rplot002.png diff --git a/docs/reference/Rplot003.png b/docs/reference/Rplot003.png Binary files differindex 1af5d4b4..774715e0 100644 --- a/docs/reference/Rplot003.png +++ b/docs/reference/Rplot003.png diff --git a/docs/reference/Rplot004.png b/docs/reference/Rplot004.png Binary files differindex 12d337a4..37e0e95e 100644 --- a/docs/reference/Rplot004.png +++ b/docs/reference/Rplot004.png diff --git a/docs/reference/Rplot005.png b/docs/reference/Rplot005.png Binary files differindex cb419daa..76f25647 100644 --- a/docs/reference/Rplot005.png +++ b/docs/reference/Rplot005.png diff --git a/docs/reference/Rplot006.png b/docs/reference/Rplot006.png Binary files differindex bc6979e9..48f5bbd8 100644 --- a/docs/reference/Rplot006.png +++ b/docs/reference/Rplot006.png diff --git a/docs/reference/SFO.solution.html b/docs/reference/SFO.solution.html index 3607a5ac..3aabc1d6 100644 --- a/docs/reference/SFO.solution.html +++ b/docs/reference/SFO.solution.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -44,19 +44,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -87,21 +93,28 @@ </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">SFO.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">k</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">SFO.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">k</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>t</dt> <dd><p>Time.</p></dd> + + <dt>parent_0</dt> <dd><p>Starting value for the response variable at time zero.</p></dd> + + <dt>k</dt> <dd><p>Kinetic rate constant.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>The value of the response variable at time <code>t</code>.</p> + + +<p>The value of the response variable at time <code>t</code>.</p> </div> <div id="references"> <h2>References</h2> @@ -129,10 +142,10 @@ Version 1.1, 18 December 2014 <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">SFO.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">3</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">2</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">SFO.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">3</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">2</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="SFO.solution-1.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -147,7 +160,7 @@ Version 1.1, 18 December 2014 </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/SFORB.solution.html b/docs/reference/SFORB.solution.html index 9b4c391c..89c932b6 100644 --- a/docs/reference/SFORB.solution.html +++ b/docs/reference/SFORB.solution.html @@ -21,7 +21,7 @@ and no substance in the bound fraction."><!-- mathjax --><script src="https://cd </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -48,19 +48,25 @@ and no substance in the bound fraction."><!-- mathjax --><script src="https://cd <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -95,26 +101,37 @@ and no substance in the bound fraction.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">SFORB.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">k_12</span>, <span class="va">k_21</span>, <span class="va">k_1output</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">SFORB.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">k_12</span>, <span class="va">k_21</span>, <span class="va">k_1output</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>t</dt> <dd><p>Time.</p></dd> + + <dt>parent_0</dt> <dd><p>Starting value for the response variable at time zero.</p></dd> + + <dt>k_12</dt> <dd><p>Kinetic constant describing transfer from free to bound.</p></dd> + + <dt>k_21</dt> <dd><p>Kinetic constant describing transfer from bound to free.</p></dd> + + <dt>k_1output</dt> <dd><p>Kinetic constant describing degradation of the free fraction.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>The value of the response variable, which is the sum of free and + + +<p>The value of the response variable, which is the sum of free and bound fractions at time <code>t</code>.</p> </div> <div id="references"> @@ -143,10 +160,10 @@ Version 1.1, 18 December 2014 <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">SFORB.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.5</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">2</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">SFORB.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.5</span>, <span class="fl">2</span>, <span class="fl">3</span><span class="op">)</span>, <span class="fl">0</span>, <span class="fl">2</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="SFORB.solution-1.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -161,7 +178,7 @@ Version 1.1, 18 December 2014 </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/add_err.html b/docs/reference/add_err.html index 3a129533..4332ba05 100644 --- a/docs/reference/add_err.html +++ b/docs/reference/add_err.html @@ -19,7 +19,7 @@ may depend on the predicted value and is specified as a standard deviation."><!- </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -46,19 +46,25 @@ may depend on the predicted value and is specified as a standard deviation."><!- <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -91,16 +97,16 @@ may depend on the predicted value and is specified as a standard deviation.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">add_err</span><span class="op">(</span> - <span class="va">prediction</span>, - <span class="va">sdfunc</span>, - secondary <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span>, - n <span class="op">=</span> <span class="fl">10</span>, - LOD <span class="op">=</span> <span class="fl">0.1</span>, - reps <span class="op">=</span> <span class="fl">2</span>, - digits <span class="op">=</span> <span class="fl">1</span>, - seed <span class="op">=</span> <span class="cn">NA</span> -<span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">add_err</span><span class="op">(</span></span> +<span> <span class="va">prediction</span>,</span> +<span> <span class="va">sdfunc</span>,</span> +<span> secondary <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span> +<span> n <span class="op">=</span> <span class="fl">10</span>,</span> +<span> LOD <span class="op">=</span> <span class="fl">0.1</span>,</span> +<span> reps <span class="op">=</span> <span class="fl">2</span>,</span> +<span> digits <span class="op">=</span> <span class="fl">1</span>,</span> +<span> seed <span class="op">=</span> <span class="cn">NA</span></span> +<span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> @@ -108,29 +114,46 @@ may depend on the predicted value and is specified as a standard deviation.</p> <dl><dt>prediction</dt> <dd><p>A prediction from a kinetic model as produced by <code><a href="mkinpredict.html">mkinpredict</a></code>.</p></dd> + + <dt>sdfunc</dt> <dd><p>A function taking the predicted value as its only argument and returning a standard deviation that should be used for generating the random error terms for this value.</p></dd> + + <dt>secondary</dt> <dd><p>The names of state variables that should have an initial value of zero</p></dd> + + <dt>n</dt> <dd><p>The number of datasets to be generated.</p></dd> + + <dt>LOD</dt> <dd><p>The limit of detection (LOD). Values that are below the LOD after adding the random error will be set to NA.</p></dd> + + <dt>reps</dt> <dd><p>The number of replicates to be generated within the datasets.</p></dd> + + <dt>digits</dt> <dd><p>The number of digits to which the values will be rounded.</p></dd> + + <dt>seed</dt> <dd><p>The seed used for the generation of random numbers. If NA, the seed is not set.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>A list of datasets compatible with <code><a href="mmkin.html">mmkin</a></code>, i.e. the + + +<p>A list of datasets compatible with <code><a href="mmkin.html">mmkin</a></code>, i.e. the components of the list are datasets compatible with <code><a href="mkinfit.html">mkinfit</a></code>.</p> </div> <div id="references"> @@ -147,53 +170,53 @@ https://jrwb.de/posters/piacenza_2015.pdf</p> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"><span class="co"># The kinetic model</span></span> -<span class="r-in"><span class="va">m_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M1"</span><span class="op">)</span>,</span> -<span class="r-in"> M1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># The kinetic model</span></span></span> +<span class="r-in"><span><span class="va">m_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># Generate a prediction for a specific set of parameters</span></span> -<span class="r-in"><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># This is the prediction used for the "Type 2 datasets" on the Piacenza poster</span></span> -<span class="r-in"><span class="co"># from 2015</span></span> -<span class="r-in"><span class="va">d_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO_SFO</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.1</span>, f_parent_to_M1 <span class="op">=</span> <span class="fl">0.5</span>,</span> -<span class="r-in"> k_M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">/</span><span class="fl">1000</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="va">sampling_times</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># Add an error term with a constant (independent of the value) standard deviation</span></span> -<span class="r-in"><span class="co"># of 10, and generate three datasets</span></span> -<span class="r-in"><span class="va">d_SFO_SFO_err</span> <span class="op"><-</span> <span class="fu">add_err</span><span class="op">(</span><span class="va">d_SFO_SFO</span>, <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fl">10</span>, n <span class="op">=</span> <span class="fl">3</span>, seed <span class="op">=</span> <span class="fl">123456789</span> <span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># Name the datasets for nicer plotting</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">d_SFO_SFO_err</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># Name the model in the list of models (with only one member in this case) for</span></span> -<span class="r-in"><span class="co"># nicer plotting later on. Be quiet and use only one core not to offend CRAN</span></span> -<span class="r-in"><span class="co"># checks</span></span> -<span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"><span class="va">f_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"SFO-SFO"</span> <span class="op">=</span> <span class="va">m_SFO_SFO</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="va">d_SFO_SFO_err</span>, cores <span class="op">=</span> <span class="fl">1</span>,</span> -<span class="r-in"> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_SFO_SFO</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Generate a prediction for a specific set of parameters</span></span></span> +<span class="r-in"><span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># This is the prediction used for the "Type 2 datasets" on the Piacenza poster</span></span></span> +<span class="r-in"><span><span class="co"># from 2015</span></span></span> +<span class="r-in"><span><span class="va">d_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO_SFO</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.1</span>, f_parent_to_M1 <span class="op">=</span> <span class="fl">0.5</span>,</span></span> +<span class="r-in"><span> k_M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">/</span><span class="fl">1000</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="va">sampling_times</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Add an error term with a constant (independent of the value) standard deviation</span></span></span> +<span class="r-in"><span><span class="co"># of 10, and generate three datasets</span></span></span> +<span class="r-in"><span><span class="va">d_SFO_SFO_err</span> <span class="op"><-</span> <span class="fu">add_err</span><span class="op">(</span><span class="va">d_SFO_SFO</span>, <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fl">10</span>, n <span class="op">=</span> <span class="fl">3</span>, seed <span class="op">=</span> <span class="fl">123456789</span> <span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Name the datasets for nicer plotting</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">d_SFO_SFO_err</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Name the model in the list of models (with only one member in this case) for</span></span></span> +<span class="r-in"><span><span class="co"># nicer plotting later on. Be quiet and use only one core not to offend CRAN</span></span></span> +<span class="r-in"><span><span class="co"># checks</span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="va">f_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"SFO-SFO"</span> <span class="op">=</span> <span class="va">m_SFO_SFO</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="va">d_SFO_SFO_err</span>, cores <span class="op">=</span> <span class="fl">1</span>,</span></span> +<span class="r-in"><span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_SFO_SFO</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="add_err-1.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># We would like to inspect the fit for dataset 3 more closely</span></span> -<span class="r-in"><span class="co"># Using double brackets makes the returned object an mkinfit object</span></span> -<span class="r-in"><span class="co"># instead of a list of mkinfit objects, so plot.mkinfit is used</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_SFO_SFO</span><span class="op">[[</span><span class="fl">3</span><span class="op">]</span><span class="op">]</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># We would like to inspect the fit for dataset 3 more closely</span></span></span> +<span class="r-in"><span><span class="co"># Using double brackets makes the returned object an mkinfit object</span></span></span> +<span class="r-in"><span><span class="co"># instead of a list of mkinfit objects, so plot.mkinfit is used</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_SFO_SFO</span><span class="op">[[</span><span class="fl">3</span><span class="op">]</span><span class="op">]</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="add_err-2.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># If we use single brackets, we should give two indices (model and dataset),</span></span> -<span class="r-in"><span class="co"># and plot.mmkin is used</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_SFO_SFO</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">]</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># If we use single brackets, we should give two indices (model and dataset),</span></span></span> +<span class="r-in"><span><span class="co"># and plot.mmkin is used</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_SFO_SFO</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">3</span><span class="op">]</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="add_err-3.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="co"># }</span></span> -<span class="r-in"></span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -208,7 +231,7 @@ https://jrwb.de/posters/piacenza_2015.pdf</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/anova.saem.mmkin.html b/docs/reference/anova.saem.mmkin.html new file mode 100644 index 00000000..02be017b --- /dev/null +++ b/docs/reference/anova.saem.mmkin.html @@ -0,0 +1,168 @@ +<!DOCTYPE html> +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Anova method for saem.mmkin objects — anova.saem.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Anova method for saem.mmkin objects — anova.saem.mmkin"><meta property="og:description" content="Generate an anova object. The method to calculate the BIC is that from the +saemix package. As in other prominent anova methods, models are sorted by +number of parameters, and the tests (if requested) are always relative to +the model on the previous line."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> +<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> +<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + + + <div class="container template-reference-topic"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <div class="container"> + <div class="navbar-header"> + <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> + <span class="sr-only">Toggle navigation</span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + </button> + <span class="navbar-brand"> + <a class="navbar-link" href="../index.html">mkin</a> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + </span> + </div> + + <div id="navbar" class="navbar-collapse collapse"> + <ul class="nav navbar-nav"><li> + <a href="../reference/index.html">Functions and data</a> +</li> +<li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> + Articles + + <span class="caret"></span> + </a> + <ul class="dropdown-menu" role="menu"><li> + <a href="../articles/mkin.html">Introduction to mkin</a> + </li> + <li> + <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> + </li> + <li> + <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> + </li> + <li> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + </li> + <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> + <li> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> +<li> + <a href="../news/index.html">News</a> +</li> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> + <span class="fab fa-github fa-lg"></span> + + </a> +</li> + </ul></div><!--/.nav-collapse --> + </div><!--/.container --> +</div><!--/.navbar --> + + + + </header><div class="row"> + <div class="col-md-9 contents"> + <div class="page-header"> + <h1>Anova method for saem.mmkin objects</h1> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/anova.saem.mmkin.R" class="external-link"><code>R/anova.saem.mmkin.R</code></a></small> + <div class="hidden name"><code>anova.saem.mmkin.Rd</code></div> + </div> + + <div class="ref-description"> + <p>Generate an anova object. The method to calculate the BIC is that from the +saemix package. As in other prominent anova methods, models are sorted by +number of parameters, and the tests (if requested) are always relative to +the model on the previous line.</p> + </div> + + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for saem.mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span></span> +<span> <span class="va">object</span>,</span> +<span> <span class="va">...</span>,</span> +<span> method <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"is"</span>, <span class="st">"lin"</span>, <span class="st">"gq"</span><span class="op">)</span>,</span> +<span> test <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> model.names <span class="op">=</span> <span class="cn">NULL</span></span> +<span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>object</dt> +<dd><p>An <a href="saem.html">saem.mmkin</a> object</p></dd> + + +<dt>...</dt> +<dd><p>further such objects</p></dd> + + +<dt>method</dt> +<dd><p>Method for likelihood calculation: "is" (importance sampling), +"lin" (linear approximation), or "gq" (Gaussian quadrature). Passed +to <a href="https://rdrr.io/pkg/saemix/man/logLik.html" class="external-link">saemix::logLik.SaemixObject</a></p></dd> + + +<dt>test</dt> +<dd><p>Should a likelihood ratio test be performed? If TRUE, +the alternative models are tested against the first model. Should +only be done for nested models.</p></dd> + + +<dt>model.names</dt> +<dd><p>Optional character vector of model names</p></dd> + +</dl></div> + <div id="value"> + <h2>Value</h2> + + +<p>an "anova" data frame; the traditional (S3) result of anova()</p> + </div> + + </div> + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> +</div> + + + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +</div> + + </footer></div> + + + + + + + </body></html> + diff --git a/docs/reference/aw.html b/docs/reference/aw.html index 1694d5f7..c1e1b4ed 100644 --- a/docs/reference/aw.html +++ b/docs/reference/aw.html @@ -19,7 +19,7 @@ by Burnham and Anderson (2004)."><!-- mathjax --><script src="https://cdnjs.clou </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -46,19 +46,25 @@ by Burnham and Anderson (2004)."><!-- mathjax --><script src="https://cdnjs.clou <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -91,13 +97,19 @@ by Burnham and Anderson (2004).</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">aw</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span> - -<span class="co"># S3 method for mkinfit</span> -<span class="fu">aw</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span> - -<span class="co"># S3 method for mmkin</span> -<span class="fu">aw</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">aw</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mkinfit</span></span> +<span><span class="fu">aw</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mmkin</span></span> +<span><span class="fu">aw</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mixed.mmkin</span></span> +<span><span class="fu">aw</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for multistart</span></span> +<span><span class="fu">aw</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> @@ -108,9 +120,12 @@ by Burnham and Anderson (2004).</p> or an mkinfit object. In the latter case, further mkinfit objects fitted to the same data should be specified as dots arguments.</p></dd> + + <dt>...</dt> <dd><p>Not used in the method for <a href="mmkin.html">mmkin</a> column objects, further <a href="mkinfit.html">mkinfit</a> objects in the method for mkinfit objects.</p></dd> + </dl></div> <div id="references"> <h2>References</h2> @@ -121,22 +136,22 @@ Inference: Understanding AIC and BIC in Model Selection. <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"><span class="va">f_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="va">f_dfop</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="va">aw_sfo_dfop</span> <span class="op"><-</span> <span class="fu">aw</span><span class="op">(</span><span class="va">f_sfo</span>, <span class="va">f_dfop</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">aw_sfo_dfop</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="va">f_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">f_dfop</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">aw_sfo_dfop</span> <span class="op"><-</span> <span class="fu">aw</span><span class="op">(</span><span class="va">f_sfo</span>, <span class="va">f_dfop</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">aw_sfo_dfop</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> [1] 1</span> -<span class="r-in"><span class="va">aw_sfo_dfop</span> <span class="co"># SFO gets more weight as it has less parameters and a similar fit</span></span> +<span class="r-in"><span><span class="va">aw_sfo_dfop</span> <span class="co"># SFO gets more weight as it has less parameters and a similar fit</span></span></span> <span class="r-out co"><span class="r-pr">#></span> [1] 0.5970258 0.4029742</span> -<span class="r-in"><span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS D"</span> <span class="op">=</span> <span class="va">FOCUS_2006_D</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="fu">aw</span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span> +<span class="r-in"><span><span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS D"</span> <span class="op">=</span> <span class="va">FOCUS_2006_D</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu">aw</span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> [1] 0.4808722 0.1945539 0.3245740</span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="fu">aw</span><span class="op">(</span><span class="va">f</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="fu">aw</span><span class="op">(</span><span class="va">f</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> [1] 1</span> -<span class="r-in"><span class="fu">aw</span><span class="op">(</span><span class="va">f</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"DFOP"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu">aw</span><span class="op">(</span><span class="va">f</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"DFOP"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> [1] 0.5970258 0.4029742</span> -<span class="r-in"><span class="co"># }</span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> </code></pre></div> </div> </div> @@ -151,7 +166,7 @@ Inference: Understanding AIC and BIC in Model Selection. </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/confint.mkinfit.html b/docs/reference/confint.mkinfit.html index 5469f8f8..8d7c272f 100644 --- a/docs/reference/confint.mkinfit.html +++ b/docs/reference/confint.mkinfit.html @@ -24,7 +24,7 @@ method of Venzon and Moolgavkar (1988)."><!-- mathjax --><script src="https://cd </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -51,19 +51,25 @@ method of Venzon and Moolgavkar (1988)."><!-- mathjax --><script src="https://cd <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -101,66 +107,91 @@ method of Venzon and Moolgavkar (1988).</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># S3 method for mkinfit</span> -<span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span> - <span class="va">object</span>, - <span class="va">parm</span>, - level <span class="op">=</span> <span class="fl">0.95</span>, - alpha <span class="op">=</span> <span class="fl">1</span> <span class="op">-</span> <span class="va">level</span>, - <span class="va">cutoff</span>, - method <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"quadratic"</span>, <span class="st">"profile"</span><span class="op">)</span>, - transformed <span class="op">=</span> <span class="cn">TRUE</span>, - backtransform <span class="op">=</span> <span class="cn">TRUE</span>, - cores <span class="op">=</span> <span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span>, - rel_tol <span class="op">=</span> <span class="fl">0.01</span>, - quiet <span class="op">=</span> <span class="cn">FALSE</span>, - <span class="va">...</span> -<span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span></span> +<span> <span class="va">object</span>,</span> +<span> <span class="va">parm</span>,</span> +<span> level <span class="op">=</span> <span class="fl">0.95</span>,</span> +<span> alpha <span class="op">=</span> <span class="fl">1</span> <span class="op">-</span> <span class="va">level</span>,</span> +<span> <span class="va">cutoff</span>,</span> +<span> method <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"quadratic"</span>, <span class="st">"profile"</span><span class="op">)</span>,</span> +<span> transformed <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> backtransform <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> cores <span class="op">=</span> <span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span>,</span> +<span> rel_tol <span class="op">=</span> <span class="fl">0.01</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>object</dt> <dd><p>An <code><a href="mkinfit.html">mkinfit</a></code> object</p></dd> + + <dt>parm</dt> <dd><p>A vector of names of the parameters which are to be given confidence intervals. If missing, all parameters are considered.</p></dd> + + <dt>level</dt> <dd><p>The confidence level required</p></dd> + + <dt>alpha</dt> <dd><p>The allowed error probability, overrides 'level' if specified.</p></dd> + + <dt>cutoff</dt> <dd><p>Possibility to specify an alternative cutoff for the difference in the log-likelihoods at the confidence boundary. Specifying an explicit cutoff value overrides arguments 'level' and 'alpha'</p></dd> + + <dt>method</dt> <dd><p>The 'quadratic' method approximates the likelihood function at the optimised parameters using the second term of the Taylor expansion, using a second derivative (hessian) contained in the object. The 'profile' method searches the parameter space for the cutoff of the confidence intervals by means of a likelihood ratio test.</p></dd> + + <dt>transformed</dt> <dd><p>If the quadratic approximation is used, should it be applied to the likelihood based on the transformed parameters?</p></dd> + + <dt>backtransform</dt> <dd><p>If we approximate the likelihood in terms of the transformed parameters, should we backtransform the parameters with their confidence intervals?</p></dd> + + <dt>cores</dt> <dd><p>The number of cores to be used for multicore processing. On Windows machines, cores > 1 is currently not supported.</p></dd> + + <dt>rel_tol</dt> <dd><p>If the method is 'profile', what should be the accuracy of the lower and upper bounds, relative to the estimate obtained from the quadratic method?</p></dd> + + <dt>quiet</dt> <dd><p>Should we suppress the message "Profiling the likelihood"</p></dd> + + <dt>...</dt> <dd><p>Not used</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>A matrix with columns giving lower and upper confidence limits for + + +<p>A matrix with columns giving lower and upper confidence limits for each parameter.</p> </div> <div id="references"> @@ -175,178 +206,178 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f</span>, method <span class="op">=</span> <span class="st">"quadratic"</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f</span>, method <span class="op">=</span> <span class="st">"quadratic"</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 71.8242430 93.1600766</span> <span class="r-out co"><span class="r-pr">#></span> k_parent 0.2109541 0.4440528</span> <span class="r-out co"><span class="r-pr">#></span> sigma 1.9778868 7.3681380</span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f</span>, method <span class="op">=</span> <span class="st">"profile"</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f</span>, method <span class="op">=</span> <span class="st">"profile"</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Profiling the likelihood</span> <span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 73.0641834 92.1392181</span> <span class="r-out co"><span class="r-pr">#></span> k_parent 0.2170293 0.4235348</span> <span class="r-out co"><span class="r-pr">#></span> sigma 3.1307772 8.0628314</span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># Set the number of cores for the profiling method for further examples</span></span> -<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/identical.html" class="external-link">identical</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.getenv.html" class="external-link">Sys.getenv</a></span><span class="op">(</span><span class="st">"NOT_CRAN"</span><span class="op">)</span>, <span class="st">"true"</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span> -<span class="r-in"> <span class="va">n_cores</span> <span class="op"><-</span> <span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span> <span class="op">-</span> <span class="fl">1</span></span> -<span class="r-in"><span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span> -<span class="r-in"> <span class="va">n_cores</span> <span class="op"><-</span> <span class="fl">1</span></span> -<span class="r-in"><span class="op">}</span></span> -<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.getenv.html" class="external-link">Sys.getenv</a></span><span class="op">(</span><span class="st">"TRAVIS"</span><span class="op">)</span> <span class="op">!=</span> <span class="st">""</span><span class="op">)</span> <span class="va">n_cores</span> <span class="op">=</span> <span class="fl">1</span></span> -<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="va">n_cores</span> <span class="op">=</span> <span class="fl">1</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> use_of_ff <span class="op">=</span> <span class="st">"min"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="va">SFO_SFO.ff</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> use_of_ff <span class="op">=</span> <span class="st">"max"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="va">f_d_1</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">ci_profile</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_1</span>, method <span class="op">=</span> <span class="st">"profile"</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Set the number of cores for the profiling method for further examples</span></span></span> +<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/identical.html" class="external-link">identical</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.getenv.html" class="external-link">Sys.getenv</a></span><span class="op">(</span><span class="st">"NOT_CRAN"</span><span class="op">)</span>, <span class="st">"true"</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span> +<span class="r-in"><span> <span class="va">n_cores</span> <span class="op"><-</span> <span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span> <span class="op">-</span> <span class="fl">1</span></span></span> +<span class="r-in"><span><span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span></span> +<span class="r-in"><span> <span class="va">n_cores</span> <span class="op"><-</span> <span class="fl">1</span></span></span> +<span class="r-in"><span><span class="op">}</span></span></span> +<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.getenv.html" class="external-link">Sys.getenv</a></span><span class="op">(</span><span class="st">"TRAVIS"</span><span class="op">)</span> <span class="op">!=</span> <span class="st">""</span><span class="op">)</span> <span class="va">n_cores</span> <span class="op">=</span> <span class="fl">1</span></span></span> +<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="va">n_cores</span> <span class="op">=</span> <span class="fl">1</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> use_of_ff <span class="op">=</span> <span class="st">"min"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO.ff</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> use_of_ff <span class="op">=</span> <span class="st">"max"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">f_d_1</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">ci_profile</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_1</span>, method <span class="op">=</span> <span class="st">"profile"</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> user system elapsed </span> -<span class="r-out co"><span class="r-pr">#></span> 3.409 0.000 3.411 </span> -<span class="r-in"><span class="co"># Using more cores does not save much time here, as parent_0 takes up most of the time</span></span> -<span class="r-in"><span class="co"># If we additionally exclude parent_0 (the confidence of which is often of</span></span> -<span class="r-in"><span class="co"># minor interest), we get a nice performance improvement if we use at least 4 cores</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">ci_profile_no_parent_0</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_1</span>, method <span class="op">=</span> <span class="st">"profile"</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"k_parent_sink"</span>, <span class="st">"k_parent_m1"</span>, <span class="st">"k_m1_sink"</span>, <span class="st">"sigma"</span><span class="op">)</span>, cores <span class="op">=</span> <span class="va">n_cores</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-out co"><span class="r-pr">#></span> 3.931 0.000 3.932 </span> +<span class="r-in"><span><span class="co"># Using more cores does not save much time here, as parent_0 takes up most of the time</span></span></span> +<span class="r-in"><span><span class="co"># If we additionally exclude parent_0 (the confidence of which is often of</span></span></span> +<span class="r-in"><span><span class="co"># minor interest), we get a nice performance improvement if we use at least 4 cores</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">ci_profile_no_parent_0</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_1</span>, method <span class="op">=</span> <span class="st">"profile"</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"k_parent_sink"</span>, <span class="st">"k_parent_m1"</span>, <span class="st">"k_m1_sink"</span>, <span class="st">"sigma"</span><span class="op">)</span>, cores <span class="op">=</span> <span class="va">n_cores</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Profiling the likelihood</span> <span class="r-out co"><span class="r-pr">#></span> user system elapsed </span> -<span class="r-out co"><span class="r-pr">#></span> 1.317 0.152 0.847 </span> -<span class="r-in"><span class="va">ci_profile</span></span> +<span class="r-out co"><span class="r-pr">#></span> 2.219 0.000 2.219 </span> +<span class="r-in"><span><span class="va">ci_profile</span></span></span> <span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 96.456003640 1.027703e+02</span> <span class="r-out co"><span class="r-pr">#></span> k_parent_sink 0.040762501 5.549764e-02</span> <span class="r-out co"><span class="r-pr">#></span> k_parent_m1 0.046786482 5.500879e-02</span> <span class="r-out co"><span class="r-pr">#></span> k_m1_sink 0.003892605 6.702778e-03</span> <span class="r-out co"><span class="r-pr">#></span> sigma 2.535612399 3.985263e+00</span> -<span class="r-in"><span class="va">ci_quadratic_transformed</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_1</span>, method <span class="op">=</span> <span class="st">"quadratic"</span><span class="op">)</span></span> -<span class="r-in"><span class="va">ci_quadratic_transformed</span></span> +<span class="r-in"><span><span class="va">ci_quadratic_transformed</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_1</span>, method <span class="op">=</span> <span class="st">"quadratic"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">ci_quadratic_transformed</span></span></span> <span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 96.403841640 1.027931e+02</span> <span class="r-out co"><span class="r-pr">#></span> k_parent_sink 0.041033378 5.596269e-02</span> <span class="r-out co"><span class="r-pr">#></span> k_parent_m1 0.046777902 5.511931e-02</span> <span class="r-out co"><span class="r-pr">#></span> k_m1_sink 0.004012217 6.897547e-03</span> <span class="r-out co"><span class="r-pr">#></span> sigma 2.396089689 3.854918e+00</span> -<span class="r-in"><span class="va">ci_quadratic_untransformed</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_1</span>, method <span class="op">=</span> <span class="st">"quadratic"</span>, transformed <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span> -<span class="r-in"><span class="va">ci_quadratic_untransformed</span></span> +<span class="r-in"><span><span class="va">ci_quadratic_untransformed</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_1</span>, method <span class="op">=</span> <span class="st">"quadratic"</span>, transformed <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">ci_quadratic_untransformed</span></span></span> <span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 96.403841645 102.79312449</span> <span class="r-out co"><span class="r-pr">#></span> k_parent_sink 0.040485331 0.05535491</span> <span class="r-out co"><span class="r-pr">#></span> k_parent_m1 0.046611582 0.05494364</span> <span class="r-out co"><span class="r-pr">#></span> k_m1_sink 0.003835483 0.00668582</span> <span class="r-out co"><span class="r-pr">#></span> sigma 2.396089689 3.85491806</span> -<span class="r-in"><span class="co"># Against the expectation based on Bates and Watts (1988), the confidence</span></span> -<span class="r-in"><span class="co"># intervals based on the internal parameter transformation are less</span></span> -<span class="r-in"><span class="co"># congruent with the likelihood based intervals. Note the superiority of the</span></span> -<span class="r-in"><span class="co"># interval based on the untransformed fit for k_m1_sink</span></span> -<span class="r-in"><span class="va">rel_diffs_transformed</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">abs</a></span><span class="op">(</span><span class="op">(</span><span class="va">ci_quadratic_transformed</span> <span class="op">-</span> <span class="va">ci_profile</span><span class="op">)</span><span class="op">/</span><span class="va">ci_profile</span><span class="op">)</span></span> -<span class="r-in"><span class="va">rel_diffs_untransformed</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">abs</a></span><span class="op">(</span><span class="op">(</span><span class="va">ci_quadratic_untransformed</span> <span class="op">-</span> <span class="va">ci_profile</span><span class="op">)</span><span class="op">/</span><span class="va">ci_profile</span><span class="op">)</span></span> -<span class="r-in"><span class="va">rel_diffs_transformed</span> <span class="op"><</span> <span class="va">rel_diffs_untransformed</span></span> +<span class="r-in"><span><span class="co"># Against the expectation based on Bates and Watts (1988), the confidence</span></span></span> +<span class="r-in"><span><span class="co"># intervals based on the internal parameter transformation are less</span></span></span> +<span class="r-in"><span><span class="co"># congruent with the likelihood based intervals. Note the superiority of the</span></span></span> +<span class="r-in"><span><span class="co"># interval based on the untransformed fit for k_m1_sink</span></span></span> +<span class="r-in"><span><span class="va">rel_diffs_transformed</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">abs</a></span><span class="op">(</span><span class="op">(</span><span class="va">ci_quadratic_transformed</span> <span class="op">-</span> <span class="va">ci_profile</span><span class="op">)</span><span class="op">/</span><span class="va">ci_profile</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">rel_diffs_untransformed</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">abs</a></span><span class="op">(</span><span class="op">(</span><span class="va">ci_quadratic_untransformed</span> <span class="op">-</span> <span class="va">ci_profile</span><span class="op">)</span><span class="op">/</span><span class="va">ci_profile</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">rel_diffs_transformed</span> <span class="op"><</span> <span class="va">rel_diffs_untransformed</span></span></span> <span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 FALSE FALSE</span> <span class="r-out co"><span class="r-pr">#></span> k_parent_sink TRUE FALSE</span> <span class="r-out co"><span class="r-pr">#></span> k_parent_m1 TRUE FALSE</span> <span class="r-out co"><span class="r-pr">#></span> k_m1_sink FALSE FALSE</span> <span class="r-out co"><span class="r-pr">#></span> sigma FALSE FALSE</span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">signif</a></span><span class="op">(</span><span class="va">rel_diffs_transformed</span>, <span class="fl">3</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">signif</a></span><span class="op">(</span><span class="va">rel_diffs_transformed</span>, <span class="fl">3</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 0.000541 0.000222</span> <span class="r-out co"><span class="r-pr">#></span> k_parent_sink 0.006650 0.008380</span> <span class="r-out co"><span class="r-pr">#></span> k_parent_m1 0.000183 0.002010</span> <span class="r-out co"><span class="r-pr">#></span> k_m1_sink 0.030700 0.029100</span> <span class="r-out co"><span class="r-pr">#></span> sigma 0.055000 0.032700</span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">signif</a></span><span class="op">(</span><span class="va">rel_diffs_untransformed</span>, <span class="fl">3</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">signif</a></span><span class="op">(</span><span class="va">rel_diffs_untransformed</span>, <span class="fl">3</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 0.000541 0.000222</span> <span class="r-out co"><span class="r-pr">#></span> k_parent_sink 0.006800 0.002570</span> <span class="r-out co"><span class="r-pr">#></span> k_parent_m1 0.003740 0.001180</span> <span class="r-out co"><span class="r-pr">#></span> k_m1_sink 0.014700 0.002530</span> <span class="r-out co"><span class="r-pr">#></span> sigma 0.055000 0.032700</span> -<span class="r-in"></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># Investigate a case with formation fractions</span></span> -<span class="r-in"><span class="va">f_d_2</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO.ff</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="va">ci_profile_ff</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_2</span>, method <span class="op">=</span> <span class="st">"profile"</span>, cores <span class="op">=</span> <span class="va">n_cores</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Investigate a case with formation fractions</span></span></span> +<span class="r-in"><span><span class="va">f_d_2</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO.ff</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">ci_profile_ff</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_2</span>, method <span class="op">=</span> <span class="st">"profile"</span>, cores <span class="op">=</span> <span class="va">n_cores</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Profiling the likelihood</span> -<span class="r-in"><span class="va">ci_profile_ff</span></span> +<span class="r-in"><span><span class="va">ci_profile_ff</span></span></span> <span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 96.456003640 1.027703e+02</span> <span class="r-out co"><span class="r-pr">#></span> k_parent 0.090911032 1.071578e-01</span> <span class="r-out co"><span class="r-pr">#></span> k_m1 0.003892606 6.702775e-03</span> <span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.471328495 5.611550e-01</span> <span class="r-out co"><span class="r-pr">#></span> sigma 2.535612399 3.985263e+00</span> -<span class="r-in"><span class="va">ci_quadratic_transformed_ff</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_2</span>, method <span class="op">=</span> <span class="st">"quadratic"</span><span class="op">)</span></span> -<span class="r-in"><span class="va">ci_quadratic_transformed_ff</span></span> +<span class="r-in"><span><span class="va">ci_quadratic_transformed_ff</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_2</span>, method <span class="op">=</span> <span class="st">"quadratic"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">ci_quadratic_transformed_ff</span></span></span> <span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 96.403833565 102.79311648</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 96.403833578 102.79311649</span> <span class="r-out co"><span class="r-pr">#></span> k_parent 0.090823771 0.10725430</span> <span class="r-out co"><span class="r-pr">#></span> k_m1 0.004012219 0.00689755</span> <span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.469118824 0.55959615</span> <span class="r-out co"><span class="r-pr">#></span> sigma 2.396089689 3.85491806</span> -<span class="r-in"><span class="va">ci_quadratic_untransformed_ff</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_2</span>, method <span class="op">=</span> <span class="st">"quadratic"</span>, transformed <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span> -<span class="r-in"><span class="va">ci_quadratic_untransformed_ff</span></span> +<span class="r-in"><span><span class="va">ci_quadratic_untransformed_ff</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_2</span>, method <span class="op">=</span> <span class="st">"quadratic"</span>, transformed <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">ci_quadratic_untransformed_ff</span></span></span> <span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 96.403833570 1.027931e+02</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 96.403833583 1.027931e+02</span> <span class="r-out co"><span class="r-pr">#></span> k_parent 0.090491913 1.069035e-01</span> <span class="r-out co"><span class="r-pr">#></span> k_m1 0.003835485 6.685823e-03</span> <span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.469113477 5.598387e-01</span> <span class="r-out co"><span class="r-pr">#></span> sigma 2.396089689 3.854918e+00</span> -<span class="r-in"><span class="va">rel_diffs_transformed_ff</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">abs</a></span><span class="op">(</span><span class="op">(</span><span class="va">ci_quadratic_transformed_ff</span> <span class="op">-</span> <span class="va">ci_profile_ff</span><span class="op">)</span><span class="op">/</span><span class="va">ci_profile_ff</span><span class="op">)</span></span> -<span class="r-in"><span class="va">rel_diffs_untransformed_ff</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">abs</a></span><span class="op">(</span><span class="op">(</span><span class="va">ci_quadratic_untransformed_ff</span> <span class="op">-</span> <span class="va">ci_profile_ff</span><span class="op">)</span><span class="op">/</span><span class="va">ci_profile_ff</span><span class="op">)</span></span> -<span class="r-in"><span class="co"># While the confidence interval for the parent rate constant is closer to</span></span> -<span class="r-in"><span class="co"># the profile based interval when using the internal parameter</span></span> -<span class="r-in"><span class="co"># transformation, the interval for the metabolite rate constant is 'better</span></span> -<span class="r-in"><span class="co"># without internal parameter transformation.</span></span> -<span class="r-in"><span class="va">rel_diffs_transformed_ff</span> <span class="op"><</span> <span class="va">rel_diffs_untransformed_ff</span></span> +<span class="r-in"><span><span class="va">rel_diffs_transformed_ff</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">abs</a></span><span class="op">(</span><span class="op">(</span><span class="va">ci_quadratic_transformed_ff</span> <span class="op">-</span> <span class="va">ci_profile_ff</span><span class="op">)</span><span class="op">/</span><span class="va">ci_profile_ff</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">rel_diffs_untransformed_ff</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">abs</a></span><span class="op">(</span><span class="op">(</span><span class="va">ci_quadratic_untransformed_ff</span> <span class="op">-</span> <span class="va">ci_profile_ff</span><span class="op">)</span><span class="op">/</span><span class="va">ci_profile_ff</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># While the confidence interval for the parent rate constant is closer to</span></span></span> +<span class="r-in"><span><span class="co"># the profile based interval when using the internal parameter</span></span></span> +<span class="r-in"><span><span class="co"># transformation, the interval for the metabolite rate constant is 'better</span></span></span> +<span class="r-in"><span><span class="co"># without internal parameter transformation.</span></span></span> +<span class="r-in"><span><span class="va">rel_diffs_transformed_ff</span> <span class="op"><</span> <span class="va">rel_diffs_untransformed_ff</span></span></span> <span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 FALSE FALSE</span> <span class="r-out co"><span class="r-pr">#></span> k_parent TRUE TRUE</span> <span class="r-out co"><span class="r-pr">#></span> k_m1 FALSE FALSE</span> <span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 TRUE FALSE</span> <span class="r-out co"><span class="r-pr">#></span> sigma TRUE FALSE</span> -<span class="r-in"><span class="va">rel_diffs_transformed_ff</span></span> +<span class="r-in"><span><span class="va">rel_diffs_transformed_ff</span></span></span> <span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 0.0005408691 0.0002217232</span> -<span class="r-out co"><span class="r-pr">#></span> k_parent 0.0009598534 0.0009001862</span> -<span class="r-out co"><span class="r-pr">#></span> k_m1 0.0307283050 0.0290588375</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.0046881765 0.0027780058</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 0.0005408690 0.0002217233</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent 0.0009598532 0.0009001864</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 0.0307283041 0.0290588361</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.0046881769 0.0027780063</span> <span class="r-out co"><span class="r-pr">#></span> sigma 0.0550252516 0.0327066836</span> -<span class="r-in"><span class="va">rel_diffs_untransformed_ff</span></span> +<span class="r-in"><span><span class="va">rel_diffs_untransformed_ff</span></span></span> <span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 0.0005408691 0.0002217231</span> -<span class="r-out co"><span class="r-pr">#></span> k_parent 0.0046102157 0.0023732283</span> -<span class="r-out co"><span class="r-pr">#></span> k_m1 0.0146740682 0.0025291807</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.0046995207 0.0023457708</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 0.0005408689 0.0002217232</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent 0.0046102156 0.0023732281</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 0.0146740690 0.0025291820</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.0046995211 0.0023457712</span> <span class="r-out co"><span class="r-pr">#></span> sigma 0.0550252516 0.0327066836</span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># The profiling for the following fit does not finish in a reasonable time,</span></span> -<span class="r-in"><span class="co"># therefore we use the quadratic approximation</span></span> -<span class="r-in"><span class="va">m_synth_DFOP_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span> -<span class="r-in"> M1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> M2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> use_of_ff <span class="op">=</span> <span class="st">"max"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="va">DFOP_par_c</span> <span class="op"><-</span> <span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span> -<span class="r-in"><span class="va">f_tc_2</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_DFOP_par</span>, <span class="va">DFOP_par_c</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>,</span> -<span class="r-in"> error_model_algorithm <span class="op">=</span> <span class="st">"direct"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_tc_2</span>, method <span class="op">=</span> <span class="st">"quadratic"</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># The profiling for the following fit does not finish in a reasonable time,</span></span></span> +<span class="r-in"><span><span class="co"># therefore we use the quadratic approximation</span></span></span> +<span class="r-in"><span><span class="va">m_synth_DFOP_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> use_of_ff <span class="op">=</span> <span class="st">"max"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">DFOP_par_c</span> <span class="op"><-</span> <span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span></span> +<span class="r-in"><span><span class="va">f_tc_2</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_DFOP_par</span>, <span class="va">DFOP_par_c</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>,</span></span> +<span class="r-in"><span> error_model_algorithm <span class="op">=</span> <span class="st">"direct"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_tc_2</span>, method <span class="op">=</span> <span class="st">"quadratic"</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 94.596210088 106.19935874</span> -<span class="r-out co"><span class="r-pr">#></span> k_M1 0.037605436 0.04490756</span> -<span class="r-out co"><span class="r-pr">#></span> k_M2 0.008568746 0.01087674</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_to_M1 0.021466645 0.62023879</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_to_M2 0.015168549 0.37975352</span> -<span class="r-out co"><span class="r-pr">#></span> k1 0.273897574 0.33388069</span> -<span class="r-out co"><span class="r-pr">#></span> k2 0.018614555 0.02250379</span> -<span class="r-out co"><span class="r-pr">#></span> g 0.671943815 0.73583258</span> -<span class="r-out co"><span class="r-pr">#></span> sigma_low 0.251283808 0.83992121</span> -<span class="r-out co"><span class="r-pr">#></span> rsd_high 0.040411010 0.07662004</span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_tc_2</span>, <span class="st">"parent_0"</span>, method <span class="op">=</span> <span class="st">"quadratic"</span><span class="op">)</span></span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 94.596039609 106.19954892</span> +<span class="r-out co"><span class="r-pr">#></span> k_M1 0.037605368 0.04490762</span> +<span class="r-out co"><span class="r-pr">#></span> k_M2 0.008568731 0.01087676</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_M1 0.021462489 0.62023882</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_M2 0.015165617 0.37975348</span> +<span class="r-out co"><span class="r-pr">#></span> k1 0.273897348 0.33388101</span> +<span class="r-out co"><span class="r-pr">#></span> k2 0.018614554 0.02250378</span> +<span class="r-out co"><span class="r-pr">#></span> g 0.671943411 0.73583305</span> +<span class="r-out co"><span class="r-pr">#></span> sigma_low 0.251283495 0.83992077</span> +<span class="r-out co"><span class="r-pr">#></span> rsd_high 0.040411024 0.07662008</span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_tc_2</span>, <span class="st">"parent_0"</span>, method <span class="op">=</span> <span class="st">"quadratic"</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 94.59621 106.1994</span> -<span class="r-in"><span class="co"># }</span></span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 94.59604 106.1995</span> +<span class="r-in"><span><span class="co"># }</span></span></span> </code></pre></div> </div> </div> @@ -361,7 +392,7 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/create_deg_func.html b/docs/reference/create_deg_func.html index b85acb7a..b93f1c4a 100644 --- a/docs/reference/create_deg_func.html +++ b/docs/reference/create_deg_func.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -44,19 +44,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -87,59 +93,64 @@ </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">create_deg_func</span><span class="op">(</span><span class="va">spec</span>, use_of_ff <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"min"</span>, <span class="st">"max"</span><span class="op">)</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">create_deg_func</span><span class="op">(</span><span class="va">spec</span>, use_of_ff <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"min"</span>, <span class="st">"max"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>spec</dt> <dd><p>List of model specifications as contained in mkinmod objects</p></dd> + + <dt>use_of_ff</dt> <dd><p>Minimum or maximum use of formation fractions</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>Degradation function to be attached to mkinmod objects</p> + + +<p>Degradation function to be attached to mkinmod objects</p> </div> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span> -<span class="r-in"> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span> -<span class="r-in"> m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"><span class="va">FOCUS_D</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span> <span class="co"># to avoid warnings</span></span> -<span class="r-in"><span class="va">fit_1</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, solution_type <span class="op">=</span> <span class="st">"analytical"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"><span class="va">fit_2</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="va"><a href="http://rbenchmark.googlecode.com" class="external-link">rbenchmark</a></span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu">benchmark</span><span class="op">(</span></span> -<span class="r-in"> analytical <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, solution_type <span class="op">=</span> <span class="st">"analytical"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span> -<span class="r-in"> deSolve <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span> -<span class="r-in"> replications <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span> +<span class="r-in"><span><span class="va">FOCUS_D</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span> <span class="co"># to avoid warnings</span></span></span> +<span class="r-in"><span><span class="va">fit_1</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, solution_type <span class="op">=</span> <span class="st">"analytical"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="va">fit_2</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="va"><a href="http://rbenchmark.googlecode.com" class="external-link">rbenchmark</a></span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/pkg/rbenchmark/man/benchmark.html" class="external-link">benchmark</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> analytical <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, solution_type <span class="op">=</span> <span class="st">"analytical"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> deSolve <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> replications <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Loading required package: rbenchmark</span> <span class="r-out co"><span class="r-pr">#></span> test replications elapsed relative user.self sys.self user.child</span> -<span class="r-out co"><span class="r-pr">#></span> 1 analytical 2 0.401 1.00 0.401 0.000 0</span> -<span class="r-out co"><span class="r-pr">#></span> 2 deSolve 2 1.211 3.02 1.210 0.002 0</span> +<span class="r-out co"><span class="r-pr">#></span> 1 analytical 2 0.445 1.000 0.444 0 0</span> +<span class="r-out co"><span class="r-pr">#></span> 2 deSolve 2 0.693 1.557 0.692 0 0</span> <span class="r-out co"><span class="r-pr">#></span> sys.child</span> <span class="r-out co"><span class="r-pr">#></span> 1 0</span> <span class="r-out co"><span class="r-pr">#></span> 2 0</span> -<span class="r-in"> <span class="va">DFOP_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span> -<span class="r-in"> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span> -<span class="r-in"> m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="va">DFOP_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"> <span class="fu">benchmark</span><span class="op">(</span></span> -<span class="r-in"> analytical <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">DFOP_SFO</span>, <span class="va">FOCUS_D</span>, solution_type <span class="op">=</span> <span class="st">"analytical"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span> -<span class="r-in"> deSolve <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">DFOP_SFO</span>, <span class="va">FOCUS_D</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span> -<span class="r-in"> replications <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/pkg/rbenchmark/man/benchmark.html" class="external-link">benchmark</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> analytical <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">DFOP_SFO</span>, <span class="va">FOCUS_D</span>, solution_type <span class="op">=</span> <span class="st">"analytical"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> deSolve <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">DFOP_SFO</span>, <span class="va">FOCUS_D</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> replications <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> test replications elapsed relative user.self sys.self user.child</span> -<span class="r-out co"><span class="r-pr">#></span> 1 analytical 2 0.84 1.000 0.839 0.001 0</span> -<span class="r-out co"><span class="r-pr">#></span> 2 deSolve 2 3.19 3.798 3.188 0.001 0</span> +<span class="r-out co"><span class="r-pr">#></span> 1 analytical 2 0.871 1.000 0.871 0 0</span> +<span class="r-out co"><span class="r-pr">#></span> 2 deSolve 2 1.519 1.744 1.519 0 0</span> <span class="r-out co"><span class="r-pr">#></span> sys.child</span> <span class="r-out co"><span class="r-pr">#></span> 1 0</span> <span class="r-out co"><span class="r-pr">#></span> 2 0</span> -<span class="r-in"><span class="co"># }</span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> </code></pre></div> </div> </div> @@ -154,7 +165,7 @@ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/dimethenamid_2018-1.png b/docs/reference/dimethenamid_2018-1.png Binary files differindex 6e5d357d..c8b05bf5 100644 --- a/docs/reference/dimethenamid_2018-1.png +++ b/docs/reference/dimethenamid_2018-1.png diff --git a/docs/reference/dimethenamid_2018.html b/docs/reference/dimethenamid_2018.html index 431e5c34..6d8c0157 100644 --- a/docs/reference/dimethenamid_2018.html +++ b/docs/reference/dimethenamid_2018.html @@ -22,7 +22,7 @@ constrained by data protection regulations."><!-- mathjax --><script src="https: </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -49,19 +49,25 @@ constrained by data protection regulations."><!-- mathjax --><script src="https: <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -97,7 +103,7 @@ constrained by data protection regulations.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="va">dimethenamid_2018</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">dimethenamid_2018</span></span></code></pre></div> </div> <div id="format"> @@ -120,7 +126,7 @@ specific pieces of information in the comments.</p> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> <mkindsg> holding 7 mkinds objects</span> <span class="r-out co"><span class="r-pr">#></span> Title $title: Aerobic soil degradation data on dimethenamid-P from the EU assessment in 2018 </span> <span class="r-out co"><span class="r-pr">#></span> Occurrence of observed compounds $observed_n:</span> @@ -145,29 +151,29 @@ specific pieces of information in the comments.</p> <span class="r-out co"><span class="r-pr">#></span> Flaach NA 20</span> <span class="r-out co"><span class="r-pr">#></span> BBA 2.2 NA 20</span> <span class="r-out co"><span class="r-pr">#></span> BBA 2.3 NA 20</span> -<span class="r-in"><span class="va">dmta_ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span> -<span class="r-in"> <span class="va">ds_i</span> <span class="op"><-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span> -<span class="r-in"> <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op"><-</span> <span class="st">"DMTA"</span></span> -<span class="r-in"> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op"><-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span></span> -<span class="r-in"> <span class="va">ds_i</span></span> -<span class="r-in"><span class="op">}</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span> -<span class="r-in"><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span> -<span class="r-in"><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span></span> -<span class="r-in"><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span></span> -<span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"><span class="co"># We don't use DFOP for the parent compound, as this gives numerical</span></span> -<span class="r-in"><span class="co"># instabilities in the fits</span></span> -<span class="r-in"><span class="va">sfo_sfo3p</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span> -<span class="r-in"> DMTA <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span> -<span class="r-in"> M23 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> M27 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> M31 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> -<span class="r-in"> quiet <span class="op">=</span> <span class="cn">TRUE</span></span> -<span class="r-in"><span class="op">)</span></span> -<span class="r-in"><span class="va">dmta_sfo_sfo3p_tc</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"SFO-SFO3+"</span> <span class="op">=</span> <span class="va">sfo_sfo3p</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="va">dmta_ds</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span></span> +<span class="r-in"><span><span class="va">dmta_ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span></span> +<span class="r-in"><span> <span class="va">ds_i</span> <span class="op"><-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span></span> +<span class="r-in"><span> <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op"><-</span> <span class="st">"DMTA"</span></span></span> +<span class="r-in"><span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op"><-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span></span></span> +<span class="r-in"><span> <span class="va">ds_i</span></span></span> +<span class="r-in"><span><span class="op">}</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span></span></span> +<span class="r-in"><span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="co"># We don't use DFOP for the parent compound, as this gives numerical</span></span></span> +<span class="r-in"><span><span class="co"># instabilities in the fits</span></span></span> +<span class="r-in"><span><span class="va">sfo_sfo3p</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> DMTA <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M23 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M27 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M31 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> quiet <span class="op">=</span> <span class="cn">TRUE</span></span></span> +<span class="r-in"><span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">dmta_sfo_sfo3p_tc</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"SFO-SFO3+"</span> <span class="op">=</span> <span class="va">sfo_sfo3p</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="va">dmta_ds</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> <mmkin> object</span> <span class="r-out co"><span class="r-pr">#></span> Status of individual fits:</span> <span class="r-out co"><span class="r-pr">#></span> </span> @@ -176,35 +182,33 @@ specific pieces of information in the comments.</p> <span class="r-out co"><span class="r-pr">#></span> SFO-SFO3+ OK OK OK OK OK OK </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> OK: No warnings</span> -<span class="r-in"><span class="co"># The default (test_log_parms = FALSE) gives an undue</span></span> -<span class="r-in"><span class="co"># influence of ill-defined rate constants that have</span></span> -<span class="r-in"><span class="co"># extremely small values:</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="mixed.html">mixed</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span> +<span class="r-in"><span><span class="co"># The default (test_log_parms = FALSE) gives an undue</span></span></span> +<span class="r-in"><span><span class="co"># influence of ill-defined rate constants that have</span></span></span> +<span class="r-in"><span><span class="co"># extremely small values:</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="mixed.html">mixed</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># If we disregards ill-defined rate constants, the results</span></span></span> +<span class="r-in"><span><span class="co"># look more plausible, but the truth is likely to be in</span></span></span> +<span class="r-in"><span><span class="co"># between these variants</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="mixed.html">mixed</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="dimethenamid_2018-1.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="co"># If we disregards ill-defined rate constants, the results</span></span> -<span class="r-in"><span class="co"># look more plausible, but the truth is likely to be in</span></span> -<span class="r-in"><span class="co"># between these variants</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="mixed.html">mixed</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-plt img"><img src="dimethenamid_2018-2.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="co"># We can also specify a default value for the failing</span></span> -<span class="r-in"><span class="co"># log parameters, to mimic FOCUS guidance</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="mixed.html">mixed</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span>,</span> -<span class="r-in"> default_log_parms <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">/</span><span class="fl">1000</span><span class="op">)</span></span> -<span class="r-plt img"><img src="dimethenamid_2018-3.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="co"># As these attempts are not satisfying, we use nonlinear mixed-effects models</span></span> -<span class="r-in"><span class="co"># f_dmta_nlme_tc <- nlme(dmta_sfo_sfo3p_tc)</span></span> -<span class="r-in"><span class="co"># nlme reaches maxIter = 50 without convergence</span></span> -<span class="r-in"><span class="va">f_dmta_saem_tc</span> <span class="op"><-</span> <span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span></span> -<span class="r-in"><span class="co"># I am commenting out the convergence plot as rendering them</span></span> -<span class="r-in"><span class="co"># with pkgdown fails (at least without further tweaks to the</span></span> -<span class="r-in"><span class="co"># graphics device used)</span></span> -<span class="r-in"><span class="co">#saemix::plot(f_dmta_saem_tc$so, plot.type = "convergence")</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_dmta_saem_tc</span><span class="op">)</span></span> -<span class="r-out co"><span class="r-pr">#></span> saemix version used for fitting: 3.0 </span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.1.0 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.1 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Jun 30 10:21:01 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Jun 30 10:21:01 2022 </span> +<span class="r-in"><span><span class="co"># We can also specify a default value for the failing</span></span></span> +<span class="r-in"><span><span class="co"># log parameters, to mimic FOCUS guidance</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="mixed.html">mixed</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span>,</span></span> +<span class="r-in"><span> default_log_parms <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">/</span><span class="fl">1000</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># As these attempts are not satisfying, we use nonlinear mixed-effects models</span></span></span> +<span class="r-in"><span><span class="co"># f_dmta_nlme_tc <- nlme(dmta_sfo_sfo3p_tc)</span></span></span> +<span class="r-in"><span><span class="co"># nlme reaches maxIter = 50 without convergence</span></span></span> +<span class="r-in"><span><span class="va">f_dmta_saem_tc</span> <span class="op"><-</span> <span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># I am commenting out the convergence plot as rendering them</span></span></span> +<span class="r-in"><span><span class="co"># with pkgdown fails (at least without further tweaks to the</span></span></span> +<span class="r-in"><span><span class="co"># graphics device used)</span></span></span> +<span class="r-in"><span><span class="co">#saemix::plot(f_dmta_saem_tc$so, plot.type = "convergence")</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_dmta_saem_tc</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> saemix version used for fitting: 3.2 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.2.0 </span> +<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Nov 17 13:57:51 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Nov 17 13:57:51 2022 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_DMTA/dt = - k_DMTA * DMTA</span> @@ -217,7 +221,7 @@ specific pieces of information in the comments.</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type deSolve </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted in 1845.619 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted in 802.957 s</span> <span class="r-out co"><span class="r-pr">#></span> Using 300, 100 iterations and 9 chains</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Variance model: Two-component variance function </span> @@ -235,78 +239,88 @@ specific pieces of information in the comments.</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Likelihood computed by importance sampling</span> <span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> -<span class="r-out co"><span class="r-pr">#></span> 2276 2272 -1120</span> +<span class="r-out co"><span class="r-pr">#></span> 2276 2273 -1120</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Optimised parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0 88.3098 84.1383 92.4813</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_DMTA -3.0510 -3.5659 -2.5361</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M23 -4.0567 -4.9178 -3.1955</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M27 -3.8592 -4.2571 -3.4614</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M31 -3.9685 -4.4683 -3.4686</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_1 0.1382 -0.2120 0.4885</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_2 0.1429 -0.2616 0.5473</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_3 -1.3889 -1.6943 -1.0836</span> +<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> DMTA_0 88.3192 83.8656 92.7729</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_DMTA -3.0530 -3.5686 -2.5373</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M23 -4.0620 -4.9202 -3.2038</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M27 -3.8633 -4.2668 -3.4598</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M31 -3.9731 -4.4763 -3.4699</span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_1 0.1346 -0.2150 0.4841</span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_2 0.1449 -0.2593 0.5491</span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_3 -1.3882 -1.7011 -1.0753</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 0.9156 0.8229 1.0084</span> +<span class="r-out co"><span class="r-pr">#></span> b.1 0.1383 0.1215 0.1551</span> +<span class="r-out co"><span class="r-pr">#></span> SD.DMTA_0 3.7280 -0.6951 8.1511</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_DMTA 0.6431 0.2781 1.0080</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_M23 1.0096 0.3782 1.6409</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_M27 0.4583 0.1541 0.7625</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_M31 0.5738 0.1942 0.9533</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_DMTA_ilr_1 0.4119 0.1528 0.6709</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_DMTA_ilr_2 0.4780 0.1806 0.7754</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_DMTA_ilr_3 0.3657 0.1383 0.5931</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Correlation: </span> <span class="r-out co"><span class="r-pr">#></span> DMTA_0 l__DMTA lg__M23 lg__M27 lg__M31 f_DMTA__1 f_DMTA__2</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_DMTA 0.0315 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M23 -0.0237 -0.0031 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M27 -0.0392 -0.0048 0.0040 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M31 -0.0257 -0.0032 0.0022 0.0821 </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_1 -0.0048 -0.0007 0.0415 -0.0435 0.0333 </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_2 -0.0007 -0.0002 0.0214 -0.0270 -0.0900 -0.0372 </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_3 -0.1861 -0.0136 0.0431 0.0797 0.0382 -0.0072 0.0066 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k_DMTA 0.0303 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M23 -0.0229 -0.0032 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M27 -0.0372 -0.0049 0.0041 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M31 -0.0245 -0.0032 0.0022 0.0815 </span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_1 -0.0046 -0.0006 0.0415 -0.0433 0.0324 </span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_2 -0.0008 -0.0002 0.0214 -0.0267 -0.0893 -0.0361 </span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_3 -0.1755 -0.0135 0.0423 0.0775 0.0377 -0.0066 0.0060 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Random effects:</span> <span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> SD.DMTA_0 3.2733 -1.1098 7.6564</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k_DMTA 0.6422 0.2777 1.0066</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k_M23 1.0131 0.3797 1.6465</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k_M27 0.4511 0.1510 0.7513</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k_M31 0.5695 0.1923 0.9466</span> -<span class="r-out co"><span class="r-pr">#></span> SD.f_DMTA_ilr_1 0.4123 0.1526 0.6720</span> -<span class="r-out co"><span class="r-pr">#></span> SD.f_DMTA_ilr_2 0.4780 0.1804 0.7757</span> -<span class="r-out co"><span class="r-pr">#></span> SD.f_DMTA_ilr_3 0.3559 0.1344 0.5775</span> +<span class="r-out co"><span class="r-pr">#></span> SD.DMTA_0 3.7280 -0.6951 8.1511</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_DMTA 0.6431 0.2781 1.0080</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_M23 1.0096 0.3782 1.6409</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_M27 0.4583 0.1541 0.7625</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_M31 0.5738 0.1942 0.9533</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_DMTA_ilr_1 0.4119 0.1528 0.6709</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_DMTA_ilr_2 0.4780 0.1806 0.7754</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_DMTA_ilr_3 0.3657 0.1383 0.5931</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Variance model:</span> <span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> a.1 0.9255 0.8288 1.0221</span> -<span class="r-out co"><span class="r-pr">#></span> b.1 0.1365 0.1191 0.1538</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 0.9156 0.8229 1.0084</span> +<span class="r-out co"><span class="r-pr">#></span> b.1 0.1383 0.1215 0.1551</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Backtransformed parameters:</span> <span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0 88.30980 84.138334 92.48126</span> -<span class="r-out co"><span class="r-pr">#></span> k_DMTA 0.04731 0.028272 0.07918</span> -<span class="r-out co"><span class="r-pr">#></span> k_M23 0.01731 0.007315 0.04095</span> -<span class="r-out co"><span class="r-pr">#></span> k_M27 0.02108 0.014164 0.03139</span> -<span class="r-out co"><span class="r-pr">#></span> k_M31 0.01890 0.011467 0.03116</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M23 0.14626 NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M27 0.12029 NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M31 0.11135 NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> DMTA_0 88.31924 83.865625 92.77286</span> +<span class="r-out co"><span class="r-pr">#></span> k_DMTA 0.04722 0.028196 0.07908</span> +<span class="r-out co"><span class="r-pr">#></span> k_M23 0.01721 0.007298 0.04061</span> +<span class="r-out co"><span class="r-pr">#></span> k_M27 0.02100 0.014027 0.03144</span> +<span class="r-out co"><span class="r-pr">#></span> k_M31 0.01882 0.011375 0.03112</span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M23 0.14608 NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M27 0.12077 NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M31 0.11123 NA NA</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Resulting formation fractions:</span> <span class="r-out co"><span class="r-pr">#></span> ff</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_M23 0.1463</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_M27 0.1203</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_M31 0.1113</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_sink 0.6221</span> +<span class="r-out co"><span class="r-pr">#></span> DMTA_M23 0.1461</span> +<span class="r-out co"><span class="r-pr">#></span> DMTA_M27 0.1208</span> +<span class="r-out co"><span class="r-pr">#></span> DMTA_M31 0.1112</span> +<span class="r-out co"><span class="r-pr">#></span> DMTA_sink 0.6219</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Estimated disappearance times:</span> <span class="r-out co"><span class="r-pr">#></span> DT50 DT90</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA 14.65 48.67</span> -<span class="r-out co"><span class="r-pr">#></span> M23 40.05 133.05</span> -<span class="r-out co"><span class="r-pr">#></span> M27 32.88 109.21</span> -<span class="r-out co"><span class="r-pr">#></span> M31 36.67 121.81</span> -<span class="r-in"><span class="co"># As the confidence interval for the random effects of DMTA_0</span></span> -<span class="r-in"><span class="co"># includes zero, we could try an alternative model without</span></span> -<span class="r-in"><span class="co"># such random effects</span></span> -<span class="r-in"><span class="co"># f_dmta_saem_tc_2 <- saem(dmta_sfo_sfo3p_tc,</span></span> -<span class="r-in"><span class="co"># covariance.model = diag(c(0, rep(1, 7))))</span></span> -<span class="r-in"><span class="co"># saemix::plot(f_dmta_saem_tc_2$so, plot.type = "convergence")</span></span> -<span class="r-in"><span class="co"># This does not perform better judged by AIC and BIC</span></span> -<span class="r-in"><span class="co"># saemix::compare.saemix(f_dmta_saem_tc$so, f_dmta_saem_tc_2$so)</span></span> -<span class="r-in"><span class="co"># }</span></span> +<span class="r-out co"><span class="r-pr">#></span> DMTA 14.68 48.76</span> +<span class="r-out co"><span class="r-pr">#></span> M23 40.27 133.76</span> +<span class="r-out co"><span class="r-pr">#></span> M27 33.01 109.65</span> +<span class="r-out co"><span class="r-pr">#></span> M31 36.84 122.38</span> +<span class="r-in"><span><span class="co"># As the confidence interval for the random effects of DMTA_0</span></span></span> +<span class="r-in"><span><span class="co"># includes zero, we could try an alternative model without</span></span></span> +<span class="r-in"><span><span class="co"># such random effects</span></span></span> +<span class="r-in"><span><span class="co"># f_dmta_saem_tc_2 <- saem(dmta_sfo_sfo3p_tc,</span></span></span> +<span class="r-in"><span><span class="co"># covariance.model = diag(c(0, rep(1, 7))))</span></span></span> +<span class="r-in"><span><span class="co"># saemix::plot(f_dmta_saem_tc_2$so, plot.type = "convergence")</span></span></span> +<span class="r-in"><span><span class="co"># This does not perform better judged by AIC and BIC</span></span></span> +<span class="r-in"><span><span class="co"># saemix::compare.saemix(f_dmta_saem_tc$so, f_dmta_saem_tc_2$so)</span></span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> </code></pre></div> </div> </div> @@ -321,7 +335,7 @@ specific pieces of information in the comments.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/ds_mixed-1.png b/docs/reference/ds_mixed-1.png Binary files differnew file mode 100644 index 00000000..a7f5c395 --- /dev/null +++ b/docs/reference/ds_mixed-1.png diff --git a/docs/reference/ds_mixed.html b/docs/reference/ds_mixed.html new file mode 100644 index 00000000..64b02749 --- /dev/null +++ b/docs/reference/ds_mixed.html @@ -0,0 +1,240 @@ +<!DOCTYPE html> +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Synthetic data for 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installed with this package in +the 'dataset_generation' directory.</p> + </div> + + + + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span> <span class="va">sfo_mmkin</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">ds_sfo</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, cores <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">sfo_saem</span> <span class="op"><-</span> <span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">sfo_mmkin</span>, no_random_effect <span class="op">=</span> <span class="st">"parent_0"</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">sfo_saem</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="ds_mixed-1.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># This is the code used to generate the datasets</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/readLines.html" class="external-link">readLines</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/system.file.html" class="external-link">system.file</a></span><span class="op">(</span><span class="st">"dataset_generation/ds_mixed.R"</span>, package <span class="op">=</span> <span class="st">"mkin"</span><span class="op">)</span><span class="op">)</span>, sep <span class="op">=</span> <span class="st">"\n"</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> # Synthetic data for hierarchical kinetic models</span> +<span class="r-out co"><span class="r-pr">#></span> # Refactored version of the code previously in tests/testthat/setup_script.R</span> +<span class="r-out co"><span class="r-pr">#></span> # The number of datasets was 3 for FOMC, and 10 for HS in that script, now it</span> +<span class="r-out co"><span class="r-pr">#></span> # is always 15 for consistency</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> library(mkin) # We use mkinmod and mkinpredict</span> +<span class="r-out co"><span class="r-pr">#></span> sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)</span> +<span class="r-out co"><span class="r-pr">#></span> n <- 15</span> +<span class="r-out co"><span class="r-pr">#></span> log_sd <- 0.3</span> +<span class="r-out co"><span class="r-pr">#></span> err_1 = list(const = 1, prop = 0.05)</span> +<span class="r-out co"><span class="r-pr">#></span> tc <- function(value) sigma_twocomp(value, err_1$const, err_1$prop)</span> +<span class="r-out co"><span class="r-pr">#></span> const <- function(value) 2</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span> +<span class="r-out co"><span class="r-pr">#></span> SFO <- mkinmod(parent = mkinsub("SFO"))</span> +<span class="r-out co"><span class="r-pr">#></span> sfo_pop <- list(parent_0 = 100, k_parent = 0.03)</span> +<span class="r-out co"><span class="r-pr">#></span> sfo_parms <- as.matrix(data.frame(</span> +<span class="r-out co"><span class="r-pr">#></span> k_parent = rlnorm(n, log(sfo_pop$k_parent), log_sd)))</span> +<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span> +<span class="r-out co"><span class="r-pr">#></span> ds_sfo <- lapply(1:n, function(i) {</span> +<span class="r-out co"><span class="r-pr">#></span> ds_mean <- mkinpredict(SFO, sfo_parms[i, ],</span> +<span class="r-out co"><span class="r-pr">#></span> c(parent = sfo_pop$parent_0), sampling_times)</span> +<span class="r-out co"><span class="r-pr">#></span> add_err(ds_mean, tc, n = 1)[[1]]</span> +<span class="r-out co"><span class="r-pr">#></span> })</span> +<span class="r-out co"><span class="r-pr">#></span> attr(ds_sfo, "pop") <- sfo_pop</span> +<span class="r-out co"><span class="r-pr">#></span> attr(ds_sfo, "parms") <- sfo_parms</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC <- mkinmod(parent = mkinsub("FOMC"))</span> +<span class="r-out co"><span class="r-pr">#></span> fomc_pop <- list(parent_0 = 100, alpha = 2, beta = 8)</span> +<span class="r-out co"><span class="r-pr">#></span> fomc_parms <- as.matrix(data.frame(</span> +<span class="r-out co"><span class="r-pr">#></span> alpha = rlnorm(n, log(fomc_pop$alpha), 0.4),</span> +<span class="r-out co"><span class="r-pr">#></span> beta = rlnorm(n, log(fomc_pop$beta), 0.2)))</span> +<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span> +<span class="r-out co"><span class="r-pr">#></span> ds_fomc <- lapply(1:n, function(i) {</span> +<span class="r-out co"><span class="r-pr">#></span> ds_mean <- mkinpredict(FOMC, fomc_parms[i, ],</span> +<span class="r-out co"><span class="r-pr">#></span> c(parent = fomc_pop$parent_0), sampling_times)</span> +<span class="r-out co"><span class="r-pr">#></span> add_err(ds_mean, tc, n = 1)[[1]]</span> +<span class="r-out co"><span class="r-pr">#></span> })</span> +<span class="r-out co"><span class="r-pr">#></span> attr(ds_fomc, "pop") <- fomc_pop</span> +<span class="r-out co"><span class="r-pr">#></span> attr(ds_fomc, "parms") <- fomc_parms</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span> +<span class="r-out co"><span class="r-pr">#></span> DFOP <- mkinmod(parent = mkinsub("DFOP"))</span> +<span class="r-out co"><span class="r-pr">#></span> dfop_pop <- list(parent_0 = 100, k1 = 0.06, k2 = 0.015, g = 0.4)</span> +<span class="r-out co"><span class="r-pr">#></span> dfop_parms <- as.matrix(data.frame(</span> +<span class="r-out co"><span class="r-pr">#></span> k1 = rlnorm(n, log(dfop_pop$k1), log_sd),</span> +<span class="r-out co"><span class="r-pr">#></span> k2 = rlnorm(n, log(dfop_pop$k2), log_sd),</span> +<span class="r-out co"><span class="r-pr">#></span> g = plogis(rnorm(n, qlogis(dfop_pop$g), log_sd))))</span> +<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span> +<span class="r-out co"><span class="r-pr">#></span> ds_dfop <- lapply(1:n, function(i) {</span> +<span class="r-out co"><span class="r-pr">#></span> ds_mean <- mkinpredict(DFOP, dfop_parms[i, ],</span> +<span class="r-out co"><span class="r-pr">#></span> c(parent = dfop_pop$parent_0), sampling_times)</span> +<span class="r-out co"><span class="r-pr">#></span> add_err(ds_mean, tc, n = 1)[[1]]</span> +<span class="r-out co"><span class="r-pr">#></span> })</span> +<span class="r-out co"><span class="r-pr">#></span> attr(ds_dfop, "pop") <- dfop_pop</span> +<span class="r-out co"><span class="r-pr">#></span> attr(ds_dfop, "parms") <- dfop_parms</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span> +<span class="r-out co"><span class="r-pr">#></span> HS <- mkinmod(parent = mkinsub("HS"))</span> +<span class="r-out co"><span class="r-pr">#></span> hs_pop <- list(parent_0 = 100, k1 = 0.08, k2 = 0.01, tb = 15)</span> +<span class="r-out co"><span class="r-pr">#></span> hs_parms <- as.matrix(data.frame(</span> +<span class="r-out co"><span class="r-pr">#></span> k1 = rlnorm(n, log(hs_pop$k1), log_sd),</span> +<span class="r-out co"><span class="r-pr">#></span> k2 = rlnorm(n, log(hs_pop$k2), log_sd),</span> +<span class="r-out co"><span class="r-pr">#></span> tb = rlnorm(n, log(hs_pop$tb), 0.1)))</span> +<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span> +<span class="r-out co"><span class="r-pr">#></span> ds_hs <- lapply(1:n, function(i) {</span> +<span class="r-out co"><span class="r-pr">#></span> ds_mean <- mkinpredict(HS, hs_parms[i, ],</span> +<span class="r-out co"><span class="r-pr">#></span> c(parent = hs_pop$parent_0), sampling_times)</span> +<span class="r-out co"><span class="r-pr">#></span> add_err(ds_mean, const, n = 1)[[1]]</span> +<span class="r-out co"><span class="r-pr">#></span> })</span> +<span class="r-out co"><span class="r-pr">#></span> attr(ds_hs, "pop") <- hs_pop</span> +<span class="r-out co"><span class="r-pr">#></span> attr(ds_hs, "parms") <- hs_parms</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span> +<span class="r-out co"><span class="r-pr">#></span> DFOP_SFO <- mkinmod(</span> +<span class="r-out co"><span class="r-pr">#></span> parent = mkinsub("DFOP", "m1"),</span> +<span class="r-out co"><span class="r-pr">#></span> m1 = mkinsub("SFO"),</span> +<span class="r-out co"><span class="r-pr">#></span> quiet = TRUE)</span> +<span class="r-out co"><span class="r-pr">#></span> dfop_sfo_pop <- list(parent_0 = 100,</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 = 0.007, f_parent_to_m1 = 0.5,</span> +<span class="r-out co"><span class="r-pr">#></span> k1 = 0.1, k2 = 0.02, g = 0.5)</span> +<span class="r-out co"><span class="r-pr">#></span> dfop_sfo_parms <- as.matrix(data.frame(</span> +<span class="r-out co"><span class="r-pr">#></span> k1 = rlnorm(n, log(dfop_sfo_pop$k1), log_sd),</span> +<span class="r-out co"><span class="r-pr">#></span> k2 = rlnorm(n, log(dfop_sfo_pop$k2), log_sd),</span> +<span class="r-out co"><span class="r-pr">#></span> g = plogis(rnorm(n, qlogis(dfop_sfo_pop$g), log_sd)),</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 = plogis(rnorm(n,</span> +<span class="r-out co"><span class="r-pr">#></span> qlogis(dfop_sfo_pop$f_parent_to_m1), log_sd)),</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 = rlnorm(n, log(dfop_sfo_pop$k_m1), log_sd)))</span> +<span class="r-out co"><span class="r-pr">#></span> ds_dfop_sfo_mean <- lapply(1:n,</span> +<span class="r-out co"><span class="r-pr">#></span> function(i) {</span> +<span class="r-out co"><span class="r-pr">#></span> mkinpredict(DFOP_SFO, dfop_sfo_parms[i, ],</span> +<span class="r-out co"><span class="r-pr">#></span> c(parent = dfop_sfo_pop$parent_0, m1 = 0), sampling_times)</span> +<span class="r-out co"><span class="r-pr">#></span> }</span> +<span class="r-out co"><span class="r-pr">#></span> )</span> +<span class="r-out co"><span class="r-pr">#></span> set.seed(123456)</span> +<span class="r-out co"><span class="r-pr">#></span> ds_dfop_sfo <- lapply(ds_dfop_sfo_mean, function(ds) {</span> +<span class="r-out co"><span class="r-pr">#></span> add_err(ds,</span> +<span class="r-out co"><span class="r-pr">#></span> sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2),</span> +<span class="r-out co"><span class="r-pr">#></span> n = 1, secondary = "m1")[[1]]</span> +<span class="r-out co"><span class="r-pr">#></span> })</span> +<span class="r-out co"><span class="r-pr">#></span> attr(ds_dfop_sfo, "pop") <- dfop_sfo_pop</span> +<span class="r-out co"><span class="r-pr">#></span> attr(ds_dfop_sfo, "parms") <- dfop_sfo_parms</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> #save(ds_sfo, ds_fomc, ds_dfop, ds_hs, ds_dfop_sfo, file = "data/ds_mixed.rda", version = 2)</span> +</code></pre></div> + </div> + </div> + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> +</div> + + + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +</div> + + </footer></div> + + + + + + + </body></html> + diff --git a/docs/reference/endpoints.html b/docs/reference/endpoints.html index 883c9e5d..1f49092e 100644 --- a/docs/reference/endpoints.html +++ b/docs/reference/endpoints.html @@ -23,7 +23,7 @@ advantage that the SFORB model can also be used for metabolites."><!-- mathjax - </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -50,19 +50,25 @@ advantage that the SFORB model can also be used for metabolites."><!-- mathjax - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -98,24 +104,26 @@ advantage that the SFORB model can also be used for metabolites.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">endpoints</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">endpoints</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>fit</dt> -<dd><p>An object of class <a href="mkinfit.html">mkinfit</a>, <a href="nlme.mmkin.html">nlme.mmkin</a> or <a href="saem.html">saem.mmkin</a>, -or another object that has list components -mkinmod containing an <a href="mkinmod.html">mkinmod</a> degradation model, and two numeric vectors, -bparms.optim and bparms.fixed, that contain parameter values -for that model.</p></dd> +<dd><p>An object of class <a href="mkinfit.html">mkinfit</a>, <a href="nlme.mmkin.html">nlme.mmkin</a> or <a href="saem.html">saem.mmkin</a>, or +another object that has list components mkinmod containing an <a href="mkinmod.html">mkinmod</a> +degradation model, and two numeric vectors, bparms.optim and bparms.fixed, +that contain parameter values for that model.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>A list with a matrix of dissipation times named distimes, -and, if applicable, a vector of formation fractions named ff -and, if the SFORB model was in use, a vector of eigenvalues -of these SFORB models, equivalent to DFOP rate constants</p> + + +<p>A list with a matrix of dissipation times named distimes, and, if +applicable, a vector of formation fractions named ff and, if the SFORB model +was in use, a vector of eigenvalues of these SFORB models, equivalent to +DFOP rate constants</p> </div> <div id="details"> <h2>Details</h2> @@ -134,36 +142,36 @@ HS and DFOP, as well as from Eigenvalues b1 and b2 of any SFORB models</p> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"> <span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu">endpoints</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu">endpoints</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> $distimes</span> <span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back</span> <span class="r-out co"><span class="r-pr">#></span> parent 1.785233 15.1479 4.559973</span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-in"> <span class="co"># \dontrun{</span></span> -<span class="r-in"> <span class="va">fit_2</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu">endpoints</span><span class="op">(</span><span class="va">fit_2</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span> <span class="va">fit_2</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu">endpoints</span><span class="op">(</span><span class="va">fit_2</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> $distimes</span> <span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_k1 DT50_k2</span> <span class="r-out co"><span class="r-pr">#></span> parent 1.886925 21.25106 6.397207 1.508293 38.83438</span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-in"> <span class="va">fit_3</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu">endpoints</span><span class="op">(</span><span class="va">fit_3</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="va">fit_3</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu">endpoints</span><span class="op">(</span><span class="va">fit_3</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> $ff</span> <span class="r-out co"><span class="r-pr">#></span> parent_free </span> <span class="r-out co"><span class="r-pr">#></span> 1 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> $SFORB</span> -<span class="r-out co"><span class="r-pr">#></span> parent_b1 parent_b2 </span> -<span class="r-out co"><span class="r-pr">#></span> 0.4595574 0.0178488 </span> +<span class="r-out co"><span class="r-pr">#></span> parent_b1 parent_b2 parent_g </span> +<span class="r-out co"><span class="r-pr">#></span> 0.4595574 0.0178488 0.8539454 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> $distimes</span> <span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_parent_b1 DT50_parent_b2</span> <span class="r-out co"><span class="r-pr">#></span> parent 1.886925 21.25106 6.397208 1.508293 38.83438</span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-in"> <span class="co"># }</span></span> -<span class="r-in"></span> +<span class="r-in"><span> <span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -178,7 +186,7 @@ HS and DFOP, as well as from Eigenvalues b1 and b2 of any SFORB models</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/experimental_data_for_UBA-1.png b/docs/reference/experimental_data_for_UBA-1.png Binary files differindex 50c09278..b7b4d63b 100644 --- a/docs/reference/experimental_data_for_UBA-1.png +++ b/docs/reference/experimental_data_for_UBA-1.png diff --git a/docs/reference/experimental_data_for_UBA.html b/docs/reference/experimental_data_for_UBA.html index 69a8baf4..08d2de00 100644 --- a/docs/reference/experimental_data_for_UBA.html +++ b/docs/reference/experimental_data_for_UBA.html @@ -45,7 +45,7 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -72,19 +72,25 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -143,7 +149,7 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="va">experimental_data_for_UBA_2019</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">experimental_data_for_UBA_2019</span></span></code></pre></div> </div> <div id="format"> @@ -182,48 +188,48 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># Model definitions</span></span> -<span class="r-in"><span class="va">sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span> -<span class="r-in"> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"A1"</span><span class="op">)</span>,</span> -<span class="r-in"> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> use_of_ff <span class="op">=</span> <span class="st">"max"</span></span> -<span class="r-in"><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Model definitions</span></span></span> +<span class="r-in"><span><span class="va">sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"A1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> use_of_ff <span class="op">=</span> <span class="st">"max"</span></span></span> +<span class="r-in"><span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"></span> -<span class="r-in"><span class="va">dfop_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span> -<span class="r-in"> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="st">"A1"</span><span class="op">)</span>,</span> -<span class="r-in"> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> use_of_ff <span class="op">=</span> <span class="st">"max"</span></span> -<span class="r-in"><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">dfop_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="st">"A1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> use_of_ff <span class="op">=</span> <span class="st">"max"</span></span></span> +<span class="r-in"><span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"></span> -<span class="r-in"><span class="va">sfo_sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span> -<span class="r-in"> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"A1"</span><span class="op">)</span>,</span> -<span class="r-in"> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"A2"</span><span class="op">)</span>,</span> -<span class="r-in"> A2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> use_of_ff <span class="op">=</span> <span class="st">"max"</span></span> -<span class="r-in"><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">sfo_sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"A1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"A2"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> A2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> use_of_ff <span class="op">=</span> <span class="st">"max"</span></span></span> +<span class="r-in"><span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"></span> -<span class="r-in"><span class="va">dfop_sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span> -<span class="r-in"> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="st">"A1"</span><span class="op">)</span>,</span> -<span class="r-in"> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"A2"</span><span class="op">)</span>,</span> -<span class="r-in"> A2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> use_of_ff <span class="op">=</span> <span class="st">"max"</span></span> -<span class="r-in"><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">dfop_sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="st">"A1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"A2"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> A2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> use_of_ff <span class="op">=</span> <span class="st">"max"</span></span></span> +<span class="r-in"><span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"><span class="va">d_1_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">2</span><span class="op">]</span>, <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">d_1_2</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Soil"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">2</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">f_1_2_tc</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"DFOP-SFO-SFO"</span> <span class="op">=</span> <span class="va">dfop_sfo_sfo</span><span class="op">)</span>, <span class="va">d_1_2</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_1_2_tc</span>, resplot <span class="op">=</span> <span class="st">"errmod"</span><span class="op">)</span></span> +<span class="r-in"><span><span class="va">d_1_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">2</span><span class="op">]</span>, <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">d_1_2</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Soil"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">2</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">f_1_2_tc</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"DFOP-SFO-SFO"</span> <span class="op">=</span> <span class="va">dfop_sfo_sfo</span><span class="op">)</span>, <span class="va">d_1_2</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_1_2_tc</span>, resplot <span class="op">=</span> <span class="st">"errmod"</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="experimental_data_for_UBA-1.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># }</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> </code></pre></div> </div> </div> @@ -238,7 +244,7 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/f_time_norm_focus.html b/docs/reference/f_time_norm_focus.html index 27da5718..caeb25a1 100644 --- a/docs/reference/f_time_norm_focus.html +++ b/docs/reference/f_time_norm_focus.html @@ -18,7 +18,7 @@ in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369)."><!-- mathjax </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -45,19 +45,25 @@ in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369)."><!-- mathjax <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -89,56 +95,73 @@ in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369).</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">f_time_norm_focus</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span> - -<span class="co"># S3 method for numeric</span> -<span class="fu">f_time_norm_focus</span><span class="op">(</span> - <span class="va">object</span>, - moisture <span class="op">=</span> <span class="cn">NA</span>, - field_moisture <span class="op">=</span> <span class="cn">NA</span>, - temperature <span class="op">=</span> <span class="va">object</span>, - Q10 <span class="op">=</span> <span class="fl">2.58</span>, - walker <span class="op">=</span> <span class="fl">0.7</span>, - f_na <span class="op">=</span> <span class="cn">NA</span>, - <span class="va">...</span> -<span class="op">)</span> - -<span class="co"># S3 method for mkindsg</span> -<span class="fu">f_time_norm_focus</span><span class="op">(</span> - <span class="va">object</span>, - study_moisture_ref_source <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"auto"</span>, <span class="st">"meta"</span>, <span class="st">"focus"</span><span class="op">)</span>, - Q10 <span class="op">=</span> <span class="fl">2.58</span>, - walker <span class="op">=</span> <span class="fl">0.7</span>, - f_na <span class="op">=</span> <span class="cn">NA</span>, - <span class="va">...</span> -<span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">f_time_norm_focus</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for numeric</span></span> +<span><span class="fu">f_time_norm_focus</span><span class="op">(</span></span> +<span> <span class="va">object</span>,</span> +<span> moisture <span class="op">=</span> <span class="cn">NA</span>,</span> +<span> field_moisture <span class="op">=</span> <span class="cn">NA</span>,</span> +<span> temperature <span class="op">=</span> <span class="va">object</span>,</span> +<span> Q10 <span class="op">=</span> <span class="fl">2.58</span>,</span> +<span> walker <span class="op">=</span> <span class="fl">0.7</span>,</span> +<span> f_na <span class="op">=</span> <span class="cn">NA</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mkindsg</span></span> +<span><span class="fu">f_time_norm_focus</span><span class="op">(</span></span> +<span> <span class="va">object</span>,</span> +<span> study_moisture_ref_source <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"auto"</span>, <span class="st">"meta"</span>, <span class="st">"focus"</span><span class="op">)</span>,</span> +<span> Q10 <span class="op">=</span> <span class="fl">2.58</span>,</span> +<span> walker <span class="op">=</span> <span class="fl">0.7</span>,</span> +<span> f_na <span class="op">=</span> <span class="cn">NA</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>object</dt> <dd><p>An object containing information used for the calculations</p></dd> + + <dt>...</dt> <dd><p>Currently not used</p></dd> + + <dt>moisture</dt> <dd><p>Numeric vector of moisture contents in \% w/w</p></dd> + + <dt>field_moisture</dt> <dd><p>Numeric vector of moisture contents at field capacity (pF2) in \% w/w</p></dd> + + <dt>temperature</dt> <dd><p>Numeric vector of temperatures in °C</p></dd> + + <dt>Q10</dt> <dd><p>The Q10 value used for temperature normalisation</p></dd> + + <dt>walker</dt> <dd><p>The Walker exponent used for moisture normalisation</p></dd> + + <dt>f_na</dt> <dd><p>The factor to use for NA values. If set to NA, only factors for complete cases will be returned.</p></dd> + + <dt>study_moisture_ref_source</dt> <dd><p>Source for the reference value used to calculate the study moisture. If 'auto', preference is given to a reference moisture given in the meta information, otherwise the focus soil moisture for the soil class is used</p></dd> + </dl></div> <div id="references"> <h2>References</h2> @@ -160,10 +183,10 @@ Version 1.1, 18 December 2014 <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="fu">f_time_norm_focus</span><span class="op">(</span><span class="fl">25</span>, <span class="fl">20</span>, <span class="fl">25</span><span class="op">)</span> <span class="co"># 1.37, compare FOCUS 2014 p. 184</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu">f_time_norm_focus</span><span class="op">(</span><span class="fl">25</span>, <span class="fl">20</span>, <span class="fl">25</span><span class="op">)</span> <span class="co"># 1.37, compare FOCUS 2014 p. 184</span></span></span> <span class="r-out co"><span class="r-pr">#></span> [1] 1.373956</span> -<span class="r-in"></span> -<span class="r-in"><span class="va">D24_2014</span><span class="op">$</span><span class="va">meta</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">D24_2014</span><span class="op">$</span><span class="va">meta</span></span></span> <span class="r-out co"><span class="r-pr">#></span> study usda_soil_type study_moisture_ref_type</span> <span class="r-out co"><span class="r-pr">#></span> Mississippi Cohen 1991 Silt loam <NA></span> <span class="r-out co"><span class="r-pr">#></span> Fayette Liu and Adelfinskaya 2011 Silt loam pF1</span> @@ -176,9 +199,9 @@ Version 1.1, 18 December 2014 <span class="r-out co"><span class="r-pr">#></span> RefSol 03-G 0.5 20</span> <span class="r-out co"><span class="r-pr">#></span> Site E1 0.5 20</span> <span class="r-out co"><span class="r-pr">#></span> Site I2 0.5 20</span> -<span class="r-in"><span class="co"># No moisture normalisation in the first dataset, so we use f_na = 1 to get</span></span> -<span class="r-in"><span class="co"># temperature only normalisation as in the EU evaluation</span></span> -<span class="r-in"><span class="fu">f_time_norm_focus</span><span class="op">(</span><span class="va">D24_2014</span>, study_moisture_ref_source <span class="op">=</span> <span class="st">"focus"</span>, f_na <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span> +<span class="r-in"><span><span class="co"># No moisture normalisation in the first dataset, so we use f_na = 1 to get</span></span></span> +<span class="r-in"><span><span class="co"># temperature only normalisation as in the EU evaluation</span></span></span> +<span class="r-in"><span><span class="fu">f_time_norm_focus</span><span class="op">(</span><span class="va">D24_2014</span>, study_moisture_ref_source <span class="op">=</span> <span class="st">"focus"</span>, f_na <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> $f_time_norm was (re)set to normalised values</span> </code></pre></div> </div> @@ -194,7 +217,7 @@ Version 1.1, 18 December 2014 </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/focus_soil_moisture.html b/docs/reference/focus_soil_moisture.html index 6b64ba73..088c7bc3 100644 --- a/docs/reference/focus_soil_moisture.html +++ b/docs/reference/focus_soil_moisture.html @@ -18,7 +18,7 @@ corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5."><!-- mathjax --><script </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -45,19 +45,25 @@ corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5."><!-- mathjax --><script <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -89,7 +95,7 @@ corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="va">focus_soil_moisture</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">focus_soil_moisture</span></span></code></pre></div> </div> <div id="format"> @@ -105,7 +111,7 @@ Version 2.2, May 2014 <a href="https://esdac.jrc.ec.europa.eu/projects/ground-wa <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="va">focus_soil_moisture</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">focus_soil_moisture</span></span></span> <span class="r-out co"><span class="r-pr">#></span> pF1 pF2 pF2.5</span> <span class="r-out co"><span class="r-pr">#></span> Sand 24 12 7</span> <span class="r-out co"><span class="r-pr">#></span> Loamy sand 24 14 9</span> @@ -133,7 +139,7 @@ Version 2.2, May 2014 <a href="https://esdac.jrc.ec.europa.eu/projects/ground-wa </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/get_deg_func.html b/docs/reference/get_deg_func.html index c63a968c..dc6fee5e 100644 --- a/docs/reference/get_deg_func.html +++ b/docs/reference/get_deg_func.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -44,19 +44,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -87,12 +93,14 @@ </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">get_deg_func</span><span class="op">(</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">get_deg_func</span><span class="op">(</span><span class="op">)</span></span></code></pre></div> </div> <div id="value"> <h2>Value</h2> - <p>A function that was likely previously assigned from within + + +<p>A function that was likely previously assigned from within nlme.mmkin</p> </div> @@ -108,7 +116,7 @@ nlme.mmkin</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/illparms.html b/docs/reference/illparms.html index cbdbfde2..52f130e6 100644 --- a/docs/reference/illparms.html +++ b/docs/reference/illparms.html @@ -21,7 +21,7 @@ without parameter transformations is used."><!-- mathjax --><script src="https:/ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -48,19 +48,25 @@ without parameter transformations is used."><!-- mathjax --><script src="https:/ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -100,6 +106,9 @@ without parameter transformations is used.</p> <span><span class="co"># S3 method for mkinfit</span></span> <span><span class="fu">illparms</span><span class="op">(</span><span class="va">object</span>, conf.level <span class="op">=</span> <span class="fl">0.95</span>, <span class="va">...</span><span class="op">)</span></span> <span></span> +<span><span class="co"># S3 method for illparms.mkinfit</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> <span><span class="co"># S3 method for mmkin</span></span> <span><span class="fu">illparms</span><span class="op">(</span><span class="va">object</span>, conf.level <span class="op">=</span> <span class="fl">0.95</span>, <span class="va">...</span><span class="op">)</span></span> <span></span> @@ -109,6 +118,9 @@ without parameter transformations is used.</p> <span><span class="co"># S3 method for saem.mmkin</span></span> <span><span class="fu">illparms</span><span class="op">(</span><span class="va">object</span>, conf.level <span class="op">=</span> <span class="fl">0.95</span>, random <span class="op">=</span> <span class="cn">TRUE</span>, errmod <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span> <span></span> +<span><span class="co"># S3 method for illparms.saem.mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> <span><span class="co"># S3 method for mhmkin</span></span> <span><span class="fu">illparms</span><span class="op">(</span><span class="va">object</span>, conf.level <span class="op">=</span> <span class="fl">0.95</span>, random <span class="op">=</span> <span class="cn">TRUE</span>, errmod <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span> <span></span> @@ -149,8 +161,7 @@ tested?</p></dd> <p>For <a href="mkinfit.html">mkinfit</a> or <a href="saem.html">saem</a> objects, a character vector of parameter names. For <a href="mmkin.html">mmkin</a> or <a href="mhmkin.html">mhmkin</a> objects, a matrix like object of class -'illparms.mmkin' or 'illparms.mhmkin'. The latter objects have a suitable -printing method.</p> +'illparms.mmkin' or 'illparms.mhmkin'.</p> </div> <div id="details"> <h2>Details</h2> @@ -161,6 +172,11 @@ effects expressed as standard deviations include zero, and if the confidence intervals for the error model parameters include zero.</p> </div> + <div id="note"> + <h2>Note</h2> + <p>All return objects have printing methods. For the single fits, printing +does not output anything in the case no ill-defined parameters are found.</p> + </div> <div id="ref-examples"> <h2>Examples</h2> diff --git a/docs/reference/ilr.html b/docs/reference/ilr.html index 58ae56b0..7c3a2e33 100644 --- a/docs/reference/ilr.html +++ b/docs/reference/ilr.html @@ -18,7 +18,7 @@ transformations."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -45,19 +45,25 @@ transformations."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -89,9 +95,9 @@ transformations.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">ilr</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> - -<span class="fu">invilr</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">ilr</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span> +<span></span> +<span><span class="fu">invilr</span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> @@ -99,10 +105,13 @@ transformations.</p> <dl><dt>x</dt> <dd><p>A numeric vector. Naturally, the forward transformation is only sensible for vectors with all elements being greater than zero.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>The result of the forward or backward transformation. The returned + + +<p>The result of the forward or backward transformation. The returned components always sum to 1 for the case of the inverse log-ratio transformation.</p> </div> @@ -123,39 +132,39 @@ Compositional Data Using Robust Methods. Math Geosci 40 233-248</p> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"><span class="co"># Order matters</span></span> -<span class="r-in"><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.1</span>, <span class="fl">1</span>, <span class="fl">10</span><span class="op">)</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Order matters</span></span></span> +<span class="r-in"><span><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.1</span>, <span class="fl">1</span>, <span class="fl">10</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> [1] -1.628174 -2.820079</span> -<span class="r-in"><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">1</span>, <span class="fl">0.1</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">1</span>, <span class="fl">0.1</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> [1] 1.628174 2.820079</span> -<span class="r-in"><span class="co"># Equal entries give ilr transformations with zeros as elements</span></span> -<span class="r-in"><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span><span class="co"># Equal entries give ilr transformations with zeros as elements</span></span></span> +<span class="r-in"><span><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> [1] 0 0</span> -<span class="r-in"><span class="co"># Almost equal entries give small numbers</span></span> -<span class="r-in"><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.3</span>, <span class="fl">0.4</span>, <span class="fl">0.3</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span><span class="co"># Almost equal entries give small numbers</span></span></span> +<span class="r-in"><span><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.3</span>, <span class="fl">0.4</span>, <span class="fl">0.3</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> [1] -0.2034219 0.1174457</span> -<span class="r-in"><span class="co"># Only the ratio between the numbers counts, not their sum</span></span> -<span class="r-in"><span class="fu">invilr</span><span class="op">(</span><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.7</span>, <span class="fl">0.29</span>, <span class="fl">0.01</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span><span class="co"># Only the ratio between the numbers counts, not their sum</span></span></span> +<span class="r-in"><span><span class="fu">invilr</span><span class="op">(</span><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.7</span>, <span class="fl">0.29</span>, <span class="fl">0.01</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> [1] 0.70 0.29 0.01</span> -<span class="r-in"><span class="fu">invilr</span><span class="op">(</span><span class="fu">ilr</span><span class="op">(</span><span class="fl">2.1</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.7</span>, <span class="fl">0.29</span>, <span class="fl">0.01</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu">invilr</span><span class="op">(</span><span class="fu">ilr</span><span class="op">(</span><span class="fl">2.1</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.7</span>, <span class="fl">0.29</span>, <span class="fl">0.01</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> [1] 0.70 0.29 0.01</span> -<span class="r-in"><span class="co"># Inverse transformation of larger numbers gives unequal elements</span></span> -<span class="r-in"><span class="fu">invilr</span><span class="op">(</span><span class="op">-</span><span class="fl">10</span><span class="op">)</span></span> +<span class="r-in"><span><span class="co"># Inverse transformation of larger numbers gives unequal elements</span></span></span> +<span class="r-in"><span><span class="fu">invilr</span><span class="op">(</span><span class="op">-</span><span class="fl">10</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> [1] 7.213536e-07 9.999993e-01</span> -<span class="r-in"><span class="fu">invilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">10</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu">invilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">10</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> [1] 7.207415e-07 9.991507e-01 8.486044e-04</span> -<span class="r-in"><span class="co"># The sum of the elements of the inverse ilr is 1</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="fu">invilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">10</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span><span class="co"># The sum of the elements of the inverse ilr is 1</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="fu">invilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">-</span><span class="fl">10</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> [1] 1</span> -<span class="r-in"><span class="co"># This is why we do not need all elements of the inverse transformation to go back:</span></span> -<span class="r-in"><span class="va">a</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.1</span>, <span class="fl">0.3</span>, <span class="fl">0.5</span><span class="op">)</span></span> -<span class="r-in"><span class="va">b</span> <span class="op"><-</span> <span class="fu">invilr</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">b</span><span class="op">)</span> <span class="co"># Four elements</span></span> +<span class="r-in"><span><span class="co"># This is why we do not need all elements of the inverse transformation to go back:</span></span></span> +<span class="r-in"><span><span class="va">a</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.1</span>, <span class="fl">0.3</span>, <span class="fl">0.5</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">b</span> <span class="op"><-</span> <span class="fu">invilr</span><span class="op">(</span><span class="va">a</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">b</span><span class="op">)</span> <span class="co"># Four elements</span></span></span> <span class="r-out co"><span class="r-pr">#></span> [1] 4</span> -<span class="r-in"><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">b</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">]</span>, <span class="fl">1</span> <span class="op">-</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">b</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">]</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="co"># Gives c(0.1, 0.3, 0.5)</span></span> +<span class="r-in"><span><span class="fu">ilr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">b</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">]</span>, <span class="fl">1</span> <span class="op">-</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">b</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">]</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="co"># Gives c(0.1, 0.3, 0.5)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> [1] 0.1 0.3 0.5</span> -<span class="r-in"></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -170,7 +179,7 @@ Compositional Data Using Robust Methods. Math Geosci 40 233-248</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/index.html b/docs/reference/index.html index 956648d2..9fddf541 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.1</span> </span> </div> @@ -44,19 +44,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -107,11 +113,15 @@ degradation models and one or more error models</p></td> <p class="section-desc"></p><p>Generic functions introduced by the package</p> </th> </tr></tbody><tbody><tr><td> - <p><code><a href="convergence.html">convergence()</a></code> <code><a href="convergence.html">print(<i><convergence.mmkin></i>)</a></code> </p> + <p><code><a href="parms.html">parms()</a></code> </p> + </td> + <td><p>Extract model parameters</p></td> + </tr><tr><td> + <p><code><a href="status.html">status()</a></code> <code><a href="status.html">print(<i><status.mmkin></i>)</a></code> <code><a href="status.html">print(<i><status.mhmkin></i>)</a></code> </p> </td> - <td><p>Method to get convergence information</p></td> + <td><p>Method to get status information for fit array objects</p></td> </tr><tr><td> - <p><code><a href="illparms.html">illparms()</a></code> <code><a href="illparms.html">print(<i><illparms.mmkin></i>)</a></code> <code><a href="illparms.html">print(<i><illparms.mhmkin></i>)</a></code> </p> + <p><code><a href="illparms.html">illparms()</a></code> <code><a href="illparms.html">print(<i><illparms.mkinfit></i>)</a></code> <code><a href="illparms.html">print(<i><illparms.mmkin></i>)</a></code> <code><a href="illparms.html">print(<i><illparms.saem.mmkin></i>)</a></code> <code><a href="illparms.html">print(<i><illparms.mhmkin></i>)</a></code> </p> </td> <td><p>Method to get the names of ill-defined parameters</p></td> </tr><tr><td> @@ -119,6 +129,10 @@ degradation models and one or more error models</p></td> </td> <td><p>Function to calculate endpoints for further use from kinetic models fitted with mkinfit</p></td> + </tr><tr><td> + <p><code><a href="aw.html">aw()</a></code> </p> + </td> + <td><p>Calculate Akaike weights for model averaging</p></td> </tr></tbody><tbody><tr><th colspan="2"> <h2 id="show-results">Show results <a href="#show-results" class="anchor" aria-hidden="true"></a></h2> <p class="section-desc"></p><p>Functions working with mkinfit objects</p> @@ -132,10 +146,6 @@ with mkinfit</p></td> </td> <td><p>Summary method for class "mkinfit"</p></td> </tr><tr><td> - <p><code><a href="parms.html">parms()</a></code> </p> - </td> - <td><p>Extract model parameters from mkinfit models</p></td> - </tr><tr><td> <p><code><a href="confint.mkinfit.html">confint(<i><mkinfit></i>)</a></code> </p> </td> <td><p>Confidence intervals for parameters of mkinfit objects</p></td> @@ -156,10 +166,6 @@ with mkinfit</p></td> </td> <td><p>Calculate the minimum error to assume in order to pass the variance test</p></td> </tr><tr><td> - <p><code><a href="aw.html">aw()</a></code> </p> - </td> - <td><p>Calculate Akaike weights for model averaging</p></td> - </tr><tr><td> <p><code><a href="CAKE_export.html">CAKE_export()</a></code> </p> </td> <td><p>Export a list of datasets format to a CAKE study file</p></td> @@ -189,11 +195,15 @@ of an mmkin object</p></td> <p class="section-desc"></p><p>Create and work with nonlinear hierarchical models</p> </th> </tr></tbody><tbody><tr><td> + <p><code><a href="read_spreadsheet.html">read_spreadsheet()</a></code> </p> + </td> + <td><p>Read datasets and relevant meta information from a spreadsheet file</p></td> + </tr><tr><td> <p><code><a href="nlme.mmkin.html">nlme(<i><mmkin></i>)</a></code> <code><a href="nlme.mmkin.html">print(<i><nlme.mmkin></i>)</a></code> <code><a href="nlme.mmkin.html">update(<i><nlme.mmkin></i>)</a></code> </p> </td> <td><p>Create an nlme model for an mmkin row object</p></td> </tr><tr><td> - <p><code><a href="saem.html">saem()</a></code> <code><a href="saem.html">print(<i><saem.mmkin></i>)</a></code> <code><a href="saem.html">saemix_model()</a></code> <code><a href="saem.html">saemix_data()</a></code> </p> + <p><code><a href="saem.html">saem()</a></code> <code><a href="saem.html">print(<i><saem.mmkin></i>)</a></code> <code><a href="saem.html">saemix_model()</a></code> <code><a href="saem.html">saemix_data()</a></code> <code><a href="saem.html">parms(<i><saem.mmkin></i>)</a></code> </p> </td> <td><p>Fit nonlinear mixed models with SAEM</p></td> </tr><tr><td> @@ -214,6 +224,14 @@ degradation models and one or more error models</p></td> </td> <td><p>Summary method for class "saem.mmkin"</p></td> </tr><tr><td> + <p><code><a href="anova.saem.mmkin.html">anova(<i><saem.mmkin></i>)</a></code> </p> + </td> + <td><p>Anova method for saem.mmkin objects</p></td> + </tr><tr><td> + <p><code><a href="logLik.saem.mmkin.html">logLik(<i><saem.mmkin></i>)</a></code> </p> + </td> + <td><p>logLik method for saem.mmkin objects</p></td> + </tr><tr><td> <p><code><a href="nlme.html">nlme_function()</a></code> <code><a href="nlme.html">nlme_data()</a></code> </p> </td> <td><p>Helper functions to create nlme models from mmkin row objects</p></td> @@ -233,14 +251,26 @@ degradation models and one or more error models</p></td> <p><code><a href="intervals.saem.mmkin.html">intervals(<i><saem.mmkin></i>)</a></code> </p> </td> <td><p>Confidence intervals for parameters in saem.mmkin objects</p></td> + </tr><tr><td> + <p><code><a href="multistart.html">multistart()</a></code> <code><a href="multistart.html">print(<i><multistart></i>)</a></code> <code><a href="multistart.html">best()</a></code> <code><a href="multistart.html">which.best()</a></code> </p> + </td> + <td><p>Perform a hierarchical model fit with multiple starting values</p></td> + </tr><tr><td> + <p><code><a href="llhist.html">llhist()</a></code> </p> + </td> + <td><p>Plot the distribution of log likelihoods from multistart objects</p></td> + </tr><tr><td> + <p><code><a href="parplot.html">parplot()</a></code> </p> + </td> + <td><p>Plot parameter variability of multistart objects</p></td> </tr></tbody><tbody><tr><th colspan="2"> <h2 id="datasets-and-known-results">Datasets and known results <a href="#datasets-and-known-results" class="anchor" aria-hidden="true"></a></h2> <p class="section-desc"></p> </th> </tr></tbody><tbody><tr><td> - <p><code><a href="focus_soil_moisture.html">focus_soil_moisture</a></code> </p> + <p><code><a href="ds_mixed.html">ds_mixed</a></code> <code><a href="ds_mixed.html">ds_sfo</a></code> <code><a href="ds_mixed.html">ds_fomc</a></code> <code><a href="ds_mixed.html">ds_dfop</a></code> <code><a href="ds_mixed.html">ds_hs</a></code> <code><a href="ds_mixed.html">ds_dfop_sfo</a></code> </p> </td> - <td><p>FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar</p></td> + <td><p>Synthetic data for hierarchical kinetic degradation models</p></td> </tr><tr><td> <p><code><a href="D24_2014.html">D24_2014</a></code> </p> </td> @@ -298,6 +328,10 @@ degradation models and one or more error models</p></td> </td> <td><p>Three experimental datasets from two water sediment systems and one soil</p></td> </tr><tr><td> + <p><code><a href="focus_soil_moisture.html">focus_soil_moisture</a></code> </p> + </td> + <td><p>FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar</p></td> + </tr><tr><td> <p><code><a href="mkinds.html">print(<i><mkinds></i>)</a></code> </p> </td> <td><p>A dataset class for mkin</p></td> @@ -322,10 +356,18 @@ degradation models and one or more error models</p></td> <p class="section-desc"></p> </th> </tr></tbody><tbody><tr><td> + <p><code><a href="tex_listing.html">tex_listing()</a></code> </p> + </td> + <td><p>Wrap the output of a summary function in tex listing environment</p></td> + </tr><tr><td> <p><code><a href="f_time_norm_focus.html">f_time_norm_focus()</a></code> </p> </td> <td><p>Normalisation factors for aerobic soil degradation according to FOCUS guidance</p></td> </tr><tr><td> + <p><code><a href="set_nd_nq.html">set_nd_nq()</a></code> <code><a href="set_nd_nq.html">set_nd_nq_focus()</a></code> </p> + </td> + <td><p>Set non-detects and unquantified values in residue series without replicates</p></td> + </tr><tr><td> <p><code><a href="max_twa_parent.html">max_twa_parent()</a></code> <code><a href="max_twa_parent.html">max_twa_sfo()</a></code> <code><a href="max_twa_parent.html">max_twa_fomc()</a></code> <code><a href="max_twa_parent.html">max_twa_dfop()</a></code> <code><a href="max_twa_parent.html">max_twa_hs()</a></code> </p> </td> <td><p>Function to calculate maximum time weighted average concentrations from diff --git a/docs/reference/intervals.saem.mmkin.html b/docs/reference/intervals.saem.mmkin.html index ae621adc..1547f3af 100644 --- a/docs/reference/intervals.saem.mmkin.html +++ b/docs/reference/intervals.saem.mmkin.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -44,19 +44,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -87,27 +93,36 @@ </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># S3 method for saem.mmkin</span> -<span class="fu"><a href="https://rdrr.io/pkg/nlme/man/intervals.html" class="external-link">intervals</a></span><span class="op">(</span><span class="va">object</span>, level <span class="op">=</span> <span class="fl">0.95</span>, backtransform <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for saem.mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/intervals.html" class="external-link">intervals</a></span><span class="op">(</span><span class="va">object</span>, level <span class="op">=</span> <span class="fl">0.95</span>, backtransform <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>object</dt> <dd><p>The fitted saem.mmkin object</p></dd> + + <dt>level</dt> <dd><p>The confidence level. Must be the default of 0.95 as this is what is available in the saemix object</p></dd> + + <dt>backtransform</dt> <dd><p>In case the model was fitted with mkin transformations, should we backtransform the parameters where a one to one correlation between transformed and backtransformed parameters exists?</p></dd> + + <dt>...</dt> <dd><p>For compatibility with the generic method</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>An object with 'intervals.saem.mmkin' and 'intervals.lme' in the + + +<p>An object with 'intervals.saem.mmkin' and 'intervals.lme' in the class attribute</p> </div> @@ -123,7 +138,7 @@ class attribute</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/llhist.html b/docs/reference/llhist.html new file mode 100644 index 00000000..cc58e481 --- /dev/null +++ b/docs/reference/llhist.html @@ -0,0 +1,151 @@ +<!DOCTYPE html> +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Plot the distribution of log likelihoods from multistart objects — llhist • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Plot the distribution of log likelihoods from multistart objects — llhist"><meta property="og:description" content="Produces a histogram of log-likelihoods. In addition, the likelihood of the +original fit is shown as a red vertical line."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> +<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> +<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + + + <div class="container template-reference-topic"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <div class="container"> + <div class="navbar-header"> + <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> + <span class="sr-only">Toggle navigation</span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + </button> + <span class="navbar-brand"> + <a class="navbar-link" href="../index.html">mkin</a> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + </span> + </div> + + <div id="navbar" class="navbar-collapse collapse"> + <ul class="nav navbar-nav"><li> + <a href="../reference/index.html">Functions and data</a> +</li> +<li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> + Articles + + <span class="caret"></span> + </a> + <ul class="dropdown-menu" role="menu"><li> + <a href="../articles/mkin.html">Introduction to mkin</a> + </li> + <li> + <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> + </li> + <li> + <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> + </li> + <li> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + </li> + <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> + <li> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> +<li> + <a href="../news/index.html">News</a> +</li> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> + <span class="fab fa-github fa-lg"></span> + + </a> +</li> + </ul></div><!--/.nav-collapse --> + </div><!--/.container --> +</div><!--/.navbar --> + + + + </header><div class="row"> + <div class="col-md-9 contents"> + <div class="page-header"> + <h1>Plot the distribution of log likelihoods from multistart objects</h1> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/llhist.R" class="external-link"><code>R/llhist.R</code></a></small> + <div class="hidden name"><code>llhist.Rd</code></div> + </div> + + <div class="ref-description"> + <p>Produces a histogram of log-likelihoods. In addition, the likelihood of the +original fit is shown as a red vertical line.</p> + </div> + + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">llhist</span><span class="op">(</span><span class="va">object</span>, breaks <span class="op">=</span> <span class="st">"Sturges"</span>, lpos <span class="op">=</span> <span class="st">"topleft"</span>, main <span class="op">=</span> <span class="st">""</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>object</dt> +<dd><p>The <a href="multistart.html">multistart</a> object</p></dd> + + +<dt>breaks</dt> +<dd><p>Passed to <a href="https://rdrr.io/r/graphics/hist.html" class="external-link">hist</a></p></dd> + + +<dt>lpos</dt> +<dd><p>Positioning of the legend.</p></dd> + + +<dt>main</dt> +<dd><p>Title of the plot</p></dd> + + +<dt>...</dt> +<dd><p>Passed to <a href="https://rdrr.io/r/graphics/hist.html" class="external-link">hist</a></p></dd> + +</dl></div> + <div id="see-also"> + <h2>See also</h2> + <div class="dont-index"><p><a href="multistart.html">multistart</a></p></div> + </div> + + </div> + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> +</div> + + + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +</div> + + </footer></div> + + + + + + + </body></html> + diff --git a/docs/reference/loftest-3.png b/docs/reference/loftest-3.png Binary files differindex a1a65a61..d897c363 100644 --- a/docs/reference/loftest-3.png +++ b/docs/reference/loftest-3.png diff --git a/docs/reference/loftest-5.png b/docs/reference/loftest-5.png Binary files differindex c441f2ed..0847bbec 100644 --- a/docs/reference/loftest-5.png +++ b/docs/reference/loftest-5.png diff --git a/docs/reference/loftest.html b/docs/reference/loftest.html index ee9b37e4..254b568f 100644 --- a/docs/reference/loftest.html +++ b/docs/reference/loftest.html @@ -20,7 +20,7 @@ lrtest.default from the lmtest package."><!-- mathjax --><script src="https://cd </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -47,19 +47,25 @@ lrtest.default from the lmtest package."><!-- mathjax --><script src="https://cd <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -93,18 +99,21 @@ compares the likelihoods using the likelihood ratio test </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">loftest</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span> - -<span class="co"># S3 method for mkinfit</span> -<span class="fu">loftest</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">loftest</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mkinfit</span></span> +<span><span class="fu">loftest</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>object</dt> <dd><p>A model object with a defined loftest method</p></dd> + + <dt>...</dt> <dd><p>Not used</p></dd> + </dl></div> <div id="details"> <h2>Details</h2> @@ -120,12 +129,12 @@ of replicate samples.</p> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"><span class="va">test_data</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"parent"</span><span class="op">)</span></span> -<span class="r-in"><span class="va">sfo_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">test_data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">sfo_fit</span><span class="op">)</span> <span class="co"># We see a clear pattern in the residuals</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="va">test_data</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"parent"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">sfo_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">test_data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">sfo_fit</span><span class="op">)</span> <span class="co"># We see a clear pattern in the residuals</span></span></span> <span class="r-plt img"><img src="loftest-1.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="fu">loftest</span><span class="op">(</span><span class="va">sfo_fit</span><span class="op">)</span> <span class="co"># We have a clear lack of fit</span></span> +<span class="r-in"><span><span class="fu">loftest</span><span class="op">(</span><span class="va">sfo_fit</span><span class="op">)</span> <span class="co"># We have a clear lack of fit</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model 1: ANOVA with error model const</span> @@ -135,13 +144,13 @@ of replicate samples.</p> <span class="r-out co"><span class="r-pr">#></span> 2 3 -63.954 -7 46.487 7.027e-08 ***</span> <span class="r-out co"><span class="r-pr">#></span> ---</span> <span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> -<span class="r-in"><span class="co">#</span></span> -<span class="r-in"><span class="co"># We try a different model (the one that was used to generate the data)</span></span> -<span class="r-in"><span class="va">dfop_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">test_data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">dfop_fit</span><span class="op">)</span> <span class="co"># We don't see systematic deviations, but heteroscedastic residuals</span></span> +<span class="r-in"><span><span class="co">#</span></span></span> +<span class="r-in"><span><span class="co"># We try a different model (the one that was used to generate the data)</span></span></span> +<span class="r-in"><span><span class="va">dfop_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">test_data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">dfop_fit</span><span class="op">)</span> <span class="co"># We don't see systematic deviations, but heteroscedastic residuals</span></span></span> <span class="r-plt img"><img src="loftest-2.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="co"># therefore we should consider adapting the error model, although we have</span></span> -<span class="r-in"><span class="fu">loftest</span><span class="op">(</span><span class="va">dfop_fit</span><span class="op">)</span> <span class="co"># no lack of fit</span></span> +<span class="r-in"><span><span class="co"># therefore we should consider adapting the error model, although we have</span></span></span> +<span class="r-in"><span><span class="fu">loftest</span><span class="op">(</span><span class="va">dfop_fit</span><span class="op">)</span> <span class="co"># no lack of fit</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model 1: ANOVA with error model const</span> @@ -149,12 +158,12 @@ of replicate samples.</p> <span class="r-out co"><span class="r-pr">#></span> #Df LogLik Df Chisq Pr(>Chisq)</span> <span class="r-out co"><span class="r-pr">#></span> 1 10 -40.710 </span> <span class="r-out co"><span class="r-pr">#></span> 2 5 -42.453 -5 3.485 0.6257</span> -<span class="r-in"><span class="co">#</span></span> -<span class="r-in"><span class="co"># This is the anova model used internally for the comparison</span></span> -<span class="r-in"><span class="va">test_data_anova</span> <span class="op"><-</span> <span class="va">test_data</span></span> -<span class="r-in"><span class="va">test_data_anova</span><span class="op">$</span><span class="va">time</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="va">test_data_anova</span><span class="op">$</span><span class="va">time</span><span class="op">)</span></span> -<span class="r-in"><span class="va">anova_fit</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/lm.html" class="external-link">lm</a></span><span class="op">(</span><span class="va">value</span> <span class="op">~</span> <span class="va">time</span>, data <span class="op">=</span> <span class="va">test_data_anova</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">anova_fit</span><span class="op">)</span></span> +<span class="r-in"><span><span class="co">#</span></span></span> +<span class="r-in"><span><span class="co"># This is the anova model used internally for the comparison</span></span></span> +<span class="r-in"><span><span class="va">test_data_anova</span> <span class="op"><-</span> <span class="va">test_data</span></span></span> +<span class="r-in"><span><span class="va">test_data_anova</span><span class="op">$</span><span class="va">time</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="va">test_data_anova</span><span class="op">$</span><span class="va">time</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">anova_fit</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/lm.html" class="external-link">lm</a></span><span class="op">(</span><span class="va">value</span> <span class="op">~</span> <span class="va">time</span>, data <span class="op">=</span> <span class="va">test_data_anova</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">anova_fit</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Call:</span> <span class="r-out co"><span class="r-pr">#></span> lm(formula = value ~ time, data = test_data_anova)</span> @@ -181,18 +190,18 @@ of replicate samples.</p> <span class="r-out co"><span class="r-pr">#></span> Multiple R-squared: 0.9953, Adjusted R-squared: 0.9912 </span> <span class="r-out co"><span class="r-pr">#></span> F-statistic: 240.5 on 8 and 9 DF, p-value: 1.417e-09</span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/stats/logLik.html" class="external-link">logLik</a></span><span class="op">(</span><span class="va">anova_fit</span><span class="op">)</span> <span class="co"># We get the same likelihood and degrees of freedom</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/logLik.html" class="external-link">logLik</a></span><span class="op">(</span><span class="va">anova_fit</span><span class="op">)</span> <span class="co"># We get the same likelihood and degrees of freedom</span></span></span> <span class="r-out co"><span class="r-pr">#></span> 'log Lik.' -40.71015 (df=10)</span> -<span class="r-in"><span class="co">#</span></span> -<span class="r-in"><span class="va">test_data_2</span> <span class="op"><-</span> <span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span> -<span class="r-in"><span class="va">m_synth_SFO_lin</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M1"</span><span class="op">)</span>,</span> -<span class="r-in"> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M2"</span><span class="op">)</span>,</span> -<span class="r-in"> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span> +<span class="r-in"><span><span class="co">#</span></span></span> +<span class="r-in"><span><span class="va">test_data_2</span> <span class="op"><-</span> <span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span></span> +<span class="r-in"><span><span class="va">m_synth_SFO_lin</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M2"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"><span class="va">sfo_lin_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_SFO_lin</span>, <span class="va">test_data_2</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">sfo_lin_fit</span><span class="op">)</span> <span class="co"># not a good model, we try parallel formation</span></span> +<span class="r-in"><span><span class="va">sfo_lin_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_SFO_lin</span>, <span class="va">test_data_2</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">sfo_lin_fit</span><span class="op">)</span> <span class="co"># not a good model, we try parallel formation</span></span></span> <span class="r-plt img"><img src="loftest-3.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="fu">loftest</span><span class="op">(</span><span class="va">sfo_lin_fit</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu">loftest</span><span class="op">(</span><span class="va">sfo_lin_fit</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model 1: ANOVA with error model const</span> @@ -202,15 +211,15 @@ of replicate samples.</p> <span class="r-out co"><span class="r-pr">#></span> 2 7 -171.927 -21 156.64 < 2.2e-16 ***</span> <span class="r-out co"><span class="r-pr">#></span> ---</span> <span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> -<span class="r-in"><span class="co">#</span></span> -<span class="r-in"><span class="va">m_synth_SFO_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span> -<span class="r-in"> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span> +<span class="r-in"><span><span class="co">#</span></span></span> +<span class="r-in"><span><span class="va">m_synth_SFO_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"><span class="va">sfo_par_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_SFO_par</span>, <span class="va">test_data_2</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">sfo_par_fit</span><span class="op">)</span> <span class="co"># much better for metabolites</span></span> +<span class="r-in"><span><span class="va">sfo_par_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_SFO_par</span>, <span class="va">test_data_2</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">sfo_par_fit</span><span class="op">)</span> <span class="co"># much better for metabolites</span></span></span> <span class="r-plt img"><img src="loftest-4.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="fu">loftest</span><span class="op">(</span><span class="va">sfo_par_fit</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu">loftest</span><span class="op">(</span><span class="va">sfo_par_fit</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model 1: ANOVA with error model const</span> @@ -220,15 +229,15 @@ of replicate samples.</p> <span class="r-out co"><span class="r-pr">#></span> 2 7 -156.331 -21 125.45 < 2.2e-16 ***</span> <span class="r-out co"><span class="r-pr">#></span> ---</span> <span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> -<span class="r-in"><span class="co">#</span></span> -<span class="r-in"><span class="va">m_synth_DFOP_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span> -<span class="r-in"> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span> +<span class="r-in"><span><span class="co">#</span></span></span> +<span class="r-in"><span><span class="va">m_synth_DFOP_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"><span class="va">dfop_par_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_DFOP_par</span>, <span class="va">test_data_2</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">dfop_par_fit</span><span class="op">)</span> <span class="co"># No visual lack of fit</span></span> +<span class="r-in"><span><span class="va">dfop_par_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_DFOP_par</span>, <span class="va">test_data_2</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">dfop_par_fit</span><span class="op">)</span> <span class="co"># No visual lack of fit</span></span></span> <span class="r-plt img"><img src="loftest-5.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="fu">loftest</span><span class="op">(</span><span class="va">dfop_par_fit</span><span class="op">)</span> <span class="co"># no lack of fit found by the test</span></span> +<span class="r-in"><span><span class="fu">loftest</span><span class="op">(</span><span class="va">dfop_par_fit</span><span class="op">)</span> <span class="co"># no lack of fit found by the test</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model 1: ANOVA with error model const</span> @@ -236,13 +245,13 @@ of replicate samples.</p> <span class="r-out co"><span class="r-pr">#></span> #Df LogLik Df Chisq Pr(>Chisq)</span> <span class="r-out co"><span class="r-pr">#></span> 1 28 -93.606 </span> <span class="r-out co"><span class="r-pr">#></span> 2 9 -102.763 -19 18.313 0.5016</span> -<span class="r-in"><span class="co">#</span></span> -<span class="r-in"><span class="co"># The anova model used for comparison in the case of transformation products</span></span> -<span class="r-in"><span class="va">test_data_anova_2</span> <span class="op"><-</span> <span class="va">dfop_par_fit</span><span class="op">$</span><span class="va">data</span></span> -<span class="r-in"><span class="va">test_data_anova_2</span><span class="op">$</span><span class="va">variable</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="va">test_data_anova_2</span><span class="op">$</span><span class="va">variable</span><span class="op">)</span></span> -<span class="r-in"><span class="va">test_data_anova_2</span><span class="op">$</span><span class="va">time</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="va">test_data_anova_2</span><span class="op">$</span><span class="va">time</span><span class="op">)</span></span> -<span class="r-in"><span class="va">anova_fit_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/lm.html" class="external-link">lm</a></span><span class="op">(</span><span class="va">observed</span> <span class="op">~</span> <span class="va">time</span><span class="op">:</span><span class="va">variable</span> <span class="op">-</span> <span class="fl">1</span>, data <span class="op">=</span> <span class="va">test_data_anova_2</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">anova_fit_2</span><span class="op">)</span></span> +<span class="r-in"><span><span class="co">#</span></span></span> +<span class="r-in"><span><span class="co"># The anova model used for comparison in the case of transformation products</span></span></span> +<span class="r-in"><span><span class="va">test_data_anova_2</span> <span class="op"><-</span> <span class="va">dfop_par_fit</span><span class="op">$</span><span class="va">data</span></span></span> +<span class="r-in"><span><span class="va">test_data_anova_2</span><span class="op">$</span><span class="va">variable</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="va">test_data_anova_2</span><span class="op">$</span><span class="va">variable</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">test_data_anova_2</span><span class="op">$</span><span class="va">time</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="va">test_data_anova_2</span><span class="op">$</span><span class="va">time</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">anova_fit_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/lm.html" class="external-link">lm</a></span><span class="op">(</span><span class="va">observed</span> <span class="op">~</span> <span class="va">time</span><span class="op">:</span><span class="va">variable</span> <span class="op">-</span> <span class="fl">1</span>, data <span class="op">=</span> <span class="va">test_data_anova_2</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">anova_fit_2</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Call:</span> <span class="r-out co"><span class="r-pr">#></span> lm(formula = observed ~ time:variable - 1, data = test_data_anova_2)</span> @@ -287,7 +296,7 @@ of replicate samples.</p> <span class="r-out co"><span class="r-pr">#></span> Multiple R-squared: 0.9979, Adjusted R-squared: 0.9957 </span> <span class="r-out co"><span class="r-pr">#></span> F-statistic: 469.2 on 25 and 25 DF, p-value: < 2.2e-16</span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-in"><span class="co"># }</span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> </code></pre></div> </div> </div> @@ -302,7 +311,7 @@ of replicate samples.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/logLik.mkinfit.html b/docs/reference/logLik.mkinfit.html index 9c34d890..76fa4645 100644 --- a/docs/reference/logLik.mkinfit.html +++ b/docs/reference/logLik.mkinfit.html @@ -21,7 +21,7 @@ the error model."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -48,19 +48,25 @@ the error model."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -95,20 +101,25 @@ the error model.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># S3 method for mkinfit</span> -<span class="fu"><a href="https://rdrr.io/r/stats/logLik.html" class="external-link">logLik</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/stats/logLik.html" class="external-link">logLik</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>object</dt> <dd><p>An object of class <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd> + + <dt>...</dt> <dd><p>For compatibility with the generic method</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>An object of class <code><a href="https://rdrr.io/r/stats/logLik.html" class="external-link">logLik</a></code> with the number of estimated + + +<p>An object of class <code><a href="https://rdrr.io/r/stats/logLik.html" class="external-link">logLik</a></code> with the number of estimated parameters (degradation model parameters plus variance model parameters) as attribute.</p> </div> @@ -130,24 +141,24 @@ and the fitted error model parameters.</p> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"> <span class="co"># \dontrun{</span></span> -<span class="r-in"> <span class="va">sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span> -<span class="r-in"> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span><span class="op">)</span>,</span> -<span class="r-in"> m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span></span> -<span class="r-in"> <span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span> <span class="va">sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"> <span class="va">d_t</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span></span> -<span class="r-in"> <span class="va">f_nw</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">sfo_sfo</span>, <span class="va">d_t</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="co"># no weighting (weights are unity)</span></span> -<span class="r-in"> <span class="va">f_obs</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_nw</span>, error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span></span> -<span class="r-in"> <span class="va">f_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_nw</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_nw</span>, <span class="va">f_obs</span>, <span class="va">f_tc</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="va">d_t</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">f_nw</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">sfo_sfo</span>, <span class="va">d_t</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="co"># no weighting (weights are unity)</span></span></span> +<span class="r-in"><span> <span class="va">f_obs</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_nw</span>, error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">f_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_nw</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_nw</span>, <span class="va">f_obs</span>, <span class="va">f_tc</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> df AIC</span> <span class="r-out co"><span class="r-pr">#></span> f_nw 5 204.4486</span> <span class="r-out co"><span class="r-pr">#></span> f_obs 6 205.8727</span> <span class="r-out co"><span class="r-pr">#></span> f_tc 6 141.9656</span> -<span class="r-in"> <span class="co"># }</span></span> -<span class="r-in"></span> +<span class="r-in"><span> <span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -162,7 +173,7 @@ and the fitted error model parameters.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/logLik.saem.mmkin.html b/docs/reference/logLik.saem.mmkin.html new file mode 100644 index 00000000..36ba6957 --- /dev/null +++ b/docs/reference/logLik.saem.mmkin.html @@ -0,0 +1,138 @@ +<!DOCTYPE html> +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>logLik method for saem.mmkin objects — logLik.saem.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" 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href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="logLik method for saem.mmkin objects — logLik.saem.mmkin"><meta property="og:description" content="logLik method for saem.mmkin objects"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> +<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> +<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + + + <div class="container template-reference-topic"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <div class="container"> + <div class="navbar-header"> + <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> + <span class="sr-only">Toggle navigation</span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + </button> + <span class="navbar-brand"> + <a class="navbar-link" href="../index.html">mkin</a> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + </span> + </div> + + <div id="navbar" class="navbar-collapse collapse"> + <ul class="nav navbar-nav"><li> + <a href="../reference/index.html">Functions and data</a> +</li> +<li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> + Articles + + <span class="caret"></span> + </a> + <ul class="dropdown-menu" role="menu"><li> + <a href="../articles/mkin.html">Introduction to mkin</a> + </li> + <li> + <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> + </li> + <li> + <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> + </li> + <li> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + </li> + <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> + <li> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> +<li> + <a href="../news/index.html">News</a> +</li> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> + <span class="fab fa-github fa-lg"></span> + + </a> +</li> + </ul></div><!--/.nav-collapse --> + </div><!--/.container --> +</div><!--/.navbar --> + + + + </header><div class="row"> + <div class="col-md-9 contents"> + <div class="page-header"> + <h1>logLik method for saem.mmkin objects</h1> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/saem.R" class="external-link"><code>R/saem.R</code></a></small> + <div class="hidden name"><code>logLik.saem.mmkin.Rd</code></div> + </div> + + <div class="ref-description"> + <p>logLik method for saem.mmkin objects</p> + </div> + + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for saem.mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/stats/logLik.html" class="external-link">logLik</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span>, method <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"is"</span>, <span class="st">"lin"</span>, <span class="st">"gq"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>object</dt> +<dd><p>The fitted <a href="saem.html">saem.mmkin</a> object</p></dd> + + +<dt>...</dt> +<dd><p>Passed to <a href="https://rdrr.io/pkg/saemix/man/logLik.html" class="external-link">saemix::logLik.SaemixObject</a></p></dd> + + +<dt>method</dt> +<dd><p>Passed to <a href="https://rdrr.io/pkg/saemix/man/logLik.html" class="external-link">saemix::logLik.SaemixObject</a></p></dd> + +</dl></div> + + </div> + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> +</div> + + + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +</div> + + </footer></div> + + + + + + + </body></html> + diff --git a/docs/reference/logistic.solution.html b/docs/reference/logistic.solution.html index b09d3a69..a63b1b1b 100644 --- a/docs/reference/logistic.solution.html +++ b/docs/reference/logistic.solution.html @@ -18,7 +18,7 @@ an increasing rate constant, supposedly caused by microbial growth"><!-- mathjax </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -45,19 +45,25 @@ an increasing rate constant, supposedly caused by microbial growth"><!-- mathjax <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -89,25 +95,36 @@ an increasing rate constant, supposedly caused by microbial growth</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">logistic.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">kmax</span>, <span class="va">k0</span>, <span class="va">r</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">logistic.solution</span><span class="op">(</span><span class="va">t</span>, <span class="va">parent_0</span>, <span class="va">kmax</span>, <span class="va">k0</span>, <span class="va">r</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>t</dt> <dd><p>Time.</p></dd> + + <dt>parent_0</dt> <dd><p>Starting value for the response variable at time zero.</p></dd> + + <dt>kmax</dt> <dd><p>Maximum rate constant.</p></dd> + + <dt>k0</dt> <dd><p>Minimum rate constant effective at time zero.</p></dd> + + <dt>r</dt> <dd><p>Growth rate of the increase in the rate constant.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>The value of the response variable at time <code>t</code>.</p> + + +<p>The value of the response variable at time <code>t</code>.</p> </div> <div id="note"> <h2>Note</h2> @@ -140,57 +157,57 @@ Version 1.1, 18 December 2014 <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"> <span class="co"># Reproduce the plot on page 57 of FOCUS (2014)</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">logistic.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.08</span>, <span class="fl">0.0001</span>, <span class="fl">0.2</span><span class="op">)</span>,</span> -<span class="r-in"> from <span class="op">=</span> <span class="fl">0</span>, to <span class="op">=</span> <span class="fl">100</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">100</span><span class="op">)</span>,</span> -<span class="r-in"> xlab <span class="op">=</span> <span class="st">"Time"</span>, ylab <span class="op">=</span> <span class="st">"Residue"</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">logistic.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.08</span>, <span class="fl">0.0001</span>, <span class="fl">0.4</span><span class="op">)</span>,</span> -<span class="r-in"> from <span class="op">=</span> <span class="fl">0</span>, to <span class="op">=</span> <span class="fl">100</span>, add <span class="op">=</span> <span class="cn">TRUE</span>, lty <span class="op">=</span> <span class="fl">2</span>, col <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">logistic.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.08</span>, <span class="fl">0.0001</span>, <span class="fl">0.8</span><span class="op">)</span>,</span> -<span class="r-in"> from <span class="op">=</span> <span class="fl">0</span>, to <span class="op">=</span> <span class="fl">100</span>, add <span class="op">=</span> <span class="cn">TRUE</span>, lty <span class="op">=</span> <span class="fl">3</span>, col <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">logistic.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.08</span>, <span class="fl">0.001</span>, <span class="fl">0.2</span><span class="op">)</span>,</span> -<span class="r-in"> from <span class="op">=</span> <span class="fl">0</span>, to <span class="op">=</span> <span class="fl">100</span>, add <span class="op">=</span> <span class="cn">TRUE</span>, lty <span class="op">=</span> <span class="fl">4</span>, col <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">logistic.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.08</span>, <span class="fl">0.08</span>, <span class="fl">0.2</span><span class="op">)</span>,</span> -<span class="r-in"> from <span class="op">=</span> <span class="fl">0</span>, to <span class="op">=</span> <span class="fl">100</span>, add <span class="op">=</span> <span class="cn">TRUE</span>, lty <span class="op">=</span> <span class="fl">5</span>, col <span class="op">=</span> <span class="fl">5</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></span><span class="op">(</span><span class="st">"topright"</span>, inset <span class="op">=</span> <span class="fl">0.05</span>,</span> -<span class="r-in"> legend <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="st">"k0 = "</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.0001</span>, <span class="fl">0.0001</span>, <span class="fl">0.0001</span>, <span class="fl">0.001</span>, <span class="fl">0.08</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="st">", r = "</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.2</span>, <span class="fl">0.4</span>, <span class="fl">0.8</span>, <span class="fl">0.2</span>, <span class="fl">0.2</span><span class="op">)</span><span class="op">)</span>,</span> -<span class="r-in"> lty <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">5</span>, col <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># Reproduce the plot on page 57 of FOCUS (2014)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">logistic.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.08</span>, <span class="fl">0.0001</span>, <span class="fl">0.2</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> from <span class="op">=</span> <span class="fl">0</span>, to <span class="op">=</span> <span class="fl">100</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">100</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> xlab <span class="op">=</span> <span class="st">"Time"</span>, ylab <span class="op">=</span> <span class="st">"Residue"</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">logistic.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.08</span>, <span class="fl">0.0001</span>, <span class="fl">0.4</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> from <span class="op">=</span> <span class="fl">0</span>, to <span class="op">=</span> <span class="fl">100</span>, add <span class="op">=</span> <span class="cn">TRUE</span>, lty <span class="op">=</span> <span class="fl">2</span>, col <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">logistic.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.08</span>, <span class="fl">0.0001</span>, <span class="fl">0.8</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> from <span class="op">=</span> <span class="fl">0</span>, to <span class="op">=</span> <span class="fl">100</span>, add <span class="op">=</span> <span class="cn">TRUE</span>, lty <span class="op">=</span> <span class="fl">3</span>, col <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">logistic.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.08</span>, <span class="fl">0.001</span>, <span class="fl">0.2</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> from <span class="op">=</span> <span class="fl">0</span>, to <span class="op">=</span> <span class="fl">100</span>, add <span class="op">=</span> <span class="cn">TRUE</span>, lty <span class="op">=</span> <span class="fl">4</span>, col <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu">logistic.solution</span><span class="op">(</span><span class="va">x</span>, <span class="fl">100</span>, <span class="fl">0.08</span>, <span class="fl">0.08</span>, <span class="fl">0.2</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> from <span class="op">=</span> <span class="fl">0</span>, to <span class="op">=</span> <span class="fl">100</span>, add <span class="op">=</span> <span class="cn">TRUE</span>, lty <span class="op">=</span> <span class="fl">5</span>, col <span class="op">=</span> <span class="fl">5</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></span><span class="op">(</span><span class="st">"topright"</span>, inset <span class="op">=</span> <span class="fl">0.05</span>,</span></span> +<span class="r-in"><span> legend <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="st">"k0 = "</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.0001</span>, <span class="fl">0.0001</span>, <span class="fl">0.0001</span>, <span class="fl">0.001</span>, <span class="fl">0.08</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="st">", r = "</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.2</span>, <span class="fl">0.4</span>, <span class="fl">0.8</span>, <span class="fl">0.2</span>, <span class="fl">0.2</span><span class="op">)</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> lty <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">5</span>, col <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="logistic.solution-1.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> -<span class="r-in"> <span class="co"># Fit with synthetic data</span></span> -<span class="r-in"> <span class="va">logistic</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"logistic"</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"> <span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span> -<span class="r-in"> <span class="va">parms_logistic</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>kmax <span class="op">=</span> <span class="fl">0.08</span>, k0 <span class="op">=</span> <span class="fl">0.0001</span>, r <span class="op">=</span> <span class="fl">0.2</span><span class="op">)</span></span> -<span class="r-in"> <span class="va">d_logistic</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">logistic</span>,</span> -<span class="r-in"> <span class="va">parms_logistic</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="va">sampling_times</span><span class="op">)</span></span> -<span class="r-in"> <span class="va">d_2_1</span> <span class="op"><-</span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_logistic</span>,</span> -<span class="r-in"> sdfunc <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="sigma_twocomp.html">sigma_twocomp</a></span><span class="op">(</span><span class="va">x</span>, <span class="fl">0.5</span>, <span class="fl">0.07</span><span class="op">)</span>,</span> -<span class="r-in"> n <span class="op">=</span> <span class="fl">1</span>, reps <span class="op">=</span> <span class="fl">2</span>, digits <span class="op">=</span> <span class="fl">5</span>, LOD <span class="op">=</span> <span class="fl">0.1</span>, seed <span class="op">=</span> <span class="fl">123456</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span> -<span class="r-in"></span> -<span class="r-in"> <span class="va">m</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"logistic"</span>, <span class="va">d_2_1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># Fit with synthetic data</span></span></span> +<span class="r-in"><span> <span class="va">logistic</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"logistic"</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">parms_logistic</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>kmax <span class="op">=</span> <span class="fl">0.08</span>, k0 <span class="op">=</span> <span class="fl">0.0001</span>, r <span class="op">=</span> <span class="fl">0.2</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">d_logistic</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">logistic</span>,</span></span> +<span class="r-in"><span> <span class="va">parms_logistic</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="va">sampling_times</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">d_2_1</span> <span class="op"><-</span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_logistic</span>,</span></span> +<span class="r-in"><span> sdfunc <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="sigma_twocomp.html">sigma_twocomp</a></span><span class="op">(</span><span class="va">x</span>, <span class="fl">0.5</span>, <span class="fl">0.07</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> n <span class="op">=</span> <span class="fl">1</span>, reps <span class="op">=</span> <span class="fl">2</span>, digits <span class="op">=</span> <span class="fl">5</span>, LOD <span class="op">=</span> <span class="fl">0.1</span>, seed <span class="op">=</span> <span class="fl">123456</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="va">m</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"logistic"</span>, <span class="va">d_2_1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="logistic.solution-2.png" alt="" width="700" height="433"></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Estimate se_notrans t value Pr(>t) Lower</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 1.057896e+02 1.9023449649 55.610120 3.768361e-16 1.016451e+02</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 1.057896e+02 1.9023449590 55.610120 3.768360e-16 1.016451e+02</span> <span class="r-out co"><span class="r-pr">#></span> kmax 6.398190e-02 0.0143201029 4.467978 3.841828e-04 3.929235e-02</span> -<span class="r-out co"><span class="r-pr">#></span> k0 1.612775e-04 0.0005866813 0.274898 3.940351e-01 5.846685e-08</span> -<span class="r-out co"><span class="r-pr">#></span> r 2.263946e-01 0.1718110773 1.317695 1.061044e-01 4.335843e-02</span> +<span class="r-out co"><span class="r-pr">#></span> k0 1.612775e-04 0.0005866813 0.274898 3.940351e-01 5.846688e-08</span> +<span class="r-out co"><span class="r-pr">#></span> r 2.263946e-01 0.1718110662 1.317695 1.061043e-01 4.335843e-02</span> <span class="r-out co"><span class="r-pr">#></span> sigma 5.332935e+00 0.9145907310 5.830952 4.036926e-05 3.340213e+00</span> <span class="r-out co"><span class="r-pr">#></span> Upper</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 109.9341588</span> <span class="r-out co"><span class="r-pr">#></span> kmax 0.1041853</span> -<span class="r-out co"><span class="r-pr">#></span> k0 0.4448750</span> -<span class="r-out co"><span class="r-pr">#></span> r 1.1821121</span> +<span class="r-out co"><span class="r-pr">#></span> k0 0.4448749</span> +<span class="r-out co"><span class="r-pr">#></span> r 1.1821120</span> <span class="r-out co"><span class="r-pr">#></span> sigma 7.3256566</span> -<span class="r-in"> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">m</span><span class="op">)</span><span class="op">$</span><span class="va">distimes</span></span> +<span class="r-in"><span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">m</span><span class="op">)</span><span class="op">$</span><span class="va">distimes</span></span></span> <span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50_k0 DT50_kmax</span> -<span class="r-out co"><span class="r-pr">#></span> parent 36.86533 62.41511 4297.854 10.83349</span> -<span class="r-in"></span> +<span class="r-out co"><span class="r-pr">#></span> parent 36.86533 62.41511 4297.853 10.83349</span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -205,7 +222,7 @@ Version 1.1, 18 December 2014 </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/lrtest.mkinfit.html b/docs/reference/lrtest.mkinfit.html index bc8dab67..a053a032 100644 --- a/docs/reference/lrtest.mkinfit.html +++ b/docs/reference/lrtest.mkinfit.html @@ -21,7 +21,7 @@ and can be expressed by fixing the parameters of the other."><!-- mathjax --><sc </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -48,19 +48,25 @@ and can be expressed by fixing the parameters of the other."><!-- mathjax --><sc <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -95,11 +101,11 @@ and can be expressed by fixing the parameters of the other.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># S3 method for mkinfit</span> -<span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">object</span>, object_2 <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span> - -<span class="co"># S3 method for mmkin</span> -<span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span> +<span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">object</span>, object_2 <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> @@ -107,11 +113,16 @@ and can be expressed by fixing the parameters of the other.</p> <dl><dt>object</dt> <dd><p>An <code><a href="mkinfit.html">mkinfit</a></code> object, or an <code><a href="mmkin.html">mmkin</a></code> column object containing two fits to the same data.</p></dd> + + <dt>object_2</dt> <dd><p>Optionally, another mkinfit object fitted to the same data.</p></dd> + + <dt>...</dt> <dd><p>Argument to <code><a href="mkinfit.html">mkinfit</a></code>, passed to <code><a href="update.mkinfit.html">update.mkinfit</a></code> for creating the alternative fitted object.</p></dd> + </dl></div> <div id="details"> <h2>Details</h2> @@ -125,11 +136,11 @@ lower number of fitted parameters (null hypothesis).</p> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"><span class="va">test_data</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"parent"</span><span class="op">)</span></span> -<span class="r-in"><span class="va">sfo_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">test_data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="va">dfop_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">test_data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">dfop_fit</span>, <span class="va">sfo_fit</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="va">test_data</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"parent"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">sfo_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">test_data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">dfop_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">test_data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">dfop_fit</span>, <span class="va">sfo_fit</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model 1: DFOP with error model const</span> @@ -139,7 +150,7 @@ lower number of fitted parameters (null hypothesis).</p> <span class="r-out co"><span class="r-pr">#></span> 2 3 -63.954 -2 43.002 4.594e-10 ***</span> <span class="r-out co"><span class="r-pr">#></span> ---</span> <span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">sfo_fit</span>, <span class="va">dfop_fit</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">sfo_fit</span>, <span class="va">dfop_fit</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model 1: DFOP with error model const</span> @@ -149,14 +160,14 @@ lower number of fitted parameters (null hypothesis).</p> <span class="r-out co"><span class="r-pr">#></span> 2 3 -63.954 -2 43.002 4.594e-10 ***</span> <span class="r-out co"><span class="r-pr">#></span> ---</span> <span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># The following two examples are commented out as they fail during</span></span> -<span class="r-in"><span class="co"># generation of the static help pages by pkgdown</span></span> -<span class="r-in"><span class="co">#lrtest(dfop_fit, error_model = "tc")</span></span> -<span class="r-in"><span class="co">#lrtest(dfop_fit, fixed_parms = c(k2 = 0))</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># However, this equivalent syntax also works for static help pages</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">dfop_fit</span>, <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">dfop_fit</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># The following two examples are commented out as they fail during</span></span></span> +<span class="r-in"><span><span class="co"># generation of the static help pages by pkgdown</span></span></span> +<span class="r-in"><span><span class="co">#lrtest(dfop_fit, error_model = "tc")</span></span></span> +<span class="r-in"><span><span class="co">#lrtest(dfop_fit, fixed_parms = c(k2 = 0))</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># However, this equivalent syntax also works for static help pages</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">dfop_fit</span>, <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">dfop_fit</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model 1: DFOP with error model tc</span> @@ -166,7 +177,7 @@ lower number of fitted parameters (null hypothesis).</p> <span class="r-out co"><span class="r-pr">#></span> 2 5 -42.453 -1 15.731 7.302e-05 ***</span> <span class="r-out co"><span class="r-pr">#></span> ---</span> <span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">dfop_fit</span>, <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">dfop_fit</span>, fixed_parms <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest</a></span><span class="op">(</span><span class="va">dfop_fit</span>, <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">dfop_fit</span>, fixed_parms <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model 1: DFOP with error model const</span> @@ -176,7 +187,7 @@ lower number of fitted parameters (null hypothesis).</p> <span class="r-out co"><span class="r-pr">#></span> 2 4 -57.340 -1 29.776 4.851e-08 ***</span> <span class="r-out co"><span class="r-pr">#></span> ---</span> <span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> -<span class="r-in"><span class="co"># }</span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> </code></pre></div> </div> </div> @@ -191,7 +202,7 @@ lower number of fitted parameters (null hypothesis).</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/max_twa_parent.html b/docs/reference/max_twa_parent.html index 69da881b..3c8e1662 100644 --- a/docs/reference/max_twa_parent.html +++ b/docs/reference/max_twa_parent.html @@ -23,7 +23,7 @@ soil section of the FOCUS guidance."><!-- mathjax --><script src="https://cdnjs. </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -50,19 +50,25 @@ soil section of the FOCUS guidance."><!-- mathjax --><script src="https://cdnjs. <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -98,49 +104,72 @@ soil section of the FOCUS guidance.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">max_twa_parent</span><span class="op">(</span><span class="va">fit</span>, <span class="va">windows</span><span class="op">)</span> - -<span class="fu">max_twa_sfo</span><span class="op">(</span>M0 <span class="op">=</span> <span class="fl">1</span>, <span class="va">k</span>, <span class="va">t</span><span class="op">)</span> - -<span class="fu">max_twa_fomc</span><span class="op">(</span>M0 <span class="op">=</span> <span class="fl">1</span>, <span class="va">alpha</span>, <span class="va">beta</span>, <span class="va">t</span><span class="op">)</span> - -<span class="fu">max_twa_dfop</span><span class="op">(</span>M0 <span class="op">=</span> <span class="fl">1</span>, <span class="va">k1</span>, <span class="va">k2</span>, <span class="va">g</span>, <span class="va">t</span><span class="op">)</span> - -<span class="fu">max_twa_hs</span><span class="op">(</span>M0 <span class="op">=</span> <span class="fl">1</span>, <span class="va">k1</span>, <span class="va">k2</span>, <span class="va">tb</span>, <span class="va">t</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">max_twa_parent</span><span class="op">(</span><span class="va">fit</span>, <span class="va">windows</span><span class="op">)</span></span> +<span></span> +<span><span class="fu">max_twa_sfo</span><span class="op">(</span>M0 <span class="op">=</span> <span class="fl">1</span>, <span class="va">k</span>, <span class="va">t</span><span class="op">)</span></span> +<span></span> +<span><span class="fu">max_twa_fomc</span><span class="op">(</span>M0 <span class="op">=</span> <span class="fl">1</span>, <span class="va">alpha</span>, <span class="va">beta</span>, <span class="va">t</span><span class="op">)</span></span> +<span></span> +<span><span class="fu">max_twa_dfop</span><span class="op">(</span>M0 <span class="op">=</span> <span class="fl">1</span>, <span class="va">k1</span>, <span class="va">k2</span>, <span class="va">g</span>, <span class="va">t</span><span class="op">)</span></span> +<span></span> +<span><span class="fu">max_twa_hs</span><span class="op">(</span>M0 <span class="op">=</span> <span class="fl">1</span>, <span class="va">k1</span>, <span class="va">k2</span>, <span class="va">tb</span>, <span class="va">t</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>fit</dt> <dd><p>An object of class <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd> + + <dt>windows</dt> <dd><p>The width of the time windows for which the TWAs should be calculated.</p></dd> + + <dt>M0</dt> <dd><p>The initial concentration for which the maximum time weighted average over the decline curve should be calculated. The default is to use a value of 1, which means that a relative maximum time weighted average factor (f_twa) is calculated.</p></dd> + + <dt>k</dt> <dd><p>The rate constant in the case of SFO kinetics.</p></dd> + + <dt>t</dt> <dd><p>The width of the time window.</p></dd> + + <dt>alpha</dt> <dd><p>Parameter of the FOMC model.</p></dd> + + <dt>beta</dt> <dd><p>Parameter of the FOMC model.</p></dd> + + <dt>k1</dt> <dd><p>The first rate constant of the DFOP or the HS kinetics.</p></dd> + + <dt>k2</dt> <dd><p>The second rate constant of the DFOP or the HS kinetics.</p></dd> + + <dt>g</dt> <dd><p>Parameter of the DFOP model.</p></dd> + + <dt>tb</dt> <dd><p>Parameter of the HS model.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>For <code>max_twa_parent</code>, a numeric vector, named using the + + +<p>For <code>max_twa_parent</code>, a numeric vector, named using the <code>windows</code> argument. For the other functions, a numeric vector of length one (also known as 'a number').</p> </div> @@ -159,12 +188,12 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"> <span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu">max_twa_parent</span><span class="op">(</span><span class="va">fit</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">7</span>, <span class="fl">21</span><span class="op">)</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu">max_twa_parent</span><span class="op">(</span><span class="va">fit</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">7</span>, <span class="fl">21</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> 7 21 </span> <span class="r-out co"><span class="r-pr">#></span> 34.71343 18.22124 </span> -<span class="r-in"></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -179,7 +208,7 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/mccall81_245T.html b/docs/reference/mccall81_245T.html index 97ac647e..0460e376 100644 --- a/docs/reference/mccall81_245T.html +++ b/docs/reference/mccall81_245T.html @@ -19,7 +19,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -46,19 +46,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -91,7 +97,7 @@ </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="va">mccall81_245T</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">mccall81_245T</span></span></code></pre></div> </div> <div id="format"> @@ -122,80 +128,80 @@ <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"> <span class="va">SFO_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>T245 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"phenol"</span><span class="op">)</span>,</span> -<span class="r-in"> phenol <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"anisole"</span><span class="op">)</span>,</span> -<span class="r-in"> anisole <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span> <span class="va">SFO_SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>T245 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"phenol"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> phenol <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"anisole"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> anisole <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"> <span class="co"># \dontrun{</span></span> -<span class="r-in"> <span class="va">fit.1</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO_SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">mccall81_245T</span>, <span class="va">soil</span> <span class="op">==</span> <span class="st">"Commerce"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span> <span class="va">fit.1</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO_SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">mccall81_245T</span>, <span class="va">soil</span> <span class="op">==</span> <span class="st">"Commerce"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.1</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.1</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Estimate se_notrans t value Pr(>t)</span> -<span class="r-out co"><span class="r-pr">#></span> T245_0 1.038550e+02 2.1847074929 47.537272 4.472189e-18</span> +<span class="r-out co"><span class="r-pr">#></span> T245_0 1.038550e+02 2.1847074945 47.537272 4.472189e-18</span> <span class="r-out co"><span class="r-pr">#></span> k_T245 4.337042e-02 0.0018983965 22.845818 2.276911e-13</span> -<span class="r-out co"><span class="r-pr">#></span> k_phenol 4.050581e-01 0.2986993439 1.356073 9.756988e-02</span> -<span class="r-out co"><span class="r-pr">#></span> k_anisole 6.678742e-03 0.0008021439 8.326114 2.623176e-07</span> -<span class="r-out co"><span class="r-pr">#></span> f_T245_to_phenol 6.227599e-01 0.3985340365 1.562627 6.949413e-02</span> -<span class="r-out co"><span class="r-pr">#></span> f_phenol_to_anisole 1.000000e+00 0.6718439498 1.488441 7.867788e-02</span> -<span class="r-out co"><span class="r-pr">#></span> sigma 2.514628e+00 0.4907558786 5.123989 6.233156e-05</span> +<span class="r-out co"><span class="r-pr">#></span> k_phenol 4.050581e-01 0.2986993563 1.356073 9.756989e-02</span> +<span class="r-out co"><span class="r-pr">#></span> k_anisole 6.678742e-03 0.0008021439 8.326114 2.623177e-07</span> +<span class="r-out co"><span class="r-pr">#></span> f_T245_to_phenol 6.227599e-01 0.3985340558 1.562627 6.949413e-02</span> +<span class="r-out co"><span class="r-pr">#></span> f_phenol_to_anisole 1.000000e+00 0.6718439825 1.488441 7.867789e-02</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 2.514628e+00 0.4907558883 5.123989 6.233157e-05</span> <span class="r-out co"><span class="r-pr">#></span> Lower Upper</span> -<span class="r-out co"><span class="r-pr">#></span> T245_0 99.246061401 1.084640e+02</span> +<span class="r-out co"><span class="r-pr">#></span> T245_0 99.246061385 1.084640e+02</span> <span class="r-out co"><span class="r-pr">#></span> k_T245 0.039631621 4.746194e-02</span> -<span class="r-out co"><span class="r-pr">#></span> k_phenol 0.218013878 7.525762e-01</span> +<span class="r-out co"><span class="r-pr">#></span> k_phenol 0.218013879 7.525762e-01</span> <span class="r-out co"><span class="r-pr">#></span> k_anisole 0.005370739 8.305299e-03</span> <span class="r-out co"><span class="r-pr">#></span> f_T245_to_phenol 0.547559081 6.924813e-01</span> <span class="r-out co"><span class="r-pr">#></span> f_phenol_to_anisole 0.000000000 1.000000e+00</span> <span class="r-out co"><span class="r-pr">#></span> sigma 1.706607296 3.322649e+00</span> -<span class="r-in"> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fit.1</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fit.1</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> $ff</span> <span class="r-out co"><span class="r-pr">#></span> T245_phenol T245_sink phenol_anisole phenol_sink </span> -<span class="r-out co"><span class="r-pr">#></span> 6.227599e-01 3.772401e-01 1.000000e+00 3.195366e-10 </span> +<span class="r-out co"><span class="r-pr">#></span> 6.227599e-01 3.772401e-01 1.000000e+00 3.773626e-10 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> $distimes</span> <span class="r-out co"><span class="r-pr">#></span> DT50 DT90</span> <span class="r-out co"><span class="r-pr">#></span> T245 15.982025 53.09114</span> <span class="r-out co"><span class="r-pr">#></span> phenol 1.711229 5.68458</span> -<span class="r-out co"><span class="r-pr">#></span> anisole 103.784092 344.76329</span> +<span class="r-out co"><span class="r-pr">#></span> anisole 103.784093 344.76329</span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-in"> <span class="co"># formation fraction from phenol to anisol is practically 1. As we cannot</span></span> -<span class="r-in"> <span class="co"># fix formation fractions when using the ilr transformation, we can turn of</span></span> -<span class="r-in"> <span class="co"># the sink in the model generation</span></span> -<span class="r-in"> <span class="va">SFO_SFO_SFO_2</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>T245 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"phenol"</span><span class="op">)</span>,</span> -<span class="r-in"> phenol <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"anisole"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> -<span class="r-in"> anisole <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="co"># formation fraction from phenol to anisol is practically 1. As we cannot</span></span></span> +<span class="r-in"><span> <span class="co"># fix formation fractions when using the ilr transformation, we can turn of</span></span></span> +<span class="r-in"><span> <span class="co"># the sink in the model generation</span></span></span> +<span class="r-in"><span> <span class="va">SFO_SFO_SFO_2</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>T245 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"phenol"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> phenol <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"anisole"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> anisole <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"> <span class="va">fit.2</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO_SFO_2</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">mccall81_245T</span>, <span class="va">soil</span> <span class="op">==</span> <span class="st">"Commerce"</span><span class="op">)</span>,</span> -<span class="r-in"> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="va">fit.2</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO_SFO_2</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">mccall81_245T</span>, <span class="va">soil</span> <span class="op">==</span> <span class="st">"Commerce"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.2</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.2</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Estimate se_notrans t value Pr(>t) Lower</span> -<span class="r-out co"><span class="r-pr">#></span> T245_0 1.038550e+02 2.1623653038 48.028439 4.993108e-19 99.271020197</span> -<span class="r-out co"><span class="r-pr">#></span> k_T245 4.337042e-02 0.0018343667 23.643268 3.573556e-14 0.039650977</span> -<span class="r-out co"><span class="r-pr">#></span> k_phenol 4.050583e-01 0.1177237899 3.440751 1.679257e-03 0.218746592</span> -<span class="r-out co"><span class="r-pr">#></span> k_anisole 6.678741e-03 0.0006829745 9.778903 1.872895e-08 0.005377082</span> -<span class="r-out co"><span class="r-pr">#></span> f_T245_to_phenol 6.227599e-01 0.0342197865 18.198824 2.039410e-12 0.547975622</span> +<span class="r-out co"><span class="r-pr">#></span> T245_0 1.038550e+02 2.1623653066 48.028439 4.993108e-19 99.271020284</span> +<span class="r-out co"><span class="r-pr">#></span> k_T245 4.337042e-02 0.0018343666 23.643268 3.573556e-14 0.039650976</span> +<span class="r-out co"><span class="r-pr">#></span> k_phenol 4.050582e-01 0.1177237473 3.440752 1.679254e-03 0.218746587</span> +<span class="r-out co"><span class="r-pr">#></span> k_anisole 6.678742e-03 0.0006829745 9.778903 1.872894e-08 0.005377083</span> +<span class="r-out co"><span class="r-pr">#></span> f_T245_to_phenol 6.227599e-01 0.0342197875 18.198824 2.039411e-12 0.547975637</span> <span class="r-out co"><span class="r-pr">#></span> sigma 2.514628e+00 0.3790944250 6.633250 2.875782e-06 1.710983655</span> <span class="r-out co"><span class="r-pr">#></span> Upper</span> -<span class="r-out co"><span class="r-pr">#></span> T245_0 1.084390e+02</span> -<span class="r-out co"><span class="r-pr">#></span> k_T245 4.743877e-02</span> -<span class="r-out co"><span class="r-pr">#></span> k_phenol 7.500560e-01</span> -<span class="r-out co"><span class="r-pr">#></span> k_anisole 8.295499e-03</span> -<span class="r-out co"><span class="r-pr">#></span> f_T245_to_phenol 6.921231e-01</span> -<span class="r-out co"><span class="r-pr">#></span> sigma 3.318272e+00</span> -<span class="r-in"> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fit.1</span><span class="op">)</span></span> +<span class="r-out co"><span class="r-pr">#></span> T245_0 108.43904074</span> +<span class="r-out co"><span class="r-pr">#></span> k_T245 0.04743877</span> +<span class="r-out co"><span class="r-pr">#></span> k_phenol 0.75005585</span> +<span class="r-out co"><span class="r-pr">#></span> k_anisole 0.00829550</span> +<span class="r-out co"><span class="r-pr">#></span> f_T245_to_phenol 0.69212308</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 3.31827222</span> +<span class="r-in"><span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fit.1</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> $ff</span> <span class="r-out co"><span class="r-pr">#></span> T245_phenol T245_sink phenol_anisole phenol_sink </span> -<span class="r-out co"><span class="r-pr">#></span> 6.227599e-01 3.772401e-01 1.000000e+00 3.195366e-10 </span> +<span class="r-out co"><span class="r-pr">#></span> 6.227599e-01 3.772401e-01 1.000000e+00 3.773626e-10 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> $distimes</span> <span class="r-out co"><span class="r-pr">#></span> DT50 DT90</span> <span class="r-out co"><span class="r-pr">#></span> T245 15.982025 53.09114</span> <span class="r-out co"><span class="r-pr">#></span> phenol 1.711229 5.68458</span> -<span class="r-out co"><span class="r-pr">#></span> anisole 103.784092 344.76329</span> +<span class="r-out co"><span class="r-pr">#></span> anisole 103.784093 344.76329</span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-in"> <span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">fit.2</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">fit.2</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="mccall81_245T-1.png" alt="" width="700" height="433"></span> -<span class="r-in"> <span class="co"># }</span></span> +<span class="r-in"><span> <span class="co"># }</span></span></span> </code></pre></div> </div> </div> @@ -210,7 +216,7 @@ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/mean_degparms.html b/docs/reference/mean_degparms.html index bea9f2d8..dedb8660 100644 --- a/docs/reference/mean_degparms.html +++ b/docs/reference/mean_degparms.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -44,19 +44,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -87,36 +93,47 @@ </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">mean_degparms</span><span class="op">(</span> - <span class="va">object</span>, - random <span class="op">=</span> <span class="cn">FALSE</span>, - test_log_parms <span class="op">=</span> <span class="cn">FALSE</span>, - conf.level <span class="op">=</span> <span class="fl">0.6</span>, - default_log_parms <span class="op">=</span> <span class="cn">NA</span> -<span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mean_degparms</span><span class="op">(</span></span> +<span> <span class="va">object</span>,</span> +<span> random <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> test_log_parms <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> conf.level <span class="op">=</span> <span class="fl">0.6</span>,</span> +<span> default_log_parms <span class="op">=</span> <span class="cn">NA</span></span> +<span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>object</dt> <dd><p>An mmkin row object containing several fits of the same model to different datasets</p></dd> + + <dt>random</dt> <dd><p>Should a list with fixed and random effects be returned?</p></dd> + + <dt>test_log_parms</dt> <dd><p>If TRUE, log parameters are only considered in the mean calculations if their untransformed counterparts (most likely rate constants) pass the t-test for significant difference from zero.</p></dd> + + <dt>conf.level</dt> <dd><p>Possibility to adjust the required confidence level for parameter that are tested if requested by 'test_log_parms'.</p></dd> + + <dt>default_log_parms</dt> <dd><p>If set to a numeric value, this is used as a default value for the tested log parameters that failed the t-test.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>If random is FALSE (default), a named vector containing mean values + + +<p>If random is FALSE (default), a named vector containing mean values of the fitted degradation model parameters. If random is TRUE, a list with fixed and random effects, in the format required by the start argument of nlme for the case of a single grouping variable ds.</p> @@ -134,7 +151,7 @@ nlme for the case of a single grouping variable ds.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/mhmkin.html b/docs/reference/mhmkin.html index 25be0af6..e4b3e9d0 100644 --- a/docs/reference/mhmkin.html +++ b/docs/reference/mhmkin.html @@ -22,7 +22,7 @@ mixed-effects model fitting functions."><!-- mathjax --><script src="https://cdn </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -49,19 +49,25 @@ mixed-effects model fitting functions."><!-- mathjax --><script src="https://cdn <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -96,12 +102,18 @@ mixed-effects model fitting functions.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre><code>mhmkin(objects, backend = "saemix", algorithm = "saem", ...) + <div class="sourceCode"><pre><code>mhmkin(objects, ...) + +# S3 method for mmkin +mhmkin(objects, ...) # S3 method for list mhmkin( objects, backend = "saemix", + algorithm = "saem", + no_random_effect = NULL, + auto_ranef_threshold = 3, ..., cores = if (Sys.info()["sysname"] == "Windows") 1 else parallel::detectCores(), cluster = NULL @@ -118,7 +130,14 @@ print(x, ...)</code></pre></div> <h2>Arguments</h2> <dl><dt>objects</dt> <dd><p>A list of <a href="mmkin.html">mmkin</a> objects containing fits of the same -degradation models to the same data, but using different error models.</p></dd> +degradation models to the same data, but using different error models. +Alternatively, a single <a href="mmkin.html">mmkin</a> object containing fits of several +degradation models to the same data</p></dd> + + +<dt>...</dt> +<dd><p>Further arguments that will be passed to the nonlinear mixed-effects +model fitting function.</p></dd> <dt>backend</dt> @@ -130,9 +149,17 @@ supported</p></dd> <dd><p>The algorithm to be used for fitting (currently not used)</p></dd> -<dt>...</dt> -<dd><p>Further arguments that will be passed to the nonlinear mixed-effects -model fitting function.</p></dd> +<dt>no_random_effect</dt> +<dd><p>Default is NULL and will be passed to <a href="saem.html">saem</a>. If +you specify "auto", random effects are only included if the number +of datasets in which the parameter passed the t-test is at least 'auto_ranef_threshold'. +Beware that while this may make for convenient model reduction or even +numerical stability of the algorithm, it will likely lead to +underparameterised models.</p></dd> + + +<dt>auto_ranef_threshold</dt> +<dd><p>See 'no_random_effect.</p></dd> <dt>cores</dt> diff --git a/docs/reference/mixed-1.png b/docs/reference/mixed-1.png Binary files differindex fe7e6c47..dbba1b03 100644 --- a/docs/reference/mixed-1.png +++ b/docs/reference/mixed-1.png diff --git a/docs/reference/mixed.html b/docs/reference/mixed.html index dfc7a731..5b250072 100644 --- a/docs/reference/mixed.html +++ b/docs/reference/mixed.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -44,19 +44,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -87,73 +93,84 @@ </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">mixed</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span> - -<span class="co"># S3 method for mmkin</span> -<span class="fu">mixed</span><span class="op">(</span><span class="va">object</span>, method <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"none"</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span> - -<span class="co"># S3 method for mixed.mmkin</span> -<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mixed</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mmkin</span></span> +<span><span class="fu">mixed</span><span class="op">(</span><span class="va">object</span>, method <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"none"</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mixed.mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>object</dt> <dd><p>An <a href="mmkin.html">mmkin</a> row object</p></dd> + + <dt>...</dt> <dd><p>Currently not used</p></dd> + + <dt>method</dt> <dd><p>The method to be used</p></dd> + + <dt>x</dt> <dd><p>A mixed.mmkin object to print</p></dd> + + <dt>digits</dt> <dd><p>Number of digits to use for printing.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>An object of class 'mixed.mmkin' which has the observed data in a + + +<p>An object of class 'mixed.mmkin' which has the observed data in a single dataframe which is convenient for plotting</p> </div> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span> -<span class="r-in"><span class="va">n_biphasic</span> <span class="op"><-</span> <span class="fl">8</span></span> -<span class="r-in"><span class="va">err_1</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>const <span class="op">=</span> <span class="fl">1</span>, prop <span class="op">=</span> <span class="fl">0.07</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">DFOP_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span> -<span class="r-in"> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span> -<span class="r-in"> m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123456</span><span class="op">)</span></span> -<span class="r-in"><span class="va">log_sd</span> <span class="op"><-</span> <span class="fl">0.3</span></span> -<span class="r-in"><span class="va">syn_biphasic_parms</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">as.matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span> -<span class="r-in"> k1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.05</span><span class="op">)</span>, <span class="va">log_sd</span><span class="op">)</span>,</span> -<span class="r-in"> k2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.01</span><span class="op">)</span>, <span class="va">log_sd</span><span class="op">)</span>,</span> -<span class="r-in"> g <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">plogis</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fl">0</span>, <span class="va">log_sd</span><span class="op">)</span><span class="op">)</span>,</span> -<span class="r-in"> f_parent_to_m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">plogis</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fl">0</span>, <span class="va">log_sd</span><span class="op">)</span><span class="op">)</span>,</span> -<span class="r-in"> k_m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.002</span><span class="op">)</span>, <span class="va">log_sd</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">ds_biphasic_mean</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="va">n_biphasic</span>,</span> -<span class="r-in"> <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span> -<span class="r-in"> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">DFOP_SFO</span>, <span class="va">syn_biphasic_parms</span><span class="op">[</span><span class="va">i</span>, <span class="op">]</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, <span class="va">sampling_times</span><span class="op">)</span></span> -<span class="r-in"> <span class="op">}</span></span> -<span class="r-in"><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123456L</span><span class="op">)</span></span> -<span class="r-in"><span class="va">ds_biphasic</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">ds_biphasic_mean</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op">{</span></span> -<span class="r-in"> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">ds</span>,</span> -<span class="r-in"> sdfunc <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">err_1</span><span class="op">$</span><span class="va">const</span><span class="op">^</span><span class="fl">2</span> <span class="op">+</span> <span class="va">value</span><span class="op">^</span><span class="fl">2</span> <span class="op">*</span> <span class="va">err_1</span><span class="op">$</span><span class="va">prop</span><span class="op">^</span><span class="fl">2</span><span class="op">)</span>,</span> -<span class="r-in"> n <span class="op">=</span> <span class="fl">1</span>, secondary <span class="op">=</span> <span class="st">"m1"</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span> -<span class="r-in"><span class="op">}</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"><span class="va">f_mmkin</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">DFOP_SFO</span><span class="op">)</span>, <span class="va">ds_biphasic</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">f_mixed</span> <span class="op"><-</span> <span class="fu">mixed</span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_mixed</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">n_biphasic</span> <span class="op"><-</span> <span class="fl">8</span></span></span> +<span class="r-in"><span><span class="va">err_1</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>const <span class="op">=</span> <span class="fl">1</span>, prop <span class="op">=</span> <span class="fl">0.07</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">DFOP_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123456</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">log_sd</span> <span class="op"><-</span> <span class="fl">0.3</span></span></span> +<span class="r-in"><span><span class="va">syn_biphasic_parms</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">as.matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> k1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.05</span><span class="op">)</span>, <span class="va">log_sd</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> k2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.01</span><span class="op">)</span>, <span class="va">log_sd</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> g <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">plogis</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fl">0</span>, <span class="va">log_sd</span><span class="op">)</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> f_parent_to_m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">plogis</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fl">0</span>, <span class="va">log_sd</span><span class="op">)</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> k_m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="va">n_biphasic</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">0.002</span><span class="op">)</span>, <span class="va">log_sd</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">ds_biphasic_mean</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="va">n_biphasic</span>,</span></span> +<span class="r-in"><span> <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">DFOP_SFO</span>, <span class="va">syn_biphasic_parms</span><span class="op">[</span><span class="va">i</span>, <span class="op">]</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, <span class="va">sampling_times</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="op">}</span></span></span> +<span class="r-in"><span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123456L</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">ds_biphasic</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">ds_biphasic_mean</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op">{</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">ds</span>,</span></span> +<span class="r-in"><span> sdfunc <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">err_1</span><span class="op">$</span><span class="va">const</span><span class="op">^</span><span class="fl">2</span> <span class="op">+</span> <span class="va">value</span><span class="op">^</span><span class="fl">2</span> <span class="op">*</span> <span class="va">err_1</span><span class="op">$</span><span class="va">prop</span><span class="op">^</span><span class="fl">2</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> n <span class="op">=</span> <span class="fl">1</span>, secondary <span class="op">=</span> <span class="st">"m1"</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span></span> +<span class="r-in"><span><span class="op">}</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="va">f_mmkin</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">DFOP_SFO</span><span class="op">)</span>, <span class="va">ds_biphasic</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">f_mixed</span> <span class="op"><-</span> <span class="fu">mixed</span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_mixed</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Kinetic model fitted by nonlinear regression to each dataset</span> <span class="r-out co"><span class="r-pr">#></span> Structural model:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span> @@ -177,12 +194,12 @@ single dataframe which is convenient for plotting</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Mean fitted parameters:</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 </span> -<span class="r-out co"><span class="r-pr">#></span> 100.674757 -8.762456 -0.004347 -3.348812 -3.986853 </span> +<span class="r-out co"><span class="r-pr">#></span> 100.674757 -8.761916 -0.004347 -3.348812 -3.986853 </span> <span class="r-out co"><span class="r-pr">#></span> g_qlogis </span> -<span class="r-out co"><span class="r-pr">#></span> -0.087391 </span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_mixed</span><span class="op">)</span></span> +<span class="r-out co"><span class="r-pr">#></span> -0.087392 </span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_mixed</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="mixed-1.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="co"># }</span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> </code></pre></div> </div> </div> @@ -197,7 +214,7 @@ single dataframe which is convenient for plotting</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/mkin_long_to_wide.html b/docs/reference/mkin_long_to_wide.html index ba00a80a..1008876d 100644 --- a/docs/reference/mkin_long_to_wide.html +++ b/docs/reference/mkin_long_to_wide.html @@ -19,7 +19,7 @@ variable and several dependent variables as columns."><!-- mathjax --><script sr </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -46,19 +46,25 @@ variable and several dependent variables as columns."><!-- mathjax --><script sr <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -91,7 +97,7 @@ variable and several dependent variables as columns.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">mkin_long_to_wide</span><span class="op">(</span><span class="va">long_data</span>, time <span class="op">=</span> <span class="st">"time"</span>, outtime <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkin_long_to_wide</span><span class="op">(</span><span class="va">long_data</span>, time <span class="op">=</span> <span class="st">"time"</span>, outtime <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> @@ -101,14 +107,21 @@ variable and several dependent variables as columns.</p> the time values specified by the <code>time</code> argument, one column called "name" with the grouping of the observed values, and finally one column of observed values called "value".</p></dd> + + <dt>time</dt> <dd><p>The name of the time variable in the long input data.</p></dd> + + <dt>outtime</dt> <dd><p>The name of the time variable in the wide output data.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>Dataframe in wide format.</p> + + +<p>Dataframe in wide format.</p> </div> <div id="author"> <h2>Author</h2> @@ -117,8 +130,8 @@ observed values called "value".</p></dd> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"><span class="fu">mkin_long_to_wide</span><span class="op">(</span><span class="va">FOCUS_2006_D</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="fu">mkin_long_to_wide</span><span class="op">(</span><span class="va">FOCUS_2006_D</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> time parent m1</span> <span class="r-out co"><span class="r-pr">#></span> 1 0 99.46 0.00</span> <span class="r-out co"><span class="r-pr">#></span> 2 0 102.04 0.00</span> @@ -142,7 +155,7 @@ observed values called "value".</p></dd> <span class="r-out co"><span class="r-pr">#></span> 20 100 NA 33.13</span> <span class="r-out co"><span class="r-pr">#></span> 21 120 NA 25.15</span> <span class="r-out co"><span class="r-pr">#></span> 22 120 NA 33.31</span> -<span class="r-in"></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -157,7 +170,7 @@ observed values called "value".</p></dd> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/mkin_wide_to_long.html b/docs/reference/mkin_wide_to_long.html index 0cb9ebca..15ead67f 100644 --- a/docs/reference/mkin_wide_to_long.html +++ b/docs/reference/mkin_wide_to_long.html @@ -19,7 +19,7 @@ mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/ma </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -46,19 +46,25 @@ mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/ma <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -91,7 +97,7 @@ several dependent variable and converts it into the long form as required by </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">mkin_wide_to_long</span><span class="op">(</span><span class="va">wide_data</span>, time <span class="op">=</span> <span class="st">"t"</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkin_wide_to_long</span><span class="op">(</span><span class="va">wide_data</span>, time <span class="op">=</span> <span class="st">"t"</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> @@ -100,12 +106,17 @@ several dependent variable and converts it into the long form as required by <dd><p>The dataframe must contain one variable with the time values specified by the <code>time</code> argument and usually more than one column of observed values.</p></dd> + + <dt>time</dt> <dd><p>The name of the time variable.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>Dataframe in long format as needed for <code><a href="mkinfit.html">mkinfit</a></code>.</p> + + +<p>Dataframe in long format as needed for <code><a href="mkinfit.html">mkinfit</a></code>.</p> </div> <div id="author"> <h2>Author</h2> @@ -114,9 +125,9 @@ column of observed values.</p></dd> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"><span class="va">wide</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span><span class="op">)</span>, x <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">4</span>,<span class="fl">7</span><span class="op">)</span>, y <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>,<span class="fl">4</span>,<span class="fl">5</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"><span class="fu">mkin_wide_to_long</span><span class="op">(</span><span class="va">wide</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">wide</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span><span class="op">)</span>, x <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>,<span class="fl">4</span>,<span class="fl">7</span><span class="op">)</span>, y <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>,<span class="fl">4</span>,<span class="fl">5</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu">mkin_wide_to_long</span><span class="op">(</span><span class="va">wide</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> name time value</span> <span class="r-out co"><span class="r-pr">#></span> 1 x 1 1</span> <span class="r-out co"><span class="r-pr">#></span> 2 x 2 4</span> @@ -124,7 +135,7 @@ column of observed values.</p></dd> <span class="r-out co"><span class="r-pr">#></span> 4 y 1 3</span> <span class="r-out co"><span class="r-pr">#></span> 5 y 2 4</span> <span class="r-out co"><span class="r-pr">#></span> 6 y 3 5</span> -<span class="r-in"></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -139,7 +150,7 @@ column of observed values.</p></dd> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/mkinds.html b/docs/reference/mkinds.html index f43cfed4..801d2364 100644 --- a/docs/reference/mkinds.html +++ b/docs/reference/mkinds.html @@ -20,7 +20,7 @@ provided by this package come as mkinds objects nevertheless."><!-- mathjax -->< </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -47,19 +47,25 @@ provided by this package come as mkinds objects nevertheless."><!-- mathjax -->< <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -225,7 +231,7 @@ and value in order to be compatible with mkinfit</p></dd> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.5.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/mkindsg.html b/docs/reference/mkindsg.html index 37ec29bd..c72fe585 100644 --- a/docs/reference/mkindsg.html +++ b/docs/reference/mkindsg.html @@ -20,7 +20,7 @@ dataset if no data are supplied."><!-- mathjax --><script src="https://cdnjs.clo </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -47,19 +47,25 @@ dataset if no data are supplied."><!-- mathjax --><script src="https://cdnjs.clo <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -413,7 +419,7 @@ or covariates like soil pH).</p></dd> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.5.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/mkinerrmin.html b/docs/reference/mkinerrmin.html index b6051ab8..eaed3aa4 100644 --- a/docs/reference/mkinerrmin.html +++ b/docs/reference/mkinerrmin.html @@ -18,7 +18,7 @@ the chi-squared test as defined in the FOCUS kinetics report from 2006."><!-- ma </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -45,19 +45,25 @@ the chi-squared test as defined in the FOCUS kinetics report from 2006."><!-- ma <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -89,26 +95,31 @@ the chi-squared test as defined in the FOCUS kinetics report from 2006.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">mkinerrmin</span><span class="op">(</span><span class="va">fit</span>, alpha <span class="op">=</span> <span class="fl">0.05</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinerrmin</span><span class="op">(</span><span class="va">fit</span>, alpha <span class="op">=</span> <span class="fl">0.05</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>fit</dt> <dd><p>an object of class <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd> + + <dt>alpha</dt> <dd><p>The confidence level chosen for the chi-squared test.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>A dataframe with the following components:</p> + + +<p>A dataframe with the following components:</p> <dl><dt>err.min</dt> <dd><p>The relative error, expressed as a fraction.</p></dd> -<dt>n.optim</dt> + <dt>n.optim</dt> <dd><p>The number of optimised parameters attributed to the data series.</p></dd> -<dt>df</dt> + <dt>df</dt> <dd><p>The number of remaining degrees of freedom for the chi2 error level calculations. Note that mean values are used for the chi2 statistic and therefore every time @@ -132,28 +143,28 @@ Document Reference Sanco/10058/2005 version 2.0, 434 pp, <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"><span class="va">SFO_SFO</span> <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span><span class="op">)</span>,</span> -<span class="r-in"> m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"></span> -<span class="r-in"><span class="va">fit_FOCUS_D</span> <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">fit_FOCUS_D</span> <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="fu">mkinerrmin</span><span class="op">(</span><span class="va">fit_FOCUS_D</span><span class="op">)</span>, <span class="fl">4</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="fu">mkinerrmin</span><span class="op">(</span><span class="va">fit_FOCUS_D</span><span class="op">)</span>, <span class="fl">4</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> err.min n.optim df</span> <span class="r-out co"><span class="r-pr">#></span> All data 0.0640 4 15</span> <span class="r-out co"><span class="r-pr">#></span> parent 0.0646 2 7</span> <span class="r-out co"><span class="r-pr">#></span> m1 0.0469 2 8</span> -<span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"> <span class="va">fit_FOCUS_E</span> <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_E</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="fu">mkinerrmin</span><span class="op">(</span><span class="va">fit_FOCUS_E</span><span class="op">)</span>, <span class="fl">4</span><span class="op">)</span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span> <span class="va">fit_FOCUS_E</span> <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_E</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">round</a></span><span class="op">(</span><span class="fu">mkinerrmin</span><span class="op">(</span><span class="va">fit_FOCUS_E</span><span class="op">)</span>, <span class="fl">4</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> err.min n.optim df</span> <span class="r-out co"><span class="r-pr">#></span> All data 0.1544 4 13</span> <span class="r-out co"><span class="r-pr">#></span> parent 0.1659 2 7</span> <span class="r-out co"><span class="r-pr">#></span> m1 0.1095 2 6</span> -<span class="r-in"><span class="co"># }</span></span> -<span class="r-in"></span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -168,7 +179,7 @@ Document Reference Sanco/10058/2005 version 2.0, 434 pp, </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/mkinerrplot-1.png b/docs/reference/mkinerrplot-1.png Binary files differindex 852da18d..49bb1c0e 100644 --- a/docs/reference/mkinerrplot-1.png +++ b/docs/reference/mkinerrplot-1.png diff --git a/docs/reference/mkinerrplot.html b/docs/reference/mkinerrplot.html index 4b78f6eb..6c640652 100644 --- a/docs/reference/mkinerrplot.html +++ b/docs/reference/mkinerrplot.html @@ -21,7 +21,7 @@ using the argument show_errplot = TRUE."><!-- mathjax --><script src="https://cd </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -48,19 +48,25 @@ using the argument show_errplot = TRUE."><!-- mathjax --><script src="https://cd <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -95,56 +101,81 @@ using the argument <code>show_errplot = TRUE</code>.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">mkinerrplot</span><span class="op">(</span> - <span class="va">object</span>, - obs_vars <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">object</span><span class="op">$</span><span class="va">mkinmod</span><span class="op">$</span><span class="va">map</span><span class="op">)</span>, - xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1.1</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="va">object</span><span class="op">$</span><span class="va">data</span><span class="op">$</span><span class="va">predicted</span><span class="op">)</span><span class="op">)</span>, - xlab <span class="op">=</span> <span class="st">"Predicted"</span>, - ylab <span class="op">=</span> <span class="st">"Squared residual"</span>, - maxy <span class="op">=</span> <span class="st">"auto"</span>, - legend <span class="op">=</span> <span class="cn">TRUE</span>, - lpos <span class="op">=</span> <span class="st">"topright"</span>, - col_obs <span class="op">=</span> <span class="st">"auto"</span>, - pch_obs <span class="op">=</span> <span class="st">"auto"</span>, - frame <span class="op">=</span> <span class="cn">TRUE</span>, - <span class="va">...</span> -<span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinerrplot</span><span class="op">(</span></span> +<span> <span class="va">object</span>,</span> +<span> obs_vars <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">object</span><span class="op">$</span><span class="va">mkinmod</span><span class="op">$</span><span class="va">map</span><span class="op">)</span>,</span> +<span> xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1.1</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="va">object</span><span class="op">$</span><span class="va">data</span><span class="op">$</span><span class="va">predicted</span><span class="op">)</span><span class="op">)</span>,</span> +<span> xlab <span class="op">=</span> <span class="st">"Predicted"</span>,</span> +<span> ylab <span class="op">=</span> <span class="st">"Squared residual"</span>,</span> +<span> maxy <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> legend <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> lpos <span class="op">=</span> <span class="st">"topright"</span>,</span> +<span> col_obs <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> pch_obs <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> frame <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>object</dt> <dd><p>A fit represented in an <code><a href="mkinfit.html">mkinfit</a></code> object.</p></dd> + + <dt>obs_vars</dt> <dd><p>A character vector of names of the observed variables for which residuals should be plotted. Defaults to all observed variables in the model</p></dd> + + <dt>xlim</dt> <dd><p>plot range in x direction.</p></dd> + + <dt>xlab</dt> <dd><p>Label for the x axis.</p></dd> + + <dt>ylab</dt> <dd><p>Label for the y axis.</p></dd> + + <dt>maxy</dt> <dd><p>Maximum value of the residuals. This is used for the scaling of the y axis and defaults to "auto".</p></dd> + + <dt>legend</dt> <dd><p>Should a legend be plotted?</p></dd> + + <dt>lpos</dt> <dd><p>Where should the legend be placed? Default is "topright". Will be passed on to <code><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></code>.</p></dd> + + <dt>col_obs</dt> <dd><p>Colors for the observed variables.</p></dd> + + <dt>pch_obs</dt> <dd><p>Symbols to be used for the observed variables.</p></dd> + + <dt>frame</dt> <dd><p>Should a frame be drawn around the plots?</p></dd> + + <dt>...</dt> <dd><p>further arguments passed to <code><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></code>.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>Nothing is returned by this function, as it is called for its side + + +<p>Nothing is returned by this function, as it is called for its side effect, namely to produce a plot.</p> </div> <div id="see-also"> @@ -159,16 +190,16 @@ lines of the mkinfit object.</p></div> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"><span class="va">model</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="va">model</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">FOCUS_2006_D</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span class="r-in"><span><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">FOCUS_2006_D</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span> -<span class="r-in"><span class="fu">mkinerrplot</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu">mkinerrplot</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="mkinerrplot-1.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="co"># }</span></span> -<span class="r-in"></span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -183,7 +214,7 @@ lines of the mkinfit object.</p></div> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/mkinfit.html b/docs/reference/mkinfit.html index 3b27ac7f..62e4bd8d 100644 --- a/docs/reference/mkinfit.html +++ b/docs/reference/mkinfit.html @@ -25,7 +25,7 @@ likelihood function."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -52,19 +52,25 @@ likelihood function."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -378,17 +384,17 @@ Degradation Data. <em>Environments</em> 6(12) 124 <span class="r-in"><span><span class="co"># Use shorthand notation for parent only degradation</span></span></span> <span class="r-in"><span><span class="va">fit</span> <span class="op"><-</span> <span class="fu">mkinfit</span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.1.2 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.1 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Wed Aug 10 13:14:25 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Wed Aug 10 13:14:25 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.2.0 </span> +<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Nov 17 13:58:19 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Nov 17 13:58:19 2022 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type analytical </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted using 222 model solutions performed in 0.046 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted using 222 model solutions performed in 0.044 s</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Error model: Constant variance </span> <span class="r-out co"><span class="r-pr">#></span> </span> @@ -528,11 +534,10 @@ Degradation Data. <em>Environments</em> 6(12) 124 <span class="r-in"><span> analytical <span class="op">=</span> <span class="fu">mkinfit</span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>,</span></span> <span class="r-in"><span> solution_type <span class="op">=</span> <span class="st">"analytical"</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="op">}</span></span></span> -<span class="r-msg co"><span class="r-pr">#></span> Loading required package: rbenchmark</span> <span class="r-out co"><span class="r-pr">#></span> test relative elapsed</span> -<span class="r-out co"><span class="r-pr">#></span> 3 analytical 1.000 0.540</span> -<span class="r-out co"><span class="r-pr">#></span> 1 deSolve_compiled 1.537 0.830</span> -<span class="r-out co"><span class="r-pr">#></span> 2 eigen 2.687 1.451</span> +<span class="r-out co"><span class="r-pr">#></span> 3 analytical 1.000 0.570</span> +<span class="r-out co"><span class="r-pr">#></span> 1 deSolve_compiled 1.682 0.959</span> +<span class="r-out co"><span class="r-pr">#></span> 2 eigen 2.609 1.487</span> <span class="r-in"><span><span class="co"># }</span></span></span> <span class="r-in"><span></span></span> <span class="r-in"><span><span class="co"># Use stepwise fitting, using optimised parameters from parent only fit, FOMC-SFO</span></span></span> @@ -559,10 +564,10 @@ Degradation Data. <em>Environments</em> 6(12) 124 <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>NaNs produced</span> <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>NaNs produced</span> <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>diag(.) had 0 or NA entries; non-finite result is doubtful</span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.1.2 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.1 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Wed Aug 10 13:14:36 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Wed Aug 10 13:14:36 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.2.0 </span> +<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Nov 17 13:58:30 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Nov 17 13:58:30 2022 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span> @@ -571,7 +576,7 @@ Degradation Data. <em>Environments</em> 6(12) 124 <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type deSolve </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted using 3729 model solutions performed in 2.488 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted using 3729 model solutions performed in 2.464 s</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Error model: Two-component variance function </span> <span class="r-out co"><span class="r-pr">#></span> </span> diff --git a/docs/reference/mkinmod.html b/docs/reference/mkinmod.html index 3a29b39b..7dfa740a 100644 --- a/docs/reference/mkinmod.html +++ b/docs/reference/mkinmod.html @@ -21,7 +21,7 @@ components."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -48,19 +48,25 @@ components."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -95,22 +101,22 @@ components.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">mkinmod</span><span class="op">(</span> - <span class="va">...</span>, - use_of_ff <span class="op">=</span> <span class="st">"max"</span>, - name <span class="op">=</span> <span class="cn">NULL</span>, - speclist <span class="op">=</span> <span class="cn">NULL</span>, - quiet <span class="op">=</span> <span class="cn">FALSE</span>, - verbose <span class="op">=</span> <span class="cn">FALSE</span>, - dll_dir <span class="op">=</span> <span class="cn">NULL</span>, - unload <span class="op">=</span> <span class="cn">FALSE</span>, - overwrite <span class="op">=</span> <span class="cn">FALSE</span> -<span class="op">)</span> - -<span class="co"># S3 method for mkinmod</span> -<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span> - -<span class="fu">mkinsub</span><span class="op">(</span><span class="va">submodel</span>, to <span class="op">=</span> <span class="cn">NULL</span>, sink <span class="op">=</span> <span class="cn">TRUE</span>, full_name <span class="op">=</span> <span class="cn">NA</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinmod</span><span class="op">(</span></span> +<span> <span class="va">...</span>,</span> +<span> use_of_ff <span class="op">=</span> <span class="st">"max"</span>,</span> +<span> name <span class="op">=</span> <span class="cn">NULL</span>,</span> +<span> speclist <span class="op">=</span> <span class="cn">NULL</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> verbose <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> dll_dir <span class="op">=</span> <span class="cn">NULL</span>,</span> +<span> unload <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> overwrite <span class="op">=</span> <span class="cn">FALSE</span></span> +<span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mkinmod</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="fu">mkinsub</span><span class="op">(</span><span class="va">submodel</span>, to <span class="op">=</span> <span class="cn">NULL</span>, sink <span class="op">=</span> <span class="cn">TRUE</span>, full_name <span class="op">=</span> <span class="cn">NA</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> @@ -130,72 +136,108 @@ If the argument <code>use_of_ff</code> is set to "min" additional <code>mkinsub()</code> argument can be <code>sink = FALSE</code>, effectively fixing the flux to sink to zero. In print.mkinmod, this argument is currently not used.</p></dd> + + <dt>use_of_ff</dt> <dd><p>Specification of the use of formation fractions in the model equations and, if applicable, the coefficient matrix. If "max", formation fractions are always used (default). If "min", a minimum use of formation fractions is made, i.e. each first-order pathway to a metabolite has its own rate constant.</p></dd> + + <dt>name</dt> <dd><p>A name for the model. Should be a valid R object name.</p></dd> + + <dt>speclist</dt> <dd><p>The specification of the observed variables and their submodel types and pathways can be given as a single list using this argument. Default is NULL.</p></dd> + + <dt>quiet</dt> <dd><p>Should messages be suppressed?</p></dd> + + <dt>verbose</dt> <dd><p>If <code>TRUE</code>, passed to <code><a href="https://rdrr.io/pkg/inline/man/cfunction.html" class="external-link">inline::cfunction()</a></code> if applicable to give detailed information about the C function being built.</p></dd> + + <dt>dll_dir</dt> <dd><p>Directory where an DLL object, if generated internally by <code><a href="https://rdrr.io/pkg/inline/man/cfunction.html" class="external-link">inline::cfunction()</a></code>, should be saved. The DLL will only be stored in a permanent location for use in future sessions, if 'dll_dir' and 'name' are specified.</p></dd> + + <dt>unload</dt> <dd><p>If a DLL from the target location in 'dll_dir' is already loaded, should that be unloaded first?</p></dd> + + <dt>overwrite</dt> <dd><p>If a file exists at the target DLL location in 'dll_dir', should this be overwritten?</p></dd> + + <dt>x</dt> <dd><p>An <code>mkinmod</code> object.</p></dd> + + <dt>submodel</dt> <dd><p>Character vector of length one to specify the submodel type. See <code>mkinmod</code> for the list of allowed submodel names.</p></dd> + + <dt>to</dt> <dd><p>Vector of the names of the state variable to which a transformation shall be included in the model.</p></dd> + + <dt>sink</dt> <dd><p>Should a pathway to sink be included in the model in addition to the pathways to other state variables?</p></dd> + + <dt>full_name</dt> <dd><p>An optional name to be used e.g. for plotting fits performed with the model. You can use non-ASCII characters here, but then your R code will not be portable, <em>i.e.</em> may produce unintended plot results on other operating systems or system configurations.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>A list of class <code>mkinmod</code> for use with <code><a href="mkinfit.html">mkinfit()</a></code>, + + +<p>A list of class <code>mkinmod</code> for use with <code><a href="mkinfit.html">mkinfit()</a></code>, containing, among others,</p> <dl><dt>diffs</dt> <dd><p>A vector of string representations of differential equations, one for each modelling variable.</p></dd> + <dt>map</dt> <dd><p>A list containing named character vectors for each observed variable, specifying the modelling variables by which it is represented.</p></dd> + <dt>use_of_ff</dt> <dd><p>The content of <code>use_of_ff</code> is passed on in this list component.</p></dd> + <dt>deg_func</dt> <dd><p>If generated, a function containing the solution of the degradation model.</p></dd> + <dt>coefmat</dt> <dd><p>The coefficient matrix, if the system of differential equations can be represented by one.</p></dd> + <dt>cf</dt> <dd><p>If generated, a compiled function calculating the derivatives as returned by cfunction.</p></dd> + + </dl><p>A list for use with <code>mkinmod</code>.</p> </div> <div id="details"> @@ -233,16 +275,16 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"><span class="co"># Specify the SFO model (this is not needed any more, as we can now mkinfit("SFO", ...)</span></span> -<span class="r-in"><span class="va">SFO</span> <span class="op"><-</span> <span class="fu">mkinmod</span><span class="op">(</span>parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># One parent compound, one metabolite, both single first order</span></span> -<span class="r-in"><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu">mkinmod</span><span class="op">(</span></span> -<span class="r-in"> parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span> -<span class="r-in"> m1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Specify the SFO model (this is not needed any more, as we can now mkinfit("SFO", ...)</span></span></span> +<span class="r-in"><span><span class="va">SFO</span> <span class="op"><-</span> <span class="fu">mkinmod</span><span class="op">(</span>parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># One parent compound, one metabolite, both single first order</span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu">mkinmod</span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">SFO_SFO</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">SFO_SFO</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> <mkinmod> model generated with</span> <span class="r-out co"><span class="r-pr">#></span> Use of formation fractions $use_of_ff: max </span> <span class="r-out co"><span class="r-pr">#></span> Specification $spec:</span> @@ -255,32 +297,32 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> <span class="r-out co"><span class="r-pr">#></span> Differential equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - k_parent * parent</span> <span class="r-out co"><span class="r-pr">#></span> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1</span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"> <span class="va">fit_sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span> <span class="va">fit_sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span><span class="op">)</span></span></span> <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span> -<span class="r-in"></span> -<span class="r-in"> <span class="co"># Now supplying compound names used for plotting, and write to user defined location</span></span> -<span class="r-in"> <span class="co"># We need to choose a path outside the session tempdir because this gets removed</span></span> -<span class="r-in"> <span class="va">DLL_dir</span> <span class="op"><-</span> <span class="st">"~/.local/share/mkin"</span></span> -<span class="r-in"> <span class="kw">if</span> <span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.exists</a></span><span class="op">(</span><span class="va">DLL_dir</span><span class="op">)</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.create</a></span><span class="op">(</span><span class="va">DLL_dir</span><span class="op">)</span></span> -<span class="r-in"> <span class="va">SFO_SFO.2</span> <span class="op"><-</span> <span class="fu">mkinmod</span><span class="op">(</span></span> -<span class="r-in"> parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span>, full_name <span class="op">=</span> <span class="st">"Test compound"</span><span class="op">)</span>,</span> -<span class="r-in"> m1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, full_name <span class="op">=</span> <span class="st">"Metabolite M1"</span><span class="op">)</span>,</span> -<span class="r-in"> name <span class="op">=</span> <span class="st">"SFO_SFO"</span>, dll_dir <span class="op">=</span> <span class="va">DLL_dir</span>, unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-msg co"><span class="r-pr">#></span> Copied DLL from /tmp/RtmpkcKjUM/file8dd657f864c2.so to /home/jranke/.local/share/mkin/SFO_SFO.so</span> -<span class="r-in"><span class="co"># Now we can save the model and restore it in a new session</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">saveRDS</a></span><span class="op">(</span><span class="va">SFO_SFO.2</span>, file <span class="op">=</span> <span class="st">"~/SFO_SFO.rds"</span><span class="op">)</span></span> -<span class="r-in"><span class="co"># Terminate the R session here if you would like to check, and then do</span></span> -<span class="r-in"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span> -<span class="r-in"><span class="va">SFO_SFO.3</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">readRDS</a></span><span class="op">(</span><span class="st">"~/SFO_SFO.rds"</span><span class="op">)</span></span> -<span class="r-in"><span class="va">fit_sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO.3</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># Now supplying compound names used for plotting, and write to user defined location</span></span></span> +<span class="r-in"><span> <span class="co"># We need to choose a path outside the session tempdir because this gets removed</span></span></span> +<span class="r-in"><span> <span class="va">DLL_dir</span> <span class="op"><-</span> <span class="st">"~/.local/share/mkin"</span></span></span> +<span class="r-in"><span> <span class="kw">if</span> <span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.exists</a></span><span class="op">(</span><span class="va">DLL_dir</span><span class="op">)</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.create</a></span><span class="op">(</span><span class="va">DLL_dir</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">SFO_SFO.2</span> <span class="op"><-</span> <span class="fu">mkinmod</span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span>, full_name <span class="op">=</span> <span class="st">"Test compound"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, full_name <span class="op">=</span> <span class="st">"Metabolite M1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> name <span class="op">=</span> <span class="st">"SFO_SFO"</span>, dll_dir <span class="op">=</span> <span class="va">DLL_dir</span>, unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-msg co"><span class="r-pr">#></span> Copied DLL from /tmp/Rtmp6DB2Bl/file3f5b93b53e71f.so to /home/jranke/.local/share/mkin/SFO_SFO.so</span> +<span class="r-in"><span><span class="co"># Now we can save the model and restore it in a new session</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">saveRDS</a></span><span class="op">(</span><span class="va">SFO_SFO.2</span>, file <span class="op">=</span> <span class="st">"~/SFO_SFO.rds"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># Terminate the R session here if you would like to check, and then do</span></span></span> +<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO.3</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">readRDS</a></span><span class="op">(</span><span class="st">"~/SFO_SFO.rds"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">fit_sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO.3</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span><span class="op">)</span></span></span> <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># Show details of creating the C function</span></span> -<span class="r-in"><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu">mkinmod</span><span class="op">(</span></span> -<span class="r-in"> parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span> -<span class="r-in"> m1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, verbose <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Show details of creating the C function</span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu">mkinmod</span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, verbose <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Program source:</span> <span class="r-out co"><span class="r-pr">#></span> 1: #include <R.h></span> <span class="r-out co"><span class="r-pr">#></span> 2: </span> @@ -302,10 +344,10 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> <span class="r-out co"><span class="r-pr">#></span> 18: f[1] = + f_parent_to_m1 * k_parent * y[0] - k_m1 * y[1];</span> <span class="r-out co"><span class="r-pr">#></span> 19: }</span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># The symbolic solution which is available in this case is not</span></span> -<span class="r-in"><span class="co"># made for human reading but for speed of computation</span></span> -<span class="r-in"><span class="va">SFO_SFO</span><span class="op">$</span><span class="va">deg_func</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># The symbolic solution which is available in this case is not</span></span></span> +<span class="r-in"><span><span class="co"># made for human reading but for speed of computation</span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO</span><span class="op">$</span><span class="va">deg_func</span></span></span> <span class="r-out co"><span class="r-pr">#></span> function (observed, odeini, odeparms) </span> <span class="r-out co"><span class="r-pr">#></span> {</span> <span class="r-out co"><span class="r-pr">#></span> predicted <- numeric(0)</span> @@ -321,21 +363,21 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> <span class="r-out co"><span class="r-pr">#></span> })</span> <span class="r-out co"><span class="r-pr">#></span> return(predicted)</span> <span class="r-out co"><span class="r-pr">#></span> }</span> -<span class="r-out co"><span class="r-pr">#></span> <environment: 0x55cc1a5f8ad8></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># If we have several parallel metabolites</span></span> -<span class="r-in"><span class="co"># (compare tests/testthat/test_synthetic_data_for_UBA_2014.R)</span></span> -<span class="r-in"><span class="va">m_synth_DFOP_par</span> <span class="op"><-</span> <span class="fu">mkinmod</span><span class="op">(</span></span> -<span class="r-in"> parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span> -<span class="r-in"> M1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> M2 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">fit_DFOP_par_c</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_DFOP_par</span>,</span> -<span class="r-in"> <span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>,</span> -<span class="r-in"> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="co"># }</span></span> -<span class="r-in"></span> +<span class="r-out co"><span class="r-pr">#></span> <environment: 0x55556401ac50></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># If we have several parallel metabolites</span></span></span> +<span class="r-in"><span><span class="co"># (compare tests/testthat/test_synthetic_data_for_UBA_2014.R)</span></span></span> +<span class="r-in"><span><span class="va">m_synth_DFOP_par</span> <span class="op"><-</span> <span class="fu">mkinmod</span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">fit_DFOP_par_c</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_DFOP_par</span>,</span></span> +<span class="r-in"><span> <span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>,</span></span> +<span class="r-in"><span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -350,7 +392,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/mkinparplot-1.png b/docs/reference/mkinparplot-1.png Binary files differindex 69d9ac75..fff98391 100644 --- a/docs/reference/mkinparplot-1.png +++ b/docs/reference/mkinparplot-1.png diff --git a/docs/reference/mkinparplot.html b/docs/reference/mkinparplot.html index b9eda413..a41456f2 100644 --- a/docs/reference/mkinparplot.html +++ b/docs/reference/mkinparplot.html @@ -18,7 +18,7 @@ mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/ma </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -45,19 +45,25 @@ mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/ma <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -89,17 +95,20 @@ mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/ma </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">mkinparplot</span><span class="op">(</span><span class="va">object</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinparplot</span><span class="op">(</span><span class="va">object</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>object</dt> <dd><p>A fit represented in an <code><a href="mkinfit.html">mkinfit</a></code> object.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>Nothing is returned by this function, as it is called for its side + + +<p>Nothing is returned by this function, as it is called for its side effect, namely to produce a plot.</p> </div> <div id="author"> @@ -109,18 +118,20 @@ effect, namely to produce a plot.</p> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"><span class="va">model</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span> -<span class="r-in"> T245 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"phenol"</span><span class="op">)</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> -<span class="r-in"> phenol <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"anisole"</span><span class="op">)</span><span class="op">)</span>,</span> -<span class="r-in"> anisole <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="va">model</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> T245 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"phenol"</span><span class="op">)</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> phenol <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"anisole"</span><span class="op">)</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> anisole <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">model</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">mccall81_245T</span>, <span class="va">soil</span> <span class="op">==</span> <span class="st">"Commerce"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span class="r-in"><span><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">model</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">mccall81_245T</span>, <span class="va">soil</span> <span class="op">==</span> <span class="st">"Commerce"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span> -<span class="r-in"><span class="fu">mkinparplot</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span> +<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Optimisation did not converge:</span> +<span class="r-wrn co"><span class="r-pr">#></span> false convergence (8)</span> +<span class="r-in"><span><span class="fu">mkinparplot</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="mkinparplot-1.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="co"># }</span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> </code></pre></div> </div> </div> @@ -135,7 +146,7 @@ effect, namely to produce a plot.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/mkinplot.html b/docs/reference/mkinplot.html index cad5b35f..46ffe33e 100644 --- a/docs/reference/mkinplot.html +++ b/docs/reference/mkinplot.html @@ -18,7 +18,7 @@ plot.mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -45,19 +45,25 @@ plot.mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -89,19 +95,24 @@ plot.mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">mkinplot</span><span class="op">(</span><span class="va">fit</span>, <span class="va">...</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinplot</span><span class="op">(</span><span class="va">fit</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>fit</dt> <dd><p>an object of class <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd> + + <dt>...</dt> <dd><p>further arguments passed to <code><a href="plot.mkinfit.html">plot.mkinfit</a></code>.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>The function is called for its side effect.</p> + + +<p>The function is called for its side effect.</p> </div> <div id="author"> <h2>Author</h2> @@ -120,7 +131,7 @@ plot.mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/mkinpredict.html b/docs/reference/mkinpredict.html index d888bb25..2aab0b50 100644 --- a/docs/reference/mkinpredict.html +++ b/docs/reference/mkinpredict.html @@ -19,7 +19,7 @@ kinetic parameters and initial values for the state variables."><!-- mathjax --> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -46,19 +46,25 @@ kinetic parameters and initial values for the state variables."><!-- mathjax --> <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -86,95 +92,125 @@ kinetic parameters and initial values for the state variables."><!-- mathjax --> <div class="ref-description"> <p>This function produces a time series for all the observed variables in a -kinetic model as specified by <code><a href="mkinmod.html">mkinmod</a></code>, using a specific set of +kinetic model as specified by <a href="mkinmod.html">mkinmod</a>, using a specific set of kinetic parameters and initial values for the state variables.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">x</span>, <span class="va">odeparms</span>, <span class="va">odeini</span>, <span class="va">outtimes</span>, <span class="va">...</span><span class="op">)</span> - -<span class="co"># S3 method for mkinmod</span> -<span class="fu">mkinpredict</span><span class="op">(</span> - <span class="va">x</span>, - odeparms <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent_sink <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>, - odeini <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, - outtimes <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">120</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>, - solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, - use_compiled <span class="op">=</span> <span class="st">"auto"</span>, - method.ode <span class="op">=</span> <span class="st">"lsoda"</span>, - atol <span class="op">=</span> <span class="fl">1e-08</span>, - rtol <span class="op">=</span> <span class="fl">1e-10</span>, - map_output <span class="op">=</span> <span class="cn">TRUE</span>, - na_stop <span class="op">=</span> <span class="cn">TRUE</span>, - <span class="va">...</span> -<span class="op">)</span> - -<span class="co"># S3 method for mkinfit</span> -<span class="fu">mkinpredict</span><span class="op">(</span> - <span class="va">x</span>, - odeparms <span class="op">=</span> <span class="va">x</span><span class="op">$</span><span class="va">bparms.ode</span>, - odeini <span class="op">=</span> <span class="va">x</span><span class="op">$</span><span class="va">bparms.state</span>, - outtimes <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">120</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>, - solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, - use_compiled <span class="op">=</span> <span class="st">"auto"</span>, - method.ode <span class="op">=</span> <span class="st">"lsoda"</span>, - atol <span class="op">=</span> <span class="fl">1e-08</span>, - rtol <span class="op">=</span> <span class="fl">1e-10</span>, - map_output <span class="op">=</span> <span class="cn">TRUE</span>, - <span class="va">...</span> -<span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">x</span>, <span class="va">odeparms</span>, <span class="va">odeini</span>, <span class="va">outtimes</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mkinmod</span></span> +<span><span class="fu">mkinpredict</span><span class="op">(</span></span> +<span> <span class="va">x</span>,</span> +<span> odeparms <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent_sink <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span> +<span> odeini <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>,</span> +<span> outtimes <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">120</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span> +<span> solution_type <span class="op">=</span> <span class="st">"deSolve"</span>,</span> +<span> use_compiled <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> method.ode <span class="op">=</span> <span class="st">"lsoda"</span>,</span> +<span> atol <span class="op">=</span> <span class="fl">1e-08</span>,</span> +<span> rtol <span class="op">=</span> <span class="fl">1e-10</span>,</span> +<span> maxsteps <span class="op">=</span> <span class="fl">20000</span>,</span> +<span> map_output <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> na_stop <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mkinfit</span></span> +<span><span class="fu">mkinpredict</span><span class="op">(</span></span> +<span> <span class="va">x</span>,</span> +<span> odeparms <span class="op">=</span> <span class="va">x</span><span class="op">$</span><span class="va">bparms.ode</span>,</span> +<span> odeini <span class="op">=</span> <span class="va">x</span><span class="op">$</span><span class="va">bparms.state</span>,</span> +<span> outtimes <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">120</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span> +<span> solution_type <span class="op">=</span> <span class="st">"deSolve"</span>,</span> +<span> use_compiled <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> method.ode <span class="op">=</span> <span class="st">"lsoda"</span>,</span> +<span> atol <span class="op">=</span> <span class="fl">1e-08</span>,</span> +<span> rtol <span class="op">=</span> <span class="fl">1e-10</span>,</span> +<span> map_output <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>x</dt> -<dd><p>A kinetic model as produced by <code><a href="mkinmod.html">mkinmod</a></code>, or a kinetic -fit as fitted by <code><a href="mkinfit.html">mkinfit</a></code>. In the latter case, the fitted -parameters are used for the prediction.</p></dd> +<dd><p>A kinetic model as produced by <a href="mkinmod.html">mkinmod</a>, or a kinetic fit as +fitted by <a href="mkinfit.html">mkinfit</a>. In the latter case, the fitted parameters are used for +the prediction.</p></dd> + + <dt>odeparms</dt> <dd><p>A numeric vector specifying the parameters used in the -kinetic model, which is generally defined as a set of ordinary -differential equations.</p></dd> +kinetic model, which is generally defined as a set of ordinary differential +equations.</p></dd> + + <dt>odeini</dt> <dd><p>A numeric vector containing the initial values of the state variables of the model. Note that the state variables can differ from the observed variables, for example in the case of the SFORB model.</p></dd> + + <dt>outtimes</dt> <dd><p>A numeric vector specifying the time points for which model predictions should be generated.</p></dd> + + <dt>...</dt> <dd><p>Further arguments passed to the ode solver in case such a solver is used.</p></dd> + + <dt>solution_type</dt> <dd><p>The method that should be used for producing the predictions. This should generally be "analytical" if there is only one observed variable, and usually "deSolve" in the case of several observed variables. The third possibility "eigen" is faster but not applicable to some models e.g. using FOMC for the parent compound.</p></dd> + + <dt>use_compiled</dt> <dd><p>If set to <code>FALSE</code>, no compiled version of the -<code><a href="mkinmod.html">mkinmod</a></code> model is used, even if is present.</p></dd> +<a href="mkinmod.html">mkinmod</a> model is used, even if is present.</p></dd> + + <dt>method.ode</dt> -<dd><p>The solution method passed via <code>mkinpredict</code> -to <code>ode</code> in case the solution type is "deSolve". The default -"lsoda" is performant, but sometimes fails to converge.</p></dd> +<dd><p>The solution method passed via mkinpredict to ode] in +case the solution type is "deSolve". The default "lsoda" is performant, but +sometimes fails to converge.</p></dd> + + <dt>atol</dt> -<dd><p>Absolute error tolerance, passed to <code>ode</code>. Default -is 1e-8, lower than in <code>lsoda</code>.</p></dd> +<dd><p>Absolute error tolerance, passed to ode. Default is 1e-8, +lower than in lsoda.</p></dd> + + <dt>rtol</dt> -<dd><p>Absolute error tolerance, passed to <code>ode</code>. Default -is 1e-10, much lower than in <code>lsoda</code>.</p></dd> +<dd><p>Absolute error tolerance, passed to ode. Default is 1e-10, +much lower than in lsoda.</p></dd> + + +<dt>maxsteps</dt> +<dd><p>Maximum number of steps, passed to ode.</p></dd> + + <dt>map_output</dt> <dd><p>Boolean to specify if the output should list values for the observed variables (default) or for all state variables (if set to FALSE). Setting this to FALSE has no effect for analytical solutions, as these always return mapped output.</p></dd> + + <dt>na_stop</dt> -<dd><p>Should it be an error if deSolve::ode returns NaN values</p></dd> +<dd><p>Should it be an error if ode returns NaN values</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>A matrix with the numeric solution in wide format</p> + + +<p>A matrix with the numeric solution in wide format</p> </div> <div id="author"> <h2>Author</h2> @@ -183,11 +219,11 @@ as these always return mapped output.</p></dd> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"><span class="va">SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"><span class="co"># Compare solution types</span></span> -<span class="r-in"><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span> -<span class="r-in"> solution_type <span class="op">=</span> <span class="st">"analytical"</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># Compare solution types</span></span></span> +<span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span></span> +<span class="r-in"><span> solution_type <span class="op">=</span> <span class="st">"analytical"</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> time degradinol</span> <span class="r-out co"><span class="r-pr">#></span> 0 0 100.0000000</span> <span class="r-out co"><span class="r-pr">#></span> 1 1 74.0818221</span> @@ -210,8 +246,8 @@ as these always return mapped output.</p></dd> <span class="r-out co"><span class="r-pr">#></span> 18 18 0.4516581</span> <span class="r-out co"><span class="r-pr">#></span> 19 19 0.3345965</span> <span class="r-out co"><span class="r-pr">#></span> 20 20 0.2478752</span> -<span class="r-in"><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span> -<span class="r-in"> solution_type <span class="op">=</span> <span class="st">"deSolve"</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span></span> +<span class="r-in"><span> solution_type <span class="op">=</span> <span class="st">"deSolve"</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> time degradinol</span> <span class="r-out co"><span class="r-pr">#></span> 0 0 100.0000000</span> <span class="r-out co"><span class="r-pr">#></span> 1 1 74.0818221</span> @@ -234,8 +270,8 @@ as these always return mapped output.</p></dd> <span class="r-out co"><span class="r-pr">#></span> 18 18 0.4516581</span> <span class="r-out co"><span class="r-pr">#></span> 19 19 0.3345965</span> <span class="r-out co"><span class="r-pr">#></span> 20 20 0.2478752</span> -<span class="r-in"><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span> -<span class="r-in"> solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, use_compiled <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span></span> +<span class="r-in"><span> solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, use_compiled <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> time degradinol</span> <span class="r-out co"><span class="r-pr">#></span> 0 0 100.0000000</span> <span class="r-out co"><span class="r-pr">#></span> 1 1 74.0818221</span> @@ -258,8 +294,8 @@ as these always return mapped output.</p></dd> <span class="r-out co"><span class="r-pr">#></span> 18 18 0.4516581</span> <span class="r-out co"><span class="r-pr">#></span> 19 19 0.3345965</span> <span class="r-out co"><span class="r-pr">#></span> 20 20 0.2478752</span> -<span class="r-in"><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span> -<span class="r-in"> solution_type <span class="op">=</span> <span class="st">"eigen"</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span></span> +<span class="r-in"><span> solution_type <span class="op">=</span> <span class="st">"eigen"</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> time degradinol</span> <span class="r-out co"><span class="r-pr">#></span> 0 0 100.0000000</span> <span class="r-out co"><span class="r-pr">#></span> 1 1 74.0818221</span> @@ -282,77 +318,82 @@ as these always return mapped output.</p></dd> <span class="r-out co"><span class="r-pr">#></span> 18 18 0.4516581</span> <span class="r-out co"><span class="r-pr">#></span> 19 19 0.3345965</span> <span class="r-out co"><span class="r-pr">#></span> 20 20 0.2478752</span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># Compare integration methods to analytical solution</span></span> -<span class="r-in"><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span> -<span class="r-in"> solution_type <span class="op">=</span> <span class="st">"analytical"</span><span class="op">)</span><span class="op">[</span><span class="fl">21</span>,<span class="op">]</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Compare integration methods to analytical solution</span></span></span> +<span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span></span> +<span class="r-in"><span> solution_type <span class="op">=</span> <span class="st">"analytical"</span><span class="op">)</span><span class="op">[</span><span class="fl">21</span>,<span class="op">]</span></span></span> <span class="r-out co"><span class="r-pr">#></span> time degradinol </span> <span class="r-out co"><span class="r-pr">#></span> 20.0000000 0.2478752 </span> -<span class="r-in"><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span> -<span class="r-in"> method <span class="op">=</span> <span class="st">"lsoda"</span><span class="op">)</span><span class="op">[</span><span class="fl">21</span>,<span class="op">]</span></span> +<span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span></span> +<span class="r-in"><span> method <span class="op">=</span> <span class="st">"lsoda"</span><span class="op">)</span><span class="op">[</span><span class="fl">21</span>,<span class="op">]</span></span></span> <span class="r-out co"><span class="r-pr">#></span> time degradinol </span> <span class="r-out co"><span class="r-pr">#></span> 20.0000000 0.2478752 </span> -<span class="r-in"><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span> -<span class="r-in"> method <span class="op">=</span> <span class="st">"ode45"</span><span class="op">)</span><span class="op">[</span><span class="fl">21</span>,<span class="op">]</span></span> +<span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span></span> +<span class="r-in"><span> method <span class="op">=</span> <span class="st">"ode45"</span><span class="op">)</span><span class="op">[</span><span class="fl">21</span>,<span class="op">]</span></span></span> <span class="r-out co"><span class="r-pr">#></span> time degradinol </span> <span class="r-out co"><span class="r-pr">#></span> 20.0000000 0.2478752 </span> -<span class="r-in"><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span> -<span class="r-in"> method <span class="op">=</span> <span class="st">"rk4"</span><span class="op">)</span><span class="op">[</span><span class="fl">21</span>,<span class="op">]</span></span> +<span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span></span> +<span class="r-in"><span> method <span class="op">=</span> <span class="st">"rk4"</span><span class="op">)</span><span class="op">[</span><span class="fl">21</span>,<span class="op">]</span></span></span> <span class="r-out co"><span class="r-pr">#></span> time degradinol </span> <span class="r-out co"><span class="r-pr">#></span> 20.0000000 0.2480043 </span> -<span class="r-in"><span class="co"># rk4 is not as precise here</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># The number of output times used to make a lot of difference until the</span></span> -<span class="r-in"><span class="co"># default for atol was adjusted</span></span> -<span class="r-in"><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">20</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span><span class="op">)</span><span class="op">[</span><span class="fl">201</span>,<span class="op">]</span></span> +<span class="r-in"><span><span class="co"># rk4 is not as precise here</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># The number of output times used to make a lot of difference until the</span></span></span> +<span class="r-in"><span><span class="co"># default for atol was adjusted</span></span></span> +<span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">20</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span><span class="op">)</span><span class="op">[</span><span class="fl">201</span>,<span class="op">]</span></span></span> <span class="r-out co"><span class="r-pr">#></span> time degradinol </span> <span class="r-out co"><span class="r-pr">#></span> 20.0000000 0.2478752 </span> -<span class="r-in"><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">20</span>, by <span class="op">=</span> <span class="fl">0.01</span><span class="op">)</span><span class="op">)</span><span class="op">[</span><span class="fl">2001</span>,<span class="op">]</span></span> +<span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">20</span>, by <span class="op">=</span> <span class="fl">0.01</span><span class="op">)</span><span class="op">)</span><span class="op">[</span><span class="fl">2001</span>,<span class="op">]</span></span></span> <span class="r-out co"><span class="r-pr">#></span> time degradinol </span> <span class="r-out co"><span class="r-pr">#></span> 20.0000000 0.2478752 </span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># Comparison of the performance of solution types</span></span> -<span class="r-in"><span class="va">SFO_SFO</span> <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span><span class="op">)</span>,</span> -<span class="r-in"> m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Comparison of the performance of solution types</span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"><span class="kw">if</span><span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="va"><a href="http://rbenchmark.googlecode.com" class="external-link">rbenchmark</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span> -<span class="r-in"> <span class="fu">benchmark</span><span class="op">(</span>replications <span class="op">=</span> <span class="fl">10</span>, order <span class="op">=</span> <span class="st">"relative"</span>, columns <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"test"</span>, <span class="st">"relative"</span>, <span class="st">"elapsed"</span><span class="op">)</span>,</span> -<span class="r-in"> eigen <span class="op">=</span> <span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO_SFO</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.15</span>, f_parent_to_m1 <span class="op">=</span> <span class="fl">0.5</span>, k_m1 <span class="op">=</span> <span class="fl">0.01</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">20</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span> -<span class="r-in"> solution_type <span class="op">=</span> <span class="st">"eigen"</span><span class="op">)</span><span class="op">[</span><span class="fl">201</span>,<span class="op">]</span>,</span> -<span class="r-in"> deSolve_compiled <span class="op">=</span> <span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO_SFO</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.15</span>, f_parent_to_m1 <span class="op">=</span> <span class="fl">0.5</span>, k_m1 <span class="op">=</span> <span class="fl">0.01</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">20</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span> -<span class="r-in"> solution_type <span class="op">=</span> <span class="st">"deSolve"</span><span class="op">)</span><span class="op">[</span><span class="fl">201</span>,<span class="op">]</span>,</span> -<span class="r-in"> deSolve <span class="op">=</span> <span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO_SFO</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.15</span>, f_parent_to_m1 <span class="op">=</span> <span class="fl">0.5</span>, k_m1 <span class="op">=</span> <span class="fl">0.01</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">20</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span> -<span class="r-in"> solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, use_compiled <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">[</span><span class="fl">201</span>,<span class="op">]</span>,</span> -<span class="r-in"> analytical <span class="op">=</span> <span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO_SFO</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.15</span>, f_parent_to_m1 <span class="op">=</span> <span class="fl">0.5</span>, k_m1 <span class="op">=</span> <span class="fl">0.01</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">20</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span> -<span class="r-in"> solution_type <span class="op">=</span> <span class="st">"analytical"</span>, use_compiled <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">[</span><span class="fl">201</span>,<span class="op">]</span><span class="op">)</span></span> -<span class="r-in"><span class="op">}</span></span> -<span class="r-msg co"><span class="r-pr">#></span> Loading required package: rbenchmark</span> -<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>there is no package called ‘rbenchmark’</span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"> <span class="co"># Predict from a fitted model</span></span> -<span class="r-in"> <span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"> <span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">f</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-out co"><span class="r-pr">#></span> time parent m1</span> -<span class="r-out co"><span class="r-pr">#></span> 0 0.0 82.49216 0.000000</span> -<span class="r-out co"><span class="r-pr">#></span> 0.1 0.1 80.00562 1.236394</span> -<span class="r-out co"><span class="r-pr">#></span> 0.2 0.2 77.59404 2.423201</span> -<span class="r-out co"><span class="r-pr">#></span> 0.3 0.3 75.25514 3.562040</span> -<span class="r-out co"><span class="r-pr">#></span> 0.4 0.4 72.98675 4.654478</span> -<span class="r-out co"><span class="r-pr">#></span> 0.5 0.5 70.78673 5.702033</span> -<span class="r-in"><span class="co"># }</span></span> -<span class="r-in"></span> +<span class="r-in"><span><span class="kw">if</span><span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="va"><a href="http://rbenchmark.googlecode.com" class="external-link">rbenchmark</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/pkg/rbenchmark/man/benchmark.html" class="external-link">benchmark</a></span><span class="op">(</span>replications <span class="op">=</span> <span class="fl">10</span>, order <span class="op">=</span> <span class="st">"relative"</span>, columns <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"test"</span>, <span class="st">"relative"</span>, <span class="st">"elapsed"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> eigen <span class="op">=</span> <span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO_SFO</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.15</span>, f_parent_to_m1 <span class="op">=</span> <span class="fl">0.5</span>, k_m1 <span class="op">=</span> <span class="fl">0.01</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">20</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> solution_type <span class="op">=</span> <span class="st">"eigen"</span><span class="op">)</span><span class="op">[</span><span class="fl">201</span>,<span class="op">]</span>,</span></span> +<span class="r-in"><span> deSolve_compiled <span class="op">=</span> <span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO_SFO</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.15</span>, f_parent_to_m1 <span class="op">=</span> <span class="fl">0.5</span>, k_m1 <span class="op">=</span> <span class="fl">0.01</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">20</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> solution_type <span class="op">=</span> <span class="st">"deSolve"</span><span class="op">)</span><span class="op">[</span><span class="fl">201</span>,<span class="op">]</span>,</span></span> +<span class="r-in"><span> deSolve <span class="op">=</span> <span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO_SFO</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.15</span>, f_parent_to_m1 <span class="op">=</span> <span class="fl">0.5</span>, k_m1 <span class="op">=</span> <span class="fl">0.01</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">20</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, use_compiled <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">[</span><span class="fl">201</span>,<span class="op">]</span>,</span></span> +<span class="r-in"><span> analytical <span class="op">=</span> <span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO_SFO</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.15</span>, f_parent_to_m1 <span class="op">=</span> <span class="fl">0.5</span>, k_m1 <span class="op">=</span> <span class="fl">0.01</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">20</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> solution_type <span class="op">=</span> <span class="st">"analytical"</span>, use_compiled <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">[</span><span class="fl">201</span>,<span class="op">]</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="op">}</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> test relative elapsed</span> +<span class="r-out co"><span class="r-pr">#></span> 2 deSolve_compiled 1.0 0.004</span> +<span class="r-out co"><span class="r-pr">#></span> 4 analytical 1.0 0.004</span> +<span class="r-out co"><span class="r-pr">#></span> 1 eigen 5.5 0.022</span> +<span class="r-out co"><span class="r-pr">#></span> 3 deSolve 51.0 0.204</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span> <span class="co"># Predict from a fitted model</span></span></span> +<span class="r-in"><span> <span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">f</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> DLSODA- At current T (=R1), MXSTEP (=I1) steps </span> +<span class="r-out co"><span class="r-pr">#></span> taken on this call before reaching TOUT </span> +<span class="r-out co"><span class="r-pr">#></span> In above message, I1 = 1</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> In above message, R1 = 9.99904e-07</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>an excessive amount of work (> maxsteps ) was done, but integration was not successful - increase maxsteps</span> +<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Returning early. Results are accurate, as far as they go</span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in out[available, var]:</span> (subscript) logical subscript too long</span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -367,7 +408,7 @@ as these always return mapped output.</p></dd> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/mkinresplot.html b/docs/reference/mkinresplot.html index 654b93d8..6966cd90 100644 --- a/docs/reference/mkinresplot.html +++ b/docs/reference/mkinresplot.html @@ -20,7 +20,7 @@ argument show_residuals = TRUE."><!-- mathjax --><script src="https://cdnjs.clou </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -47,19 +47,25 @@ argument show_residuals = TRUE."><!-- mathjax --><script src="https://cdnjs.clou <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -93,60 +99,87 @@ argument <code>show_residuals = TRUE</code>.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">mkinresplot</span><span class="op">(</span> - <span class="va">object</span>, - obs_vars <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">object</span><span class="op">$</span><span class="va">mkinmod</span><span class="op">$</span><span class="va">map</span><span class="op">)</span>, - xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1.1</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="va">object</span><span class="op">$</span><span class="va">data</span><span class="op">$</span><span class="va">time</span><span class="op">)</span><span class="op">)</span>, - standardized <span class="op">=</span> <span class="cn">FALSE</span>, - xlab <span class="op">=</span> <span class="st">"Time"</span>, - ylab <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">standardized</span>, <span class="st">"Standardized residual"</span>, <span class="st">"Residual"</span><span class="op">)</span>, - maxabs <span class="op">=</span> <span class="st">"auto"</span>, - legend <span class="op">=</span> <span class="cn">TRUE</span>, - lpos <span class="op">=</span> <span class="st">"topright"</span>, - col_obs <span class="op">=</span> <span class="st">"auto"</span>, - pch_obs <span class="op">=</span> <span class="st">"auto"</span>, - frame <span class="op">=</span> <span class="cn">TRUE</span>, - <span class="va">...</span> -<span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mkinresplot</span><span class="op">(</span></span> +<span> <span class="va">object</span>,</span> +<span> obs_vars <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">object</span><span class="op">$</span><span class="va">mkinmod</span><span class="op">$</span><span class="va">map</span><span class="op">)</span>,</span> +<span> xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1.1</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="va">object</span><span class="op">$</span><span class="va">data</span><span class="op">$</span><span class="va">time</span><span class="op">)</span><span class="op">)</span>,</span> +<span> standardized <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> xlab <span class="op">=</span> <span class="st">"Time"</span>,</span> +<span> ylab <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">standardized</span>, <span class="st">"Standardized residual"</span>, <span class="st">"Residual"</span><span class="op">)</span>,</span> +<span> maxabs <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> legend <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> lpos <span class="op">=</span> <span class="st">"topright"</span>,</span> +<span> col_obs <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> pch_obs <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> frame <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>object</dt> <dd><p>A fit represented in an <code><a href="mkinfit.html">mkinfit</a></code> object.</p></dd> + + <dt>obs_vars</dt> <dd><p>A character vector of names of the observed variables for which residuals should be plotted. Defaults to all observed variables in the model</p></dd> + + <dt>xlim</dt> <dd><p>plot range in x direction.</p></dd> + + <dt>standardized</dt> <dd><p>Should the residuals be standardized by dividing by the standard deviation given by the error model of the fit?</p></dd> + + <dt>xlab</dt> <dd><p>Label for the x axis.</p></dd> + + <dt>ylab</dt> <dd><p>Label for the y axis.</p></dd> + + <dt>maxabs</dt> <dd><p>Maximum absolute value of the residuals. This is used for the scaling of the y axis and defaults to "auto".</p></dd> + + <dt>legend</dt> <dd><p>Should a legend be plotted?</p></dd> + + <dt>lpos</dt> <dd><p>Where should the legend be placed? Default is "topright". Will be passed on to <code><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></code>.</p></dd> + + <dt>col_obs</dt> <dd><p>Colors for the observed variables.</p></dd> + + <dt>pch_obs</dt> <dd><p>Symbols to be used for the observed variables.</p></dd> + + <dt>frame</dt> <dd><p>Should a frame be drawn around the plots?</p></dd> + + <dt>...</dt> <dd><p>further arguments passed to <code><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></code>.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>Nothing is returned by this function, as it is called for its side + + +<p>Nothing is returned by this function, as it is called for its side effect, namely to produce a plot.</p> </div> <div id="see-also"> @@ -162,14 +195,14 @@ combining the plot of the fit and the residual plot.</p></div> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"><span class="va">model</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">model</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span class="r-in"><span><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span> -<span class="r-in"><span class="fu">mkinresplot</span><span class="op">(</span><span class="va">fit</span>, <span class="st">"m1"</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu">mkinresplot</span><span class="op">(</span><span class="va">fit</span>, <span class="st">"m1"</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="mkinresplot-1.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -184,7 +217,7 @@ combining the plot of the fit and the residual plot.</p></div> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/mmkin-1.png b/docs/reference/mmkin-1.png Binary files differindex 75cbc054..8ad9c11d 100644 --- a/docs/reference/mmkin-1.png +++ b/docs/reference/mmkin-1.png diff --git a/docs/reference/mmkin-2.png b/docs/reference/mmkin-2.png Binary files differindex 76f42b73..da2a48a8 100644 --- a/docs/reference/mmkin-2.png +++ b/docs/reference/mmkin-2.png diff --git a/docs/reference/mmkin-3.png b/docs/reference/mmkin-3.png Binary files differindex e92d81b2..10d3f35b 100644 --- a/docs/reference/mmkin-3.png +++ b/docs/reference/mmkin-3.png diff --git a/docs/reference/mmkin-4.png b/docs/reference/mmkin-4.png Binary files differindex 8d747ffe..132380a8 100644 --- a/docs/reference/mmkin-4.png +++ b/docs/reference/mmkin-4.png diff --git a/docs/reference/mmkin-5.png b/docs/reference/mmkin-5.png Binary files differindex ffd7640d..4bfcc55e 100644 --- a/docs/reference/mmkin-5.png +++ b/docs/reference/mmkin-5.png diff --git a/docs/reference/mmkin.html b/docs/reference/mmkin.html index cbb46ab0..686c9310 100644 --- a/docs/reference/mmkin.html +++ b/docs/reference/mmkin.html @@ -20,7 +20,7 @@ datasets specified in its first two arguments."><!-- mathjax --><script src="htt </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -47,19 +47,25 @@ datasets specified in its first two arguments."><!-- mathjax --><script src="htt <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -92,16 +98,16 @@ datasets specified in its first two arguments.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">mmkin</span><span class="op">(</span> - models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, - <span class="va">datasets</span>, - cores <span class="op">=</span> <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="fl">1</span> <span class="kw">else</span> <span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span>, - cluster <span class="op">=</span> <span class="cn">NULL</span>, - <span class="va">...</span> -<span class="op">)</span> - -<span class="co"># S3 method for mmkin</span> -<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mmkin</span><span class="op">(</span></span> +<span> models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>,</span> +<span> <span class="va">datasets</span>,</span> +<span> cores <span class="op">=</span> <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="fl">1</span> <span class="kw">else</span> <span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span>,</span> +<span> cluster <span class="op">=</span> <span class="cn">NULL</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> @@ -110,9 +116,13 @@ datasets specified in its first two arguments.</p> <dd><p>Either a character vector of shorthand names like <code>c("SFO", "FOMC", "DFOP", "HS", "SFORB")</code>, or an optionally named list of <code><a href="mkinmod.html">mkinmod</a></code> objects.</p></dd> + + <dt>datasets</dt> <dd><p>An optionally named list of datasets suitable as observed data for <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd> + + <dt>cores</dt> <dd><p>The number of cores to be used for multicore processing. This is only used when the <code>cluster</code> argument is <code>NULL</code>. On Windows @@ -120,17 +130,29 @@ machines, cores > 1 is not supported, you need to use the <code>cluster</code argument to use multiple logical processors. Per default, all cores detected by <code><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">parallel::detectCores()</a></code> are used, except on Windows where the default is 1.</p></dd> + + <dt>cluster</dt> -<dd><p>A cluster as returned by <code><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makeCluster</a></code> to be used +<dd><p>A cluster as returned by <code>makeCluster</code> to be used for parallel execution.</p></dd> + + <dt>...</dt> <dd><p>Not used.</p></dd> + + <dt>x</dt> <dd><p>An mmkin object.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>A two-dimensional <code><a href="https://rdrr.io/r/base/array.html" class="external-link">array</a></code> of <code><a href="mkinfit.html">mkinfit</a></code>objects and/or try-errors that can be indexed using the model names for the + + +<p>A two-dimensional <code><a href="https://rdrr.io/r/base/array.html" class="external-link">array</a></code> of <code><a href="mkinfit.html">mkinfit</a></code></p> + + +<p>objects and/or try-errors that can be indexed using the model names for the first index (row index) and the dataset names for the second index (column index).</p> </div> @@ -146,33 +168,33 @@ plotting.</p></div> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"><span class="va">m_synth_SFO_lin</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M1"</span><span class="op">)</span>,</span> -<span class="r-in"> M1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span> -<span class="r-in"> M2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="va">m_synth_SFO_lin</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"></span> -<span class="r-in"><span class="va">m_synth_FOMC_lin</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="st">"M1"</span><span class="op">)</span>,</span> -<span class="r-in"> M1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span> -<span class="r-in"> M2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">m_synth_FOMC_lin</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="st">"M1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"></span> -<span class="r-in"><span class="va">models</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>SFO_lin <span class="op">=</span> <span class="va">m_synth_SFO_lin</span>, FOMC_lin <span class="op">=</span> <span class="va">m_synth_FOMC_lin</span><span class="op">)</span></span> -<span class="r-in"><span class="va">datasets</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">synthetic_data_for_UBA_2014</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">]</span>, <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">datasets</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">time_default</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">fits.0</span> <span class="op"><-</span> <span class="fu">mmkin</span><span class="op">(</span><span class="va">models</span>, <span class="va">datasets</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"><span class="va">time_1</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">fits.4</span> <span class="op"><-</span> <span class="fu">mmkin</span><span class="op">(</span><span class="va">models</span>, <span class="va">datasets</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">time_default</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">models</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>SFO_lin <span class="op">=</span> <span class="va">m_synth_SFO_lin</span>, FOMC_lin <span class="op">=</span> <span class="va">m_synth_FOMC_lin</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">datasets</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">synthetic_data_for_UBA_2014</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">]</span>, <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">datasets</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">time_default</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">fits.0</span> <span class="op"><-</span> <span class="fu">mmkin</span><span class="op">(</span><span class="va">models</span>, <span class="va">datasets</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">time_1</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">fits.4</span> <span class="op"><-</span> <span class="fu">mmkin</span><span class="op">(</span><span class="va">models</span>, <span class="va">datasets</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">time_default</span></span></span> <span class="r-out co"><span class="r-pr">#></span> user system elapsed </span> -<span class="r-out co"><span class="r-pr">#></span> 35.152 1.185 8.439 </span> -<span class="r-in"><span class="va">time_1</span></span> +<span class="r-out co"><span class="r-pr">#></span> 5.526 0.809 2.006 </span> +<span class="r-in"><span><span class="va">time_1</span></span></span> <span class="r-out co"><span class="r-pr">#></span> user system elapsed </span> -<span class="r-out co"><span class="r-pr">#></span> 14.744 0.008 14.768 </span> -<span class="r-in"></span> -<span class="r-in"><span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[[</span><span class="st">"SFO_lin"</span>, <span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span> +<span class="r-out co"><span class="r-pr">#></span> 5.403 0.008 5.412 </span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[[</span><span class="st">"SFO_lin"</span>, <span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> $ff</span> <span class="r-out co"><span class="r-pr">#></span> parent_M1 parent_sink M1_M2 M1_sink </span> <span class="r-out co"><span class="r-pr">#></span> 0.7340481 0.2659519 0.7505683 0.2494317 </span> @@ -180,51 +202,44 @@ plotting.</p></div> <span class="r-out co"><span class="r-pr">#></span> $distimes</span> <span class="r-out co"><span class="r-pr">#></span> DT50 DT90</span> <span class="r-out co"><span class="r-pr">#></span> parent 0.877769 2.915885</span> -<span class="r-out co"><span class="r-pr">#></span> M1 2.325744 7.725955</span> -<span class="r-out co"><span class="r-pr">#></span> M2 33.720111 112.015785</span> +<span class="r-out co"><span class="r-pr">#></span> M1 2.325744 7.725956</span> +<span class="r-out co"><span class="r-pr">#></span> M2 33.720100 112.015749</span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># plot.mkinfit handles rows or columns of mmkin result objects</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[</span><span class="fl">1</span>, <span class="op">]</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># plot.mkinfit handles rows or columns of mmkin result objects</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[</span><span class="fl">1</span>, <span class="op">]</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="mmkin-1.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[</span><span class="fl">1</span>, <span class="op">]</span>, obs_var <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[</span><span class="fl">1</span>, <span class="op">]</span>, obs_var <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="mmkin-2.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[</span>, <span class="fl">1</span><span class="op">]</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[</span>, <span class="fl">1</span><span class="op">]</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="mmkin-3.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="co"># Use double brackets to extract a single mkinfit object, which will be plotted</span></span> -<span class="r-in"><span class="co"># by plot.mkinfit and can be plotted using plot_sep</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[[</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, sep_obs <span class="op">=</span> <span class="cn">TRUE</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span class="r-in"><span><span class="co"># Use double brackets to extract a single mkinfit object, which will be plotted</span></span></span> +<span class="r-in"><span><span class="co"># by plot.mkinfit and can be plotted using plot_sep</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[[</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, sep_obs <span class="op">=</span> <span class="cn">TRUE</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="mmkin-4.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[[</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span> -<span class="r-in"><span class="co"># Plotting with mmkin (single brackets, extracting an mmkin object) does not</span></span> -<span class="r-in"><span class="co"># allow to plot the observed variables separately</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">]</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[[</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># Plotting with mmkin (single brackets, extracting an mmkin object) does not</span></span></span> +<span class="r-in"><span><span class="co"># allow to plot the observed variables separately</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">]</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="mmkin-5.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># On Windows, we can use multiple cores by making a cluster using the parallel</span></span> -<span class="r-in"><span class="co"># package, which gets loaded with mkin, and passing it to mmkin, e.g.</span></span> -<span class="r-in"><span class="va">cl</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="fl">12</span><span class="op">)</span></span> -<span class="r-in"><span class="va">f</span> <span class="op"><-</span> <span class="fu">mmkin</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>A <span class="op">=</span> <span class="va">FOCUS_2006_A</span>, B <span class="op">=</span> <span class="va">FOCUS_2006_B</span>, C <span class="op">=</span> <span class="va">FOCUS_2006_C</span>, D <span class="op">=</span> <span class="va">FOCUS_2006_D</span><span class="op">)</span>,</span> -<span class="r-in"> cluster <span class="op">=</span> <span class="va">cl</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span> -<span class="r-out co"><span class="r-pr">#></span> <mmkin> object</span> -<span class="r-out co"><span class="r-pr">#></span> Status of individual fits:</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> dataset</span> -<span class="r-out co"><span class="r-pr">#></span> model A B C D </span> -<span class="r-out co"><span class="r-pr">#></span> SFO OK OK OK OK</span> -<span class="r-out co"><span class="r-pr">#></span> FOMC C OK OK OK</span> -<span class="r-out co"><span class="r-pr">#></span> DFOP OK OK OK OK</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> OK: No warnings</span> -<span class="r-out co"><span class="r-pr">#></span> C: Optimisation did not converge:</span> -<span class="r-out co"><span class="r-pr">#></span> false convergence (8)</span> -<span class="r-in"><span class="co"># We get false convergence for the FOMC fit to FOCUS_2006_A because this</span></span> -<span class="r-in"><span class="co"># dataset is really SFO, and the FOMC fit is overparameterised</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">stopCluster</a></span><span class="op">(</span><span class="va">cl</span><span class="op">)</span></span> -<span class="r-in"><span class="co"># }</span></span> -<span class="r-in"></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># On Windows, we can use multiple cores by making a cluster using the parallel</span></span></span> +<span class="r-in"><span><span class="co"># package, which gets loaded with mkin, and passing it to mmkin, e.g.</span></span></span> +<span class="r-in"><span><span class="va">cl</span> <span class="op"><-</span> <span class="fu">makePSOCKcluster</span><span class="op">(</span><span class="fl">12</span><span class="op">)</span></span></span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in makePSOCKcluster(12):</span> could not find function "makePSOCKcluster"</span> +<span class="r-in"><span><span class="va">f</span> <span class="op"><-</span> <span class="fu">mmkin</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>A <span class="op">=</span> <span class="va">FOCUS_2006_A</span>, B <span class="op">=</span> <span class="va">FOCUS_2006_B</span>, C <span class="op">=</span> <span class="va">FOCUS_2006_C</span>, D <span class="op">=</span> <span class="va">FOCUS_2006_D</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> cluster <span class="op">=</span> <span class="va">cl</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in system.time({ if (is.null(cluster)) { results <- parallel::mclapply(as.list(1:n.fits), fit_function, mc.cores = cores, mc.preschedule = FALSE) } else { results <- parallel::parLapply(cluster, as.list(1:n.fits), fit_function) }}):</span> object 'cl' not found</span> +<span class="r-msg co"><span class="r-pr">#></span> Timing stopped at: 0 0 0</span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span></span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in print(f):</span> object 'f' not found</span> +<span class="r-in"><span><span class="co"># We get false convergence for the FOMC fit to FOCUS_2006_A because this</span></span></span> +<span class="r-in"><span><span class="co"># dataset is really SFO, and the FOMC fit is overparameterised</span></span></span> +<span class="r-in"><span><span class="fu">stopCluster</span><span class="op">(</span><span class="va">cl</span><span class="op">)</span></span></span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in stopCluster(cl):</span> could not find function "stopCluster"</span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -239,7 +254,7 @@ plotting.</p></div> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/multistart-1.png b/docs/reference/multistart-1.png Binary files differnew file mode 100644 index 00000000..cd12bac1 --- /dev/null +++ b/docs/reference/multistart-1.png diff --git a/docs/reference/multistart-2.png b/docs/reference/multistart-2.png Binary files differnew file mode 100644 index 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It is +inspired by the article on practical identifiabiliy in the frame of nonlinear +mixed-effects models by Duchesne et al (2021)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> +<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> +<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + + + <div class="container template-reference-topic"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <div class="container"> + <div class="navbar-header"> + <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> + <span class="sr-only">Toggle navigation</span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + </button> + <span class="navbar-brand"> + <a class="navbar-link" href="../index.html">mkin</a> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + </span> + </div> + + <div id="navbar" class="navbar-collapse collapse"> + <ul class="nav navbar-nav"><li> + <a href="../reference/index.html">Functions and data</a> +</li> +<li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> + Articles + + <span class="caret"></span> + </a> + <ul class="dropdown-menu" role="menu"><li> + <a href="../articles/mkin.html">Introduction to mkin</a> + </li> + <li> + <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> + </li> + <li> + <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> + </li> + <li> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + </li> + <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> + <li> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> +<li> + <a href="../news/index.html">News</a> +</li> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> + <span class="fab fa-github fa-lg"></span> + + </a> +</li> + </ul></div><!--/.nav-collapse --> + </div><!--/.container --> +</div><!--/.navbar --> + + + + </header><div class="row"> + <div class="col-md-9 contents"> + <div class="page-header"> + <h1>Perform a hierarchical model fit with multiple starting values</h1> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/multistart.R" class="external-link"><code>R/multistart.R</code></a></small> + <div class="hidden name"><code>multistart.Rd</code></div> + </div> + + <div class="ref-description"> + <p>The purpose of this method is to check if a certain algorithm for fitting +nonlinear hierarchical models (also known as nonlinear mixed-effects models) +will reliably yield results that are sufficiently similar to each other, if +started with a certain range of reasonable starting parameters. It is +inspired by the article on practical identifiabiliy in the frame of nonlinear +mixed-effects models by Duchesne et al (2021).</p> + </div> + + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">multistart</span><span class="op">(</span></span> +<span> <span class="va">object</span>,</span> +<span> n <span class="op">=</span> <span class="fl">50</span>,</span> +<span> cores <span class="op">=</span> <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="fl">1</span> <span class="kw">else</span> <span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span>,</span> +<span> cluster <span class="op">=</span> <span class="cn">NULL</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for saem.mmkin</span></span> +<span><span class="fu">multistart</span><span class="op">(</span><span class="va">object</span>, n <span class="op">=</span> <span class="fl">50</span>, cores <span class="op">=</span> <span class="fl">1</span>, cluster <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for multistart</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="fu">best</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for default</span></span> +<span><span class="fu">best</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="fu">which.best</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for default</span></span> +<span><span class="fu">which.best</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>object</dt> +<dd><p>The fit object to work with</p></dd> + + +<dt>n</dt> +<dd><p>How many different combinations of starting parameters should be +used?</p></dd> + + +<dt>cores</dt> +<dd><p>How many fits should be run in parallel (only on posix platforms)?</p></dd> + + +<dt>cluster</dt> +<dd><p>A cluster as returned by <a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">parallel::makeCluster</a> to be used +for parallel execution.</p></dd> + + +<dt>...</dt> +<dd><p>Passed to the update function.</p></dd> + + +<dt>x</dt> +<dd><p>The multistart object to print</p></dd> + +</dl></div> + <div id="value"> + <h2>Value</h2> + + +<p>A list of <a href="saem.html">saem.mmkin</a> objects, with class attributes +'multistart.saem.mmkin' and 'multistart'.</p> + + +<p>The object with the highest likelihood</p> + + +<p>The index of the object with the highest likelihood</p> + </div> + <div id="references"> + <h2>References</h2> + <p>Duchesne R, Guillemin A, Gandrillon O, Crauste F. Practical +identifiability in the frame of nonlinear mixed effects models: the example +of the in vitro erythropoiesis. BMC Bioinformatics. 2021 Oct 4;22(1):478. +doi: 10.1186/s12859-021-04373-4.</p> + </div> + <div id="see-also"> + <h2>See also</h2> + <div class="dont-index"><p><a href="parplot.html">parplot</a>, <a href="llhist.html">llhist</a></p></div> + </div> + + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">dmta_ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span></span> +<span class="r-in"><span> <span class="va">ds_i</span> <span class="op"><-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span></span> +<span class="r-in"><span> <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op"><-</span> <span class="st">"DMTA"</span></span></span> +<span class="r-in"><span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op"><-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span></span></span> +<span class="r-in"><span> <span class="va">ds_i</span></span></span> +<span class="r-in"><span><span class="op">}</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">f_mmkin</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">dmta_ds</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, cores <span class="op">=</span> <span class="fl">7</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">f_saem_full</span> <span class="op"><-</span> <span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">f_saem_full_multi</span> <span class="op"><-</span> <span class="fu">multistart</span><span class="op">(</span><span class="va">f_saem_full</span>, n <span class="op">=</span> <span class="fl">16</span>, cores <span class="op">=</span> <span class="fl">16</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_full_multi</span>, lpos <span class="op">=</span> <span class="st">"topleft"</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="multistart-1.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_full</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] "sd(log_k2)"</span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">f_saem_reduced</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_full</span>, no_random_effect <span class="op">=</span> <span class="st">"log_k2"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_reduced</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># On Windows, we need to create a cluster first. When working with</span></span></span> +<span class="r-in"><span><span class="co"># such a cluster, we need to export the mmkin object to the cluster</span></span></span> +<span class="r-in"><span><span class="co"># nodes, as it is referred to when updating the saem object on the nodes.</span></span></span> +<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">parallel</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">cl</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="fl">12</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">f_saem_reduced_multi</span> <span class="op"><-</span> <span class="fu">multistart</span><span class="op">(</span><span class="va">f_saem_reduced</span>, n <span class="op">=</span> <span class="fl">16</span>, cluster <span class="op">=</span> <span class="va">cl</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span>, lpos <span class="op">=</span> <span class="st">"topright"</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="multistart-2.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">stopCluster</a></span><span class="op">(</span><span class="va">cl</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> +</code></pre></div> + </div> + </div> + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> +</div> + + + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +</div> + + </footer></div> + + + + + + + </body></html> + diff --git a/docs/reference/nafta.html b/docs/reference/nafta.html index 1d22b617..5906db4c 100644 --- a/docs/reference/nafta.html +++ b/docs/reference/nafta.html @@ -21,7 +21,7 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP."><! </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -48,19 +48,25 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP."><! <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -95,10 +101,10 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nafta</span><span class="op">(</span><span class="va">ds</span>, title <span class="op">=</span> <span class="cn">NA</span>, quiet <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span> - -<span class="co"># S3 method for nafta</span> -<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, digits <span class="op">=</span> <span class="fl">3</span>, <span class="va">...</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">nafta</span><span class="op">(</span><span class="va">ds</span>, title <span class="op">=</span> <span class="cn">NA</span>, quiet <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for nafta</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, digits <span class="op">=</span> <span class="fl">3</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> </div> <div id="source"> @@ -120,22 +126,35 @@ Degradation time values specified by the <code>time</code> argument, one column called "name" with the grouping of the observed values, and finally one column of observed values called "value".</p></dd> + + <dt>title</dt> <dd><p>Optional title of the dataset</p></dd> + + <dt>quiet</dt> <dd><p>Should the evaluation text be shown?</p></dd> + + <dt>...</dt> <dd><p>Further arguments passed to <code><a href="mmkin.html">mmkin</a></code> (not for the printing method).</p></dd> + + <dt>x</dt> <dd><p>An <code>nafta</code> object.</p></dd> + + <dt>digits</dt> <dd><p>Number of digits to be used for printing parameters and dissipation times.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>An list of class <code>nafta</code>. The list element named "mmkin" is the + + +<p>An list of class <code>nafta</code>. The list element named "mmkin" is the <code><a href="mmkin.html">mmkin</a></code> object containing the fits of the three models. The list element named "title" contains the title of the dataset used. The list element "data" contains the dataset used in the fits.</p> @@ -147,13 +166,13 @@ list element "data" contains the dataset used in the fits.</p> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"> <span class="va">nafta_evaluation</span> <span class="op"><-</span> <span class="fu">nafta</span><span class="op">(</span><span class="va">NAFTA_SOP_Appendix_D</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="va">nafta_evaluation</span> <span class="op"><-</span> <span class="fu">nafta</span><span class="op">(</span><span class="va">NAFTA_SOP_Appendix_D</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span> <span class="r-msg co"><span class="r-pr">#></span> The representative half-life of the IORE model is longer than the one corresponding</span> <span class="r-msg co"><span class="r-pr">#></span> to the terminal degradation rate found with the DFOP model.</span> <span class="r-msg co"><span class="r-pr">#></span> The representative half-life obtained from the DFOP model may be used</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">nafta_evaluation</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">nafta_evaluation</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Sums of squares:</span> <span class="r-out co"><span class="r-pr">#></span> SFO IORE DFOP </span> <span class="r-out co"><span class="r-pr">#></span> 1378.6832 615.7730 517.8836 </span> @@ -192,9 +211,9 @@ list element "data" contains the dataset used in the fits.</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Representative half-life:</span> <span class="r-out co"><span class="r-pr">#></span> [1] 841.41</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">nafta_evaluation</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">nafta_evaluation</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="nafta-1.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -209,7 +228,7 @@ list element "data" contains the dataset used in the fits.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/nlme-1.png b/docs/reference/nlme-1.png Binary files differindex 0b6cb78d..f4d04e1d 100644 --- a/docs/reference/nlme-1.png +++ b/docs/reference/nlme-1.png diff --git a/docs/reference/nlme-2.png b/docs/reference/nlme-2.png Binary files differindex ef152270..d9512f41 100644 --- a/docs/reference/nlme-2.png +++ b/docs/reference/nlme-2.png diff --git a/docs/reference/nlme.html b/docs/reference/nlme.html index ff8de9f3..83576e56 100644 --- a/docs/reference/nlme.html +++ b/docs/reference/nlme.html @@ -20,7 +20,7 @@ datasets. They are used internally by the nlme.mmkin() method."><!-- mathjax --> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -47,19 +47,25 @@ datasets. They are used internally by the nlme.mmkin() method."><!-- mathjax --> <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -93,20 +99,25 @@ datasets. They are used internally by the <code><a href="nlme.mmkin.html">nlme.m </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">nlme_function</span><span class="op">(</span><span class="va">object</span><span class="op">)</span> - -<span class="fu">nlme_data</span><span class="op">(</span><span class="va">object</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">nlme_function</span><span class="op">(</span><span class="va">object</span><span class="op">)</span></span> +<span></span> +<span><span class="fu">nlme_data</span><span class="op">(</span><span class="va">object</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>object</dt> <dd><p>An mmkin row object containing several fits of the same model to different datasets</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>A function that can be used with nlme -A <code><a href="https://rdrr.io/pkg/nlme/man/groupedData.html" class="external-link">groupedData</a></code> object</p> + + +<p>A function that can be used with nlme</p> + + +<p>A <code><a href="https://rdrr.io/pkg/nlme/man/groupedData.html" class="external-link">groupedData</a></code> object</p> </div> <div id="see-also"> <h2>See also</h2> @@ -115,78 +126,78 @@ A <code><a href="https://rdrr.io/pkg/nlme/man/groupedData.html" class="external- <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span> -<span class="r-in"><span class="va">m_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"><span class="va">d_SFO_1</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">98</span><span class="op">)</span>, <span class="va">sampling_times</span><span class="op">)</span></span> -<span class="r-in"><span class="va">d_SFO_1_long</span> <span class="op"><-</span> <span class="fu"><a href="mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">d_SFO_1</span>, time <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span> -<span class="r-in"><span class="va">d_SFO_2</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.05</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">102</span><span class="op">)</span>, <span class="va">sampling_times</span><span class="op">)</span></span> -<span class="r-in"><span class="va">d_SFO_2_long</span> <span class="op"><-</span> <span class="fu"><a href="mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">d_SFO_2</span>, time <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span> -<span class="r-in"><span class="va">d_SFO_3</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.02</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">103</span><span class="op">)</span>, <span class="va">sampling_times</span><span class="op">)</span></span> -<span class="r-in"><span class="va">d_SFO_3_long</span> <span class="op"><-</span> <span class="fu"><a href="mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">d_SFO_3</span>, time <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">d1</span> <span class="op"><-</span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_SFO_1</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">3</span>, n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span> -<span class="r-in"><span class="va">d2</span> <span class="op"><-</span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_SFO_2</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">2</span>, n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span> -<span class="r-in"><span class="va">d3</span> <span class="op"><-</span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_SFO_3</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">4</span>, n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span> -<span class="r-in"><span class="va">ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>d1 <span class="op">=</span> <span class="va">d1</span>, d2 <span class="op">=</span> <span class="va">d2</span>, d3 <span class="op">=</span> <span class="va">d3</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">ds</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="va">mean_dp</span> <span class="op"><-</span> <span class="fu"><a href="mean_degparms.html">mean_degparms</a></span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span> -<span class="r-in"><span class="va">grouped_data</span> <span class="op"><-</span> <span class="fu">nlme_data</span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span> -<span class="r-in"><span class="va">nlme_f</span> <span class="op"><-</span> <span class="fu">nlme_function</span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span> -<span class="r-in"><span class="co"># These assignments are necessary for these objects to be</span></span> -<span class="r-in"><span class="co"># visible to nlme and augPred when evaluation is done by</span></span> -<span class="r-in"><span class="co"># pkgdown to generate the html docs.</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="st">"nlme_f"</span>, <span class="va">nlme_f</span>, <span class="fu"><a href="https://rdrr.io/r/base/environment.html" class="external-link">globalenv</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="st">"grouped_data"</span>, <span class="va">grouped_data</span>, <span class="fu"><a href="https://rdrr.io/r/base/environment.html" class="external-link">globalenv</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://svn.r-project.org/R-packages/trunk/nlme/" class="external-link">nlme</a></span><span class="op">)</span></span> -<span class="r-in"><span class="va">m_nlme</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">value</span> <span class="op">~</span> <span class="fu">nlme_f</span><span class="op">(</span><span class="va">name</span>, <span class="va">time</span>, <span class="va">parent_0</span>, <span class="va">log_k_parent_sink</span><span class="op">)</span>,</span> -<span class="r-in"> data <span class="op">=</span> <span class="va">grouped_data</span>,</span> -<span class="r-in"> fixed <span class="op">=</span> <span class="va">parent_0</span> <span class="op">+</span> <span class="va">log_k_parent_sink</span> <span class="op">~</span> <span class="fl">1</span>,</span> -<span class="r-in"> random <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/pdDiag.html" class="external-link">pdDiag</a></span><span class="op">(</span><span class="va">parent_0</span> <span class="op">+</span> <span class="va">log_k_parent_sink</span> <span class="op">~</span> <span class="fl">1</span><span class="op">)</span>,</span> -<span class="r-in"> start <span class="op">=</span> <span class="va">mean_dp</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m_nlme</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">m_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">d_SFO_1</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">98</span><span class="op">)</span>, <span class="va">sampling_times</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">d_SFO_1_long</span> <span class="op"><-</span> <span class="fu"><a href="mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">d_SFO_1</span>, time <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">d_SFO_2</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.05</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">102</span><span class="op">)</span>, <span class="va">sampling_times</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">d_SFO_2_long</span> <span class="op"><-</span> <span class="fu"><a href="mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">d_SFO_2</span>, time <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">d_SFO_3</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_SFO</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.02</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">103</span><span class="op">)</span>, <span class="va">sampling_times</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">d_SFO_3_long</span> <span class="op"><-</span> <span class="fu"><a href="mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">d_SFO_3</span>, time <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">d1</span> <span class="op"><-</span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_SFO_1</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">3</span>, n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">d2</span> <span class="op"><-</span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_SFO_2</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">2</span>, n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">d3</span> <span class="op"><-</span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_SFO_3</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">4</span>, n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>d1 <span class="op">=</span> <span class="va">d1</span>, d2 <span class="op">=</span> <span class="va">d2</span>, d3 <span class="op">=</span> <span class="va">d3</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">ds</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">mean_dp</span> <span class="op"><-</span> <span class="fu"><a href="mean_degparms.html">mean_degparms</a></span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">grouped_data</span> <span class="op"><-</span> <span class="fu">nlme_data</span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">nlme_f</span> <span class="op"><-</span> <span class="fu">nlme_function</span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># These assignments are necessary for these objects to be</span></span></span> +<span class="r-in"><span><span class="co"># visible to nlme and augPred when evaluation is done by</span></span></span> +<span class="r-in"><span><span class="co"># pkgdown to generate the html docs.</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="st">"nlme_f"</span>, <span class="va">nlme_f</span>, <span class="fu"><a href="https://rdrr.io/r/base/environment.html" class="external-link">globalenv</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="st">"grouped_data"</span>, <span class="va">grouped_data</span>, <span class="fu"><a href="https://rdrr.io/r/base/environment.html" class="external-link">globalenv</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://svn.r-project.org/R-packages/trunk/nlme/" class="external-link">nlme</a></span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">m_nlme</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">value</span> <span class="op">~</span> <span class="fu">nlme_f</span><span class="op">(</span><span class="va">name</span>, <span class="va">time</span>, <span class="va">parent_0</span>, <span class="va">log_k_parent_sink</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> data <span class="op">=</span> <span class="va">grouped_data</span>,</span></span> +<span class="r-in"><span> fixed <span class="op">=</span> <span class="va">parent_0</span> <span class="op">+</span> <span class="va">log_k_parent_sink</span> <span class="op">~</span> <span class="fl">1</span>,</span></span> +<span class="r-in"><span> random <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/pdDiag.html" class="external-link">pdDiag</a></span><span class="op">(</span><span class="va">parent_0</span> <span class="op">+</span> <span class="va">log_k_parent_sink</span> <span class="op">~</span> <span class="fl">1</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> start <span class="op">=</span> <span class="va">mean_dp</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m_nlme</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Nonlinear mixed-effects model fit by maximum likelihood</span> <span class="r-out co"><span class="r-pr">#></span> Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink) </span> <span class="r-out co"><span class="r-pr">#></span> Data: grouped_data </span> <span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> -<span class="r-out co"><span class="r-pr">#></span> 300.6824 310.2426 -145.3412</span> +<span class="r-out co"><span class="r-pr">#></span> 266.6428 275.8935 -128.3214</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Random effects:</span> <span class="r-out co"><span class="r-pr">#></span> Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1)</span> <span class="r-out co"><span class="r-pr">#></span> Level: ds</span> <span class="r-out co"><span class="r-pr">#></span> Structure: Diagonal</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_parent_sink Residual</span> -<span class="r-out co"><span class="r-pr">#></span> StdDev: 1.697361 0.6801209 3.666073</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_parent_sink Residual</span> +<span class="r-out co"><span class="r-pr">#></span> StdDev: 0.000368491 0.7058039 3.065183</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Fixed effects: parent_0 + log_k_parent_sink ~ 1 </span> -<span class="r-out co"><span class="r-pr">#></span> Value Std.Error DF t-value p-value</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 100.99378 1.3890416 46 72.70753 0</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_parent_sink -3.07521 0.4018589 46 -7.65246 0</span> +<span class="r-out co"><span class="r-pr">#></span> Value Std.Error DF t-value p-value</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 101.18323 0.7900461 43 128.07257 0</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_parent_sink -3.08708 0.4171755 43 -7.39995 0</span> <span class="r-out co"><span class="r-pr">#></span> Correlation: </span> <span class="r-out co"><span class="r-pr">#></span> prnt_0</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_parent_sink 0.027 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k_parent_sink 0.031 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Standardized Within-Group Residuals:</span> -<span class="r-out co"><span class="r-pr">#></span> Min Q1 Med Q3 Max </span> -<span class="r-out co"><span class="r-pr">#></span> -1.9942823 -0.5622565 0.1791579 0.7165038 2.0704781 </span> +<span class="r-out co"><span class="r-pr">#></span> Min Q1 Med Q3 Max </span> +<span class="r-out co"><span class="r-pr">#></span> -2.38427070 -0.52059848 0.03593021 0.39987268 2.73188969 </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Number of Observations: 50</span> +<span class="r-out co"><span class="r-pr">#></span> Number of Observations: 47</span> <span class="r-out co"><span class="r-pr">#></span> Number of Groups: 3 </span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/augPred.html" class="external-link">augPred</a></span><span class="op">(</span><span class="va">m_nlme</span>, level <span class="op">=</span> <span class="fl">0</span><span class="op">:</span><span class="fl">1</span><span class="op">)</span>, layout <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/augPred.html" class="external-link">augPred</a></span><span class="op">(</span><span class="va">m_nlme</span>, level <span class="op">=</span> <span class="fl">0</span><span class="op">:</span><span class="fl">1</span><span class="op">)</span>, layout <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="nlme-1.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="co"># augPred does not work on fits with more than one state</span></span> -<span class="r-in"><span class="co"># variable</span></span> -<span class="r-in"><span class="co">#</span></span> -<span class="r-in"><span class="co"># The procedure is greatly simplified by the nlme.mmkin function</span></span> -<span class="r-in"><span class="va">f_nlme</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlme</span><span class="op">)</span></span> +<span class="r-in"><span><span class="co"># augPred does not work on fits with more than one state</span></span></span> +<span class="r-in"><span><span class="co"># variable</span></span></span> +<span class="r-in"><span><span class="co">#</span></span></span> +<span class="r-in"><span><span class="co"># The procedure is greatly simplified by the nlme.mmkin function</span></span></span> +<span class="r-in"><span><span class="va">f_nlme</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlme</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="nlme-2.png" alt="" width="700" height="433"></span> </code></pre></div> </div> @@ -202,7 +213,7 @@ A <code><a href="https://rdrr.io/pkg/nlme/man/groupedData.html" class="external- </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/nlme.mmkin.html b/docs/reference/nlme.mmkin.html index c8bd28e9..1e294eaf 100644 --- a/docs/reference/nlme.mmkin.html +++ b/docs/reference/nlme.mmkin.html @@ -19,7 +19,7 @@ have been obtained by fitting the same model to a list of datasets."><!-- mathja </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -46,19 +46,25 @@ have been obtained by fitting the same model to a list of datasets."><!-- mathja <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -91,78 +97,115 @@ have been obtained by fitting the same model to a list of datasets.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># S3 method for mmkin</span> -<span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span> - <span class="va">model</span>, - data <span class="op">=</span> <span class="st">"auto"</span>, - fixed <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">as.list</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="fu"><a href="mean_degparms.html">mean_degparms</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>, <span class="kw">function</span><span class="op">(</span><span class="va">el</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/eval.html" class="external-link">eval</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/parse.html" class="external-link">parse</a></span><span class="op">(</span>text <span class="op">=</span> - <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="va">el</span>, <span class="fl">1</span>, sep <span class="op">=</span> <span class="st">"~"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>, - random <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/pdDiag.html" class="external-link">pdDiag</a></span><span class="op">(</span><span class="va">fixed</span><span class="op">)</span>, - <span class="va">groups</span>, - start <span class="op">=</span> <span class="fu"><a href="mean_degparms.html">mean_degparms</a></span><span class="op">(</span><span class="va">model</span>, random <span class="op">=</span> <span class="cn">TRUE</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>, - correlation <span class="op">=</span> <span class="cn">NULL</span>, - weights <span class="op">=</span> <span class="cn">NULL</span>, - <span class="va">subset</span>, - method <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"ML"</span>, <span class="st">"REML"</span><span class="op">)</span>, - na.action <span class="op">=</span> <span class="va">na.fail</span>, - <span class="va">naPattern</span>, - control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>, - verbose <span class="op">=</span> <span class="cn">FALSE</span> -<span class="op">)</span> - -<span class="co"># S3 method for nlme.mmkin</span> -<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span> - -<span class="co"># S3 method for nlme.mmkin</span> -<span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span></span> +<span> <span class="va">model</span>,</span> +<span> data <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> fixed <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">as.list</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="fu"><a href="mean_degparms.html">mean_degparms</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>, <span class="kw">function</span><span class="op">(</span><span class="va">el</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/eval.html" class="external-link">eval</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/parse.html" class="external-link">parse</a></span><span class="op">(</span>text <span class="op">=</span></span> +<span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="va">el</span>, <span class="fl">1</span>, sep <span class="op">=</span> <span class="st">"~"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>,</span> +<span> random <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/pdDiag.html" class="external-link">pdDiag</a></span><span class="op">(</span><span class="va">fixed</span><span class="op">)</span>,</span> +<span> <span class="va">groups</span>,</span> +<span> start <span class="op">=</span> <span class="fu"><a href="mean_degparms.html">mean_degparms</a></span><span class="op">(</span><span class="va">model</span>, random <span class="op">=</span> <span class="cn">TRUE</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span> +<span> correlation <span class="op">=</span> <span class="cn">NULL</span>,</span> +<span> weights <span class="op">=</span> <span class="cn">NULL</span>,</span> +<span> <span class="va">subset</span>,</span> +<span> method <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"ML"</span>, <span class="st">"REML"</span><span class="op">)</span>,</span> +<span> na.action <span class="op">=</span> <span class="va">na.fail</span>,</span> +<span> <span class="va">naPattern</span>,</span> +<span> control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>,</span> +<span> verbose <span class="op">=</span> <span class="cn">FALSE</span></span> +<span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for nlme.mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for nlme.mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>model</dt> <dd><p>An <a href="mmkin.html">mmkin</a> row object.</p></dd> + + <dt>data</dt> <dd><p>Ignored, data are taken from the mmkin model</p></dd> + + <dt>fixed</dt> <dd><p>Ignored, all degradation parameters fitted in the mmkin model are used as fixed parameters</p></dd> + + <dt>random</dt> <dd><p>If not specified, no correlations between random effects are set up for the optimised degradation model parameters. This is achieved by using the <a href="https://rdrr.io/pkg/nlme/man/pdDiag.html" class="external-link">nlme::pdDiag</a> method.</p></dd> + + <dt>groups</dt> <dd><p>See the documentation of nlme</p></dd> + + <dt>start</dt> <dd><p>If not specified, mean values of the fitted degradation parameters taken from the mmkin object are used</p></dd> + + <dt>correlation</dt> <dd><p>See the documentation of nlme</p></dd> + + <dt>weights</dt> <dd><p>passed to nlme</p></dd> + + <dt>subset</dt> <dd><p>passed to nlme</p></dd> + + <dt>method</dt> <dd><p>passed to nlme</p></dd> + + <dt>na.action</dt> <dd><p>passed to nlme</p></dd> + + <dt>naPattern</dt> <dd><p>passed to nlme</p></dd> + + <dt>control</dt> <dd><p>passed to nlme</p></dd> + + <dt>verbose</dt> <dd><p>passed to nlme</p></dd> + + <dt>x</dt> <dd><p>An nlme.mmkin object to print</p></dd> + + <dt>digits</dt> <dd><p>Number of digits to use for printing</p></dd> + + <dt>...</dt> <dd><p>Update specifications passed to update.nlme</p></dd> + + <dt>object</dt> <dd><p>An nlme.mmkin object to update</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>Upon success, a fitted 'nlme.mmkin' object, which is an nlme object + + +<p>Upon success, a fitted 'nlme.mmkin' object, which is an nlme object with additional elements. It also inherits from 'mixed.mmkin'.</p> </div> <div id="details"> @@ -185,19 +228,19 @@ methods that will automatically work on 'nlme.mmkin' objects, such as <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="va">ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span> -<span class="r-in"> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">]</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"parent"</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"> <span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span> -<span class="r-in"> <span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://svn.r-project.org/R-packages/trunk/nlme/" class="external-link">nlme</a></span><span class="op">)</span></span> -<span class="r-in"> <span class="va">f_nlme_sfo</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span></span> -<span class="r-in"> <span class="va">f_nlme_dfop</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_nlme_sfo</span>, <span class="va">f_nlme_dfop</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span></span> +<span class="r-in"><span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">]</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"parent"</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span> <span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://svn.r-project.org/R-packages/trunk/nlme/" class="external-link">nlme</a></span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">f_nlme_sfo</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">f_nlme_dfop</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_nlme_sfo</span>, <span class="va">f_nlme_dfop</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Model df AIC BIC logLik Test L.Ratio p-value</span> <span class="r-out co"><span class="r-pr">#></span> f_nlme_sfo 1 5 625.0539 637.5529 -307.5269 </span> -<span class="r-out co"><span class="r-pr">#></span> f_nlme_dfop 2 9 495.1270 517.6253 -238.5635 1 vs 2 137.9268 <.0001</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_nlme_dfop</span><span class="op">)</span></span> +<span class="r-out co"><span class="r-pr">#></span> f_nlme_dfop 2 9 495.1270 517.6253 -238.5635 1 vs 2 137.9269 <.0001</span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_nlme_dfop</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Kinetic nonlinear mixed-effects model fit by maximum likelihood</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Structural model:</span> @@ -222,50 +265,50 @@ methods that will automatically work on 'nlme.mmkin' objects, such as <span class="r-out co"><span class="r-pr">#></span> parent_0 log_k1 log_k2 g_qlogis Residual</span> <span class="r-out co"><span class="r-pr">#></span> StdDev: 2.488 0.8447 1.33 0.4652 2.321</span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlme_dfop</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlme_dfop</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="nlme.mmkin-1.png" alt="" width="700" height="433"></span> -<span class="r-in"> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">f_nlme_dfop</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">f_nlme_dfop</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> $distimes</span> <span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_k1 DT50_k2</span> -<span class="r-out co"><span class="r-pr">#></span> parent 10.79857 100.7937 30.34192 4.193936 43.85441</span> +<span class="r-out co"><span class="r-pr">#></span> parent 10.79857 100.7937 30.34193 4.193938 43.85443</span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-in"></span> -<span class="r-in"> <span class="va">ds_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span> -<span class="r-in"> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span></span> -<span class="r-in"> <span class="va">m_sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span> -<span class="r-in"> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"min"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"> <span class="va">m_sfo_sfo_ff</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span> -<span class="r-in"> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"> <span class="va">m_dfop_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span> -<span class="r-in"> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"> <span class="va">f_2</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"SFO-SFO"</span> <span class="op">=</span> <span class="va">m_sfo_sfo</span>,</span> -<span class="r-in"> <span class="st">"SFO-SFO-ff"</span> <span class="op">=</span> <span class="va">m_sfo_sfo_ff</span>,</span> -<span class="r-in"> <span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">m_dfop_sfo</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="va">ds_2</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"> <span class="va">f_nlme_sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_2</span><span class="op">[</span><span class="st">"SFO-SFO"</span>, <span class="op">]</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlme_sfo_sfo</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="va">ds_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span></span> +<span class="r-in"><span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">m_sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"min"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">m_sfo_sfo_ff</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">m_dfop_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="va">f_2</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"SFO-SFO"</span> <span class="op">=</span> <span class="va">m_sfo_sfo</span>,</span></span> +<span class="r-in"><span> <span class="st">"SFO-SFO-ff"</span> <span class="op">=</span> <span class="va">m_sfo_sfo_ff</span>,</span></span> +<span class="r-in"><span> <span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">m_dfop_sfo</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="va">ds_2</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="va">f_nlme_sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_2</span><span class="op">[</span><span class="st">"SFO-SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlme_sfo_sfo</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="nlme.mmkin-2.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> -<span class="r-in"> <span class="co"># With formation fractions this does not coverge with defaults</span></span> -<span class="r-in"> <span class="co"># f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ])</span></span> -<span class="r-in"> <span class="co">#plot(f_nlme_sfo_sfo_ff)</span></span> -<span class="r-in"></span> -<span class="r-in"> <span class="co"># For the following, we need to increase pnlsMaxIter and the tolerance</span></span> -<span class="r-in"> <span class="co"># to get convergence</span></span> -<span class="r-in"> <span class="va">f_nlme_dfop_sfo</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_2</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>,</span> -<span class="r-in"> control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>pnlsMaxIter <span class="op">=</span> <span class="fl">120</span>, tolerance <span class="op">=</span> <span class="fl">5e-4</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlme_dfop_sfo</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># With formation fractions this does not coverge with defaults</span></span></span> +<span class="r-in"><span> <span class="co"># f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ])</span></span></span> +<span class="r-in"><span> <span class="co">#plot(f_nlme_sfo_sfo_ff)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># For the following, we need to increase pnlsMaxIter and the tolerance</span></span></span> +<span class="r-in"><span> <span class="co"># to get convergence</span></span></span> +<span class="r-in"><span> <span class="va">f_nlme_dfop_sfo</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_2</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>,</span></span> +<span class="r-in"><span> control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>pnlsMaxIter <span class="op">=</span> <span class="fl">120</span>, tolerance <span class="op">=</span> <span class="fl">5e-4</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlme_dfop_sfo</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="nlme.mmkin-3.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_nlme_dfop_sfo</span>, <span class="va">f_nlme_sfo_sfo</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_nlme_dfop_sfo</span>, <span class="va">f_nlme_sfo_sfo</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Model df AIC BIC logLik Test L.Ratio p-value</span> <span class="r-out co"><span class="r-pr">#></span> f_nlme_dfop_sfo 1 13 843.8547 884.6201 -408.9274 </span> <span class="r-out co"><span class="r-pr">#></span> f_nlme_sfo_sfo 2 9 1085.1821 1113.4043 -533.5910 1 vs 2 249.3274 <.0001</span> -<span class="r-in"></span> -<span class="r-in"> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">f_nlme_sfo_sfo</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">f_nlme_sfo_sfo</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> $ff</span> <span class="r-out co"><span class="r-pr">#></span> parent_sink parent_A1 A1_sink </span> <span class="r-out co"><span class="r-pr">#></span> 0.5912432 0.4087568 1.0000000 </span> @@ -275,27 +318,27 @@ methods that will automatically work on 'nlme.mmkin' objects, such as <span class="r-out co"><span class="r-pr">#></span> parent 19.13518 63.5657</span> <span class="r-out co"><span class="r-pr">#></span> A1 66.02155 219.3189</span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-in"> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">f_nlme_dfop_sfo</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">f_nlme_dfop_sfo</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> $ff</span> <span class="r-out co"><span class="r-pr">#></span> parent_A1 parent_sink </span> -<span class="r-out co"><span class="r-pr">#></span> 0.2768575 0.7231425 </span> +<span class="r-out co"><span class="r-pr">#></span> 0.2768574 0.7231426 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> $distimes</span> <span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_k1 DT50_k2</span> <span class="r-out co"><span class="r-pr">#></span> parent 11.07091 104.6320 31.49737 4.462383 46.20825</span> -<span class="r-out co"><span class="r-pr">#></span> A1 162.30524 539.1663 NA NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> A1 162.30519 539.1662 NA NA NA</span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-in"></span> -<span class="r-in"> <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="fu">findFunction</span><span class="op">(</span><span class="st">"varConstProp"</span><span class="op">)</span><span class="op">)</span> <span class="op">></span> <span class="fl">0</span><span class="op">)</span> <span class="op">{</span> <span class="co"># tc error model for nlme available</span></span> -<span class="r-in"> <span class="co"># Attempts to fit metabolite kinetics with the tc error model are possible,</span></span> -<span class="r-in"> <span class="co"># but need tweeking of control values and sometimes do not converge</span></span> -<span class="r-in"></span> -<span class="r-in"> <span class="va">f_tc</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span> -<span class="r-in"> <span class="va">f_nlme_sfo_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_tc</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span></span> -<span class="r-in"> <span class="va">f_nlme_dfop_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_nlme_sfo</span>, <span class="va">f_nlme_sfo_tc</span>, <span class="va">f_nlme_dfop</span>, <span class="va">f_nlme_dfop_tc</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_nlme_dfop_tc</span><span class="op">)</span></span> -<span class="r-in"> <span class="op">}</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="fu">findFunction</span><span class="op">(</span><span class="st">"varConstProp"</span><span class="op">)</span><span class="op">)</span> <span class="op">></span> <span class="fl">0</span><span class="op">)</span> <span class="op">{</span> <span class="co"># tc error model for nlme available</span></span></span> +<span class="r-in"><span> <span class="co"># Attempts to fit metabolite kinetics with the tc error model are possible,</span></span></span> +<span class="r-in"><span> <span class="co"># but need tweeking of control values and sometimes do not converge</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="va">f_tc</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">f_nlme_sfo_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_tc</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">f_nlme_dfop_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_nlme_sfo</span>, <span class="va">f_nlme_sfo_tc</span>, <span class="va">f_nlme_dfop</span>, <span class="va">f_nlme_dfop_tc</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_nlme_dfop_tc</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="op">}</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Kinetic nonlinear mixed-effects model fit by maximum likelihood</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Structural model:</span> @@ -311,7 +354,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as <span class="r-out co"><span class="r-pr">#></span> Fixed effects:</span> <span class="r-out co"><span class="r-pr">#></span> list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) </span> <span class="r-out co"><span class="r-pr">#></span> parent_0 log_k1 log_k2 g_qlogis </span> -<span class="r-out co"><span class="r-pr">#></span> 94.04775 -1.82340 -4.16715 0.05685 </span> +<span class="r-out co"><span class="r-pr">#></span> 94.04774 -1.82340 -4.16716 0.05686 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Random effects:</span> <span class="r-out co"><span class="r-pr">#></span> Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)</span> @@ -325,11 +368,11 @@ methods that will automatically work on 'nlme.mmkin' objects, such as <span class="r-out co"><span class="r-pr">#></span> Formula: ~fitted(.) </span> <span class="r-out co"><span class="r-pr">#></span> Parameter estimates:</span> <span class="r-out co"><span class="r-pr">#></span> const prop </span> -<span class="r-out co"><span class="r-pr">#></span> 2.23223513 0.01262371 </span> -<span class="r-in"></span> -<span class="r-in"> <span class="va">f_2_obs</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_2</span>, error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span></span> -<span class="r-in"> <span class="va">f_nlme_sfo_sfo_obs</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_2_obs</span><span class="op">[</span><span class="st">"SFO-SFO"</span>, <span class="op">]</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_nlme_sfo_sfo_obs</span><span class="op">)</span></span> +<span class="r-out co"><span class="r-pr">#></span> 2.23223147 0.01262395 </span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="va">f_2_obs</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_2</span>, error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">f_nlme_sfo_sfo_obs</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_2_obs</span><span class="op">[</span><span class="st">"SFO-SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_nlme_sfo_sfo_obs</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Kinetic nonlinear mixed-effects model fit by maximum likelihood</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Structural model:</span> @@ -358,24 +401,24 @@ methods that will automatically work on 'nlme.mmkin' objects, such as <span class="r-out co"><span class="r-pr">#></span> Formula: ~1 | name </span> <span class="r-out co"><span class="r-pr">#></span> Parameter estimates:</span> <span class="r-out co"><span class="r-pr">#></span> parent A1 </span> -<span class="r-out co"><span class="r-pr">#></span> 1.0000000 0.2049985 </span> -<span class="r-in"> <span class="va">f_nlme_dfop_sfo_obs</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_2_obs</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>,</span> -<span class="r-in"> control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>pnlsMaxIter <span class="op">=</span> <span class="fl">120</span>, tolerance <span class="op">=</span> <span class="fl">5e-4</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"> <span class="va">f_2_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_2</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span> -<span class="r-in"> <span class="co"># f_nlme_sfo_sfo_tc <- nlme(f_2_tc["SFO-SFO", ]) # No convergence with 50 iterations</span></span> -<span class="r-in"> <span class="co"># f_nlme_dfop_sfo_tc <- nlme(f_2_tc["DFOP-SFO", ],</span></span> -<span class="r-in"> <span class="co"># control = list(pnlsMaxIter = 120, tolerance = 5e-4)) # Error in X[, fmap[[nm]]] <- gradnm</span></span> -<span class="r-in"></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_nlme_dfop_sfo</span>, <span class="va">f_nlme_dfop_sfo_obs</span><span class="op">)</span></span> +<span class="r-out co"><span class="r-pr">#></span> 1.0000000 0.2049995 </span> +<span class="r-in"><span> <span class="va">f_nlme_dfop_sfo_obs</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_2_obs</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>,</span></span> +<span class="r-in"><span> control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>pnlsMaxIter <span class="op">=</span> <span class="fl">120</span>, tolerance <span class="op">=</span> <span class="fl">5e-4</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="va">f_2_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_2</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="co"># f_nlme_sfo_sfo_tc <- nlme(f_2_tc["SFO-SFO", ]) # No convergence with 50 iterations</span></span></span> +<span class="r-in"><span> <span class="co"># f_nlme_dfop_sfo_tc <- nlme(f_2_tc["DFOP-SFO", ],</span></span></span> +<span class="r-in"><span> <span class="co"># control = list(pnlsMaxIter = 120, tolerance = 5e-4)) # Error in X[, fmap[[nm]]] <- gradnm</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_nlme_dfop_sfo</span>, <span class="va">f_nlme_dfop_sfo_obs</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Model df AIC BIC logLik Test L.Ratio</span> <span class="r-out co"><span class="r-pr">#></span> f_nlme_dfop_sfo 1 13 843.8547 884.6201 -408.9274 </span> -<span class="r-out co"><span class="r-pr">#></span> f_nlme_dfop_sfo_obs 2 14 817.5338 861.4350 -394.7669 1 vs 2 28.32092</span> +<span class="r-out co"><span class="r-pr">#></span> f_nlme_dfop_sfo_obs 2 14 817.5338 861.4350 -394.7669 1 vs 2 28.32091</span> <span class="r-out co"><span class="r-pr">#></span> p-value</span> <span class="r-out co"><span class="r-pr">#></span> f_nlme_dfop_sfo </span> <span class="r-out co"><span class="r-pr">#></span> f_nlme_dfop_sfo_obs <.0001</span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># }</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> </code></pre></div> </div> </div> @@ -390,7 +433,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/nobs.mkinfit.html b/docs/reference/nobs.mkinfit.html index e37fff64..edf97142 100644 --- a/docs/reference/nobs.mkinfit.html +++ b/docs/reference/nobs.mkinfit.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -44,19 +44,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -87,20 +93,25 @@ </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># S3 method for mkinfit</span> -<span class="fu"><a href="https://rdrr.io/r/stats/nobs.html" class="external-link">nobs</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/stats/nobs.html" class="external-link">nobs</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>object</dt> <dd><p>An mkinfit object</p></dd> + + <dt>...</dt> <dd><p>For compatibility with the generic method</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>The number of rows in the data included in the mkinfit object</p> + + +<p>The number of rows in the data included in the mkinfit object</p> </div> </div> @@ -115,7 +126,7 @@ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/parms.html b/docs/reference/parms.html index 18f3566e..a3d62d1e 100644 --- a/docs/reference/parms.html +++ b/docs/reference/parms.html @@ -1,7 +1,7 @@ <!DOCTYPE html> -<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Extract model parameters from mkinfit models — parms • mkin</title><!-- jquery --><script 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href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Extract model parameters — parms"><meta property="og:description" content="This function returns degradation model parameters as well as error +model parameters per default, in order to avoid working with a fitted model +without considering the error structure that was assumed for the fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> <![endif]--></head><body data-spy="scroll" data-target="#toc"> @@ -19,7 +19,7 @@ considering the error structure that was assumed for the fit."><!-- mathjax -->< </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -46,19 +46,25 @@ considering the error structure that was assumed for the fit."><!-- mathjax -->< <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -79,69 +85,91 @@ considering the error structure that was assumed for the fit."><!-- mathjax -->< </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> - <h1>Extract model parameters from mkinfit models</h1> - <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parms.mkinfit.R" class="external-link"><code>R/parms.mkinfit.R</code></a></small> + <h1>Extract model parameters</h1> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parms.R" class="external-link"><code>R/parms.R</code></a></small> <div class="hidden name"><code>parms.Rd</code></div> </div> <div class="ref-description"> - <p>This function always returns degradation model parameters as well as error -model parameters, in order to avoid working with a fitted model without -considering the error structure that was assumed for the fit.</p> + <p>This function returns degradation model parameters as well as error +model parameters per default, in order to avoid working with a fitted model +without considering the error structure that was assumed for the fit.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">parms</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span> - -<span class="co"># S3 method for mkinfit</span> -<span class="fu">parms</span><span class="op">(</span><span class="va">object</span>, transformed <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span> - -<span class="co"># S3 method for mmkin</span> -<span class="fu">parms</span><span class="op">(</span><span class="va">object</span>, transformed <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">parms</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mkinfit</span></span> +<span><span class="fu">parms</span><span class="op">(</span><span class="va">object</span>, transformed <span class="op">=</span> <span class="cn">FALSE</span>, errparms <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mmkin</span></span> +<span><span class="fu">parms</span><span class="op">(</span><span class="va">object</span>, transformed <span class="op">=</span> <span class="cn">FALSE</span>, errparms <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for multistart</span></span> +<span><span class="fu">parms</span><span class="op">(</span><span class="va">object</span>, exclude_failed <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>object</dt> -<dd><p>A fitted model object. Methods are implemented for -<code><a href="mkinfit.html">mkinfit()</a></code> objects and for <code><a href="mmkin.html">mmkin()</a></code> objects.</p></dd> +<dd><p>A fitted model object.</p></dd> + + <dt>...</dt> <dd><p>Not used</p></dd> + + <dt>transformed</dt> -<dd><p>Should the parameters be returned -as used internally during the optimisation?</p></dd> +<dd><p>Should the parameters be returned as used internally +during the optimisation?</p></dd> + + +<dt>errparms</dt> +<dd><p>Should the error model parameters be returned +in addition to the degradation parameters?</p></dd> + + +<dt>exclude_failed</dt> +<dd><p>For <a href="multistart.html">multistart</a> objects, should rows for failed fits +be removed from the returned parameter matrix?</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>For mkinfit objects, a numeric vector of fitted model parameters. -For mmkin row objects, a matrix with the parameters with a -row for each dataset. If the mmkin object has more than one row, a list of -such matrices is returned.</p> + + +<p>Depending on the object, a numeric vector of fitted model parameters, +a matrix (e.g. for mmkin row objects), or a list of matrices (e.g. for +mmkin objects with more than one row).</p> + </div> + <div id="see-also"> + <h2>See also</h2> + <div class="dont-index"><p><a href="saem.html">saem</a>, <a href="multistart.html">multistart</a></p></div> </div> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="co"># mkinfit objects</span></span> -<span class="r-in"><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="fu">parms</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># mkinfit objects</span></span></span> +<span class="r-in"><span><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu">parms</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> parent_0 k_parent sigma </span> <span class="r-out co"><span class="r-pr">#></span> 82.4921598 0.3060633 4.6730124 </span> -<span class="r-in"><span class="fu">parms</span><span class="op">(</span><span class="va">fit</span>, transformed <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu">parms</span><span class="op">(</span><span class="va">fit</span>, transformed <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_parent sigma </span> <span class="r-out co"><span class="r-pr">#></span> 82.492160 -1.183963 4.673012 </span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># mmkin objects</span></span> -<span class="r-in"><span class="va">ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span> -<span class="r-in"> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">)</span></span> -<span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"><span class="va">fits</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span> -<span class="r-in"><span class="fu">parms</span><span class="op">(</span><span class="va">fits</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># mmkin objects</span></span></span> +<span class="r-in"><span><span class="va">ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span></span> +<span class="r-in"><span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="va">fits</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu">parms</span><span class="op">(</span><span class="va">fits</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 88.52275400 82.666781678 86.8547308 91.7779306 82.14809450</span> <span class="r-out co"><span class="r-pr">#></span> k_parent 0.05794659 0.009647805 0.2102974 0.1232258 0.00720421</span> <span class="r-out co"><span class="r-pr">#></span> sigma 5.15274487 7.040168584 3.6769645 6.4669234 6.50457673</span> -<span class="r-in"><span class="fu">parms</span><span class="op">(</span><span class="va">fits</span><span class="op">[</span>, <span class="fl">2</span><span class="op">]</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu">parms</span><span class="op">(</span><span class="va">fits</span><span class="op">[</span>, <span class="fl">2</span><span class="op">]</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> $SFO</span> <span class="r-out co"><span class="r-pr">#></span> Dataset 7</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 82.666781678</span> @@ -157,13 +185,13 @@ such matrices is returned.</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> $DFOP</span> <span class="r-out co"><span class="r-pr">#></span> Dataset 7</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 91.058971599</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 91.058971589</span> <span class="r-out co"><span class="r-pr">#></span> k1 0.044946770</span> <span class="r-out co"><span class="r-pr">#></span> k2 0.002868336</span> -<span class="r-out co"><span class="r-pr">#></span> g 0.526942414</span> +<span class="r-out co"><span class="r-pr">#></span> g 0.526942415</span> <span class="r-out co"><span class="r-pr">#></span> sigma 2.221302196</span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-in"><span class="fu">parms</span><span class="op">(</span><span class="va">fits</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu">parms</span><span class="op">(</span><span class="va">fits</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> $SFO</span> <span class="r-out co"><span class="r-pr">#></span> Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 88.52275400 82.666781678 86.8547308 91.7779306 82.14809450</span> @@ -179,13 +207,13 @@ such matrices is returned.</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> $DFOP</span> <span class="r-out co"><span class="r-pr">#></span> Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 96.55213663 91.058971599 90.34509493 98.14858821 94.311323732</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 96.55213663 91.058971589 90.34509493 98.14858820 94.311323734</span> <span class="r-out co"><span class="r-pr">#></span> k1 0.21954588 0.044946770 0.41232288 0.31697588 0.080663857</span> <span class="r-out co"><span class="r-pr">#></span> k2 0.02957934 0.002868336 0.07581766 0.03260384 0.003425417</span> -<span class="r-out co"><span class="r-pr">#></span> g 0.44845068 0.526942414 0.66091967 0.65322767 0.342652880</span> +<span class="r-out co"><span class="r-pr">#></span> g 0.44845068 0.526942415 0.66091967 0.65322767 0.342652880</span> <span class="r-out co"><span class="r-pr">#></span> sigma 1.35690468 2.221302196 1.34169076 2.87159846 1.942067831</span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-in"><span class="fu">parms</span><span class="op">(</span><span class="va">fits</span>, transformed <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu">parms</span><span class="op">(</span><span class="va">fits</span>, transformed <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> $SFO</span> <span class="r-out co"><span class="r-pr">#></span> Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 88.522754 82.666782 86.854731 91.777931 82.148094</span> @@ -207,7 +235,7 @@ such matrices is returned.</p> <span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.2069326 0.1078741 0.6673953 0.6332573 -0.6514943</span> <span class="r-out co"><span class="r-pr">#></span> sigma 1.3569047 2.2213022 1.3416908 2.8715985 1.9420678</span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-in"><span class="co"># }</span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> </code></pre></div> </div> </div> @@ -222,7 +250,7 @@ such matrices is returned.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> 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(2021)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> +<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> +<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + + + <div class="container template-reference-topic"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <div class="container"> + <div class="navbar-header"> + <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> + <span class="sr-only">Toggle navigation</span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + </button> + <span class="navbar-brand"> + <a class="navbar-link" href="../index.html">mkin</a> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.1</span> + </span> + </div> + + <div id="navbar" class="navbar-collapse collapse"> + <ul class="nav navbar-nav"><li> + <a href="../reference/index.html">Functions and data</a> +</li> +<li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> + Articles + + <span class="caret"></span> + </a> + <ul class="dropdown-menu" role="menu"><li> + <a href="../articles/mkin.html">Introduction to mkin</a> + </li> + <li> + <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> + </li> + <li> + <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> + </li> + <li> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + </li> + <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> + <li> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> +<li> + <a href="../news/index.html">News</a> +</li> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> + <span class="fab fa-github fa-lg"></span> + + </a> +</li> + </ul></div><!--/.nav-collapse --> + </div><!--/.container --> +</div><!--/.navbar --> + + + + </header><div class="row"> + <div class="col-md-9 contents"> + <div class="page-header"> + <h1>Plot parameter variability of multistart objects</h1> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parplot.R" class="external-link"><code>R/parplot.R</code></a></small> + <div class="hidden name"><code>parplot.Rd</code></div> + </div> + + <div class="ref-description"> + <p>Produces a boxplot with all parameters from the multiple runs, scaled +either by the parameters of the run with the highest likelihood, +or by their medians as proposed in the paper by Duchesne et al. (2021).</p> + </div> + + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">parplot</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for multistart.saem.mmkin</span></span> +<span><span class="fu">parplot</span><span class="op">(</span></span> +<span> <span class="va">object</span>,</span> +<span> llmin <span class="op">=</span> <span class="op">-</span><span class="cn">Inf</span>,</span> +<span> scale <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"best"</span>, <span class="st">"median"</span><span class="op">)</span>,</span> +<span> lpos <span class="op">=</span> <span class="st">"bottomleft"</span>,</span> +<span> main <span class="op">=</span> <span class="st">""</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>object</dt> +<dd><p>The <a href="multistart.html">multistart</a> object</p></dd> + + +<dt>...</dt> +<dd><p>Passed to <a href="https://rdrr.io/r/graphics/boxplot.html" class="external-link">boxplot</a></p></dd> + + +<dt>llmin</dt> +<dd><p>The minimum likelihood of objects to be shown</p></dd> + + +<dt>scale</dt> +<dd><p>By default, scale parameters using the best available fit. +If 'median', parameters are scaled using the median parameters from all fits.</p></dd> + + +<dt>lpos</dt> +<dd><p>Positioning of the legend.</p></dd> + + +<dt>main</dt> +<dd><p>Title of the plot</p></dd> + +</dl></div> + <div id="details"> + <h2>Details</h2> + <p>Starting values of degradation model parameters and error model parameters +are shown as green circles. The results obtained in the original run +are shown as red circles.</p> + </div> + <div id="references"> + <h2>References</h2> + <p>Duchesne R, Guillemin A, Gandrillon O, Crauste F. Practical +identifiability in the frame of nonlinear mixed effects models: the example +of the in vitro erythropoiesis. BMC Bioinformatics. 2021 Oct 4;22(1):478. +doi: 10.1186/s12859-021-04373-4.</p> + </div> + <div id="see-also"> + <h2>See also</h2> + <div class="dont-index"><p><a href="multistart.html">multistart</a></p></div> + </div> + + </div> + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> +</div> + + + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +</div> + + </footer></div> + + + + + + + </body></html> + diff --git a/docs/reference/plot.mixed.mmkin-2.png b/docs/reference/plot.mixed.mmkin-2.png Binary files differindex b35f28d6..8678c166 100644 --- a/docs/reference/plot.mixed.mmkin-2.png +++ b/docs/reference/plot.mixed.mmkin-2.png diff --git a/docs/reference/plot.mixed.mmkin-3.png b/docs/reference/plot.mixed.mmkin-3.png Binary files differindex c981538f..9bd01852 100644 --- a/docs/reference/plot.mixed.mmkin-3.png +++ b/docs/reference/plot.mixed.mmkin-3.png diff --git a/docs/reference/plot.mixed.mmkin-4.png b/docs/reference/plot.mixed.mmkin-4.png Binary files differindex ccbe5861..a849aaee 100644 --- a/docs/reference/plot.mixed.mmkin-4.png +++ b/docs/reference/plot.mixed.mmkin-4.png diff --git a/docs/reference/plot.mixed.mmkin.html b/docs/reference/plot.mixed.mmkin.html index 2af2328d..b1083204 100644 --- a/docs/reference/plot.mixed.mmkin.html +++ b/docs/reference/plot.mixed.mmkin.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -44,19 +44,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -87,94 +93,146 @@ </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># S3 method for mixed.mmkin</span> -<span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span> - <span class="va">x</span>, - i <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">ncol</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">mmkin</span><span class="op">)</span>, - obs_vars <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">mkinmod</span><span class="op">$</span><span class="va">map</span><span class="op">)</span>, - standardized <span class="op">=</span> <span class="cn">TRUE</span>, - xlab <span class="op">=</span> <span class="st">"Time"</span>, - xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/range.html" class="external-link">range</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">$</span><span class="va">time</span><span class="op">)</span>, - resplot <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"predicted"</span>, <span class="st">"time"</span><span class="op">)</span>, - pred_over <span class="op">=</span> <span class="cn">NULL</span>, - test_log_parms <span class="op">=</span> <span class="cn">FALSE</span>, - conf.level <span class="op">=</span> <span class="fl">0.6</span>, - default_log_parms <span class="op">=</span> <span class="cn">NA</span>, - ymax <span class="op">=</span> <span class="st">"auto"</span>, - maxabs <span class="op">=</span> <span class="st">"auto"</span>, - ncol.legend <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op"><=</span> <span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">+</span> <span class="fl">1</span>, <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op"><=</span> <span class="fl">8</span>, <span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span>, - nrow.legend <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">ceiling</a></span><span class="op">(</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">+</span> <span class="fl">1</span><span class="op">)</span><span class="op">/</span><span class="va">ncol.legend</span><span class="op">)</span>, - rel.height.legend <span class="op">=</span> <span class="fl">0.02</span> <span class="op">+</span> <span class="fl">0.07</span> <span class="op">*</span> <span class="va">nrow.legend</span>, - rel.height.bottom <span class="op">=</span> <span class="fl">1.1</span>, - pch_ds <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">i</span><span class="op">)</span>, - col_ds <span class="op">=</span> <span class="va">pch_ds</span> <span class="op">+</span> <span class="fl">1</span>, - lty_ds <span class="op">=</span> <span class="va">col_ds</span>, - frame <span class="op">=</span> <span class="cn">TRUE</span>, - <span class="va">...</span> -<span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mixed.mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span> +<span> <span class="va">x</span>,</span> +<span> i <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">ncol</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">mmkin</span><span class="op">)</span>,</span> +<span> obs_vars <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">mkinmod</span><span class="op">$</span><span class="va">map</span><span class="op">)</span>,</span> +<span> standardized <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> xlab <span class="op">=</span> <span class="st">"Time"</span>,</span> +<span> xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/range.html" class="external-link">range</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">$</span><span class="va">time</span><span class="op">)</span>,</span> +<span> resplot <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"predicted"</span>, <span class="st">"time"</span><span class="op">)</span>,</span> +<span> pop_curve <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> pred_over <span class="op">=</span> <span class="cn">NULL</span>,</span> +<span> test_log_parms <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> conf.level <span class="op">=</span> <span class="fl">0.6</span>,</span> +<span> default_log_parms <span class="op">=</span> <span class="cn">NA</span>,</span> +<span> ymax <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> maxabs <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> ncol.legend <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op"><=</span> <span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">+</span> <span class="fl">1</span>, <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op"><=</span> <span class="fl">8</span>, <span class="fl">3</span>, <span class="fl">4</span><span class="op">)</span><span class="op">)</span>,</span> +<span> nrow.legend <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Round.html" class="external-link">ceiling</a></span><span class="op">(</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">+</span> <span class="fl">1</span><span class="op">)</span><span class="op">/</span><span class="va">ncol.legend</span><span class="op">)</span>,</span> +<span> rel.height.legend <span class="op">=</span> <span class="fl">0.02</span> <span class="op">+</span> <span class="fl">0.07</span> <span class="op">*</span> <span class="va">nrow.legend</span>,</span> +<span> rel.height.bottom <span class="op">=</span> <span class="fl">1.1</span>,</span> +<span> pch_ds <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">i</span><span class="op">)</span>,</span> +<span> col_ds <span class="op">=</span> <span class="va">pch_ds</span> <span class="op">+</span> <span class="fl">1</span>,</span> +<span> lty_ds <span class="op">=</span> <span class="va">col_ds</span>,</span> +<span> frame <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>x</dt> <dd><p>An object of class <a href="mixed.html">mixed.mmkin</a>, <a href="saem.html">saem.mmkin</a> or <a href="nlme.mmkin.html">nlme.mmkin</a></p></dd> + + <dt>i</dt> <dd><p>A numeric index to select datasets for which to plot the individual predictions, in case plots get too large</p></dd> + + <dt>obs_vars</dt> <dd><p>A character vector of names of the observed variables for which the data and the model should be plotted. Defauls to all observed variables in the model.</p></dd> + + <dt>standardized</dt> <dd><p>Should the residuals be standardized? Only takes effect if <code>resplot = "time"</code>.</p></dd> + + <dt>xlab</dt> <dd><p>Label for the x axis.</p></dd> + + <dt>xlim</dt> <dd><p>Plot range in x direction.</p></dd> + + <dt>resplot</dt> <dd><p>Should the residuals plotted against time or against predicted values?</p></dd> + + +<dt>pop_curve</dt> +<dd><p>Per default, a population curve is drawn in case +population parameters are fitted by the model, e.g. for saem objects. +In case there is a covariate model, no population curve is currently shown.</p></dd> + + <dt>pred_over</dt> <dd><p>Named list of alternative predictions as obtained from <a href="mkinpredict.html">mkinpredict</a> with a compatible <a href="mkinmod.html">mkinmod</a>.</p></dd> + + <dt>test_log_parms</dt> <dd><p>Passed to <a href="mean_degparms.html">mean_degparms</a> in the case of an <a href="mixed.html">mixed.mmkin</a> object</p></dd> + + <dt>conf.level</dt> <dd><p>Passed to <a href="mean_degparms.html">mean_degparms</a> in the case of an <a href="mixed.html">mixed.mmkin</a> object</p></dd> + + <dt>default_log_parms</dt> <dd><p>Passed to <a href="mean_degparms.html">mean_degparms</a> in the case of an <a href="mixed.html">mixed.mmkin</a> object</p></dd> + + <dt>ymax</dt> <dd><p>Vector of maximum y axis values</p></dd> + + <dt>maxabs</dt> <dd><p>Maximum absolute value of the residuals. This is used for the scaling of the y axis and defaults to "auto".</p></dd> + + <dt>ncol.legend</dt> <dd><p>Number of columns to use in the legend</p></dd> + + <dt>nrow.legend</dt> <dd><p>Number of rows to use in the legend</p></dd> + + <dt>rel.height.legend</dt> <dd><p>The relative height of the legend shown on top</p></dd> + + <dt>rel.height.bottom</dt> <dd><p>The relative height of the bottom plot row</p></dd> + + <dt>pch_ds</dt> <dd><p>Symbols to be used for plotting the data.</p></dd> + + <dt>col_ds</dt> <dd><p>Colors used for plotting the observed data and the corresponding model prediction lines for the different datasets.</p></dd> + + <dt>lty_ds</dt> <dd><p>Line types to be used for the model predictions.</p></dd> + + <dt>frame</dt> <dd><p>Should a frame be drawn around the plots?</p></dd> + + <dt>...</dt> <dd><p>Further arguments passed to <code><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></code>.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>The function is called for its side effect.</p> + + +<p>The function is called for its side effect.</p> </div> <div id="author"> <h2>Author</h2> @@ -183,41 +241,41 @@ corresponding model prediction lines for the different datasets.</p></dd> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="va">ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span> -<span class="r-in"> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="st">"ds "</span>, <span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">)</span></span> -<span class="r-in"><span class="va">dfop_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span> -<span class="r-in"> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"><span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">dfop_sfo</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="fl">3</span><span class="op">:</span><span class="fl">4</span><span class="op">]</span>, standardized <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span></span> +<span class="r-in"><span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="st">"ds "</span>, <span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">dfop_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">dfop_sfo</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span>, <span class="fl">3</span><span class="op">:</span><span class="fl">4</span><span class="op">]</span>, standardized <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="plot.mixed.mmkin-1.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># For this fit we need to increase pnlsMaxiter, and we increase the</span></span> -<span class="r-in"><span class="co"># tolerance in order to speed up the fit for this example evaluation</span></span> -<span class="r-in"><span class="co"># It still takes 20 seconds to run</span></span> -<span class="r-in"><span class="va">f_nlme</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f</span>, control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>pnlsMaxIter <span class="op">=</span> <span class="fl">120</span>, tolerance <span class="op">=</span> <span class="fl">1e-3</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlme</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># For this fit we need to increase pnlsMaxiter, and we increase the</span></span></span> +<span class="r-in"><span><span class="co"># tolerance in order to speed up the fit for this example evaluation</span></span></span> +<span class="r-in"><span><span class="co"># It still takes 20 seconds to run</span></span></span> +<span class="r-in"><span><span class="va">f_nlme</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f</span>, control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>pnlsMaxIter <span class="op">=</span> <span class="fl">120</span>, tolerance <span class="op">=</span> <span class="fl">1e-3</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlme</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="plot.mixed.mmkin-2.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">f_saem</span> <span class="op"><-</span> <span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">f</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">f_saem</span> <span class="op"><-</span> <span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">f</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="plot.mixed.mmkin-3.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">f_obs</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">dfop_sfo</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span></span> -<span class="r-in"><span class="va">f_nlmix</span> <span class="op"><-</span> <span class="fu">nlmix</span><span class="op">(</span><span class="va">f_obs</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">f_obs</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">dfop_sfo</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">f_nlmix</span> <span class="op"><-</span> <span class="fu">nlmix</span><span class="op">(</span><span class="va">f_obs</span><span class="op">)</span></span></span> <span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in nlmix(f_obs):</span> could not find function "nlmix"</span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlmix</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlmix</span><span class="op">)</span></span></span> <span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in plot(f_nlmix):</span> object 'f_nlmix' not found</span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># We can overlay the two variants if we generate predictions</span></span> -<span class="r-in"><span class="va">pred_nlme</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">dfop_sfo</span>,</span> -<span class="r-in"> <span class="va">f_nlme</span><span class="op">$</span><span class="va">bparms.optim</span><span class="op">[</span><span class="op">-</span><span class="fl">1</span><span class="op">]</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="va">f_nlme</span><span class="op">$</span><span class="va">bparms.optim</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, A1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">180</span>, by <span class="op">=</span> <span class="fl">0.2</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem</span>, pred_over <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>nlme <span class="op">=</span> <span class="va">pred_nlme</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># We can overlay the two variants if we generate predictions</span></span></span> +<span class="r-in"><span><span class="va">pred_nlme</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">dfop_sfo</span>,</span></span> +<span class="r-in"><span> <span class="va">f_nlme</span><span class="op">$</span><span class="va">bparms.optim</span><span class="op">[</span><span class="op">-</span><span class="fl">1</span><span class="op">]</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="va">f_nlme</span><span class="op">$</span><span class="va">bparms.optim</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, A1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">180</span>, by <span class="op">=</span> <span class="fl">0.2</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem</span>, pred_over <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>nlme <span class="op">=</span> <span class="va">pred_nlme</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="plot.mixed.mmkin-4.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="co"># }</span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> </code></pre></div> </div> </div> @@ -232,7 +290,7 @@ corresponding model prediction lines for the different datasets.</p></dd> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/plot.mkinfit-2.png b/docs/reference/plot.mkinfit-2.png Binary files differindex 39098648..cef94cb8 100644 --- a/docs/reference/plot.mkinfit-2.png +++ b/docs/reference/plot.mkinfit-2.png diff --git a/docs/reference/plot.mkinfit-5.png b/docs/reference/plot.mkinfit-5.png Binary files differindex 3545b8d8..f90b3f54 100644 --- a/docs/reference/plot.mkinfit-5.png +++ b/docs/reference/plot.mkinfit-5.png diff --git a/docs/reference/plot.mkinfit-7.png b/docs/reference/plot.mkinfit-7.png Binary files differindex daf43033..3e5d828e 100644 --- a/docs/reference/plot.mkinfit-7.png +++ b/docs/reference/plot.mkinfit-7.png diff --git a/docs/reference/plot.mkinfit.html b/docs/reference/plot.mkinfit.html index 23cf27b5..cf96990e 100644 --- a/docs/reference/plot.mkinfit.html +++ b/docs/reference/plot.mkinfit.html @@ -19,7 +19,7 @@ observed data together with the solution of the fitted model."><!-- mathjax -->< </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -46,19 +46,25 @@ observed data together with the solution of the fitted model."><!-- mathjax -->< <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -91,50 +97,50 @@ observed data together with the solution of the fitted model.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># S3 method for mkinfit</span> -<span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span> - <span class="va">x</span>, - fit <span class="op">=</span> <span class="va">x</span>, - obs_vars <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">fit</span><span class="op">$</span><span class="va">mkinmod</span><span class="op">$</span><span class="va">map</span><span class="op">)</span>, - xlab <span class="op">=</span> <span class="st">"Time"</span>, - ylab <span class="op">=</span> <span class="st">"Residue"</span>, - xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/range.html" class="external-link">range</a></span><span class="op">(</span><span class="va">fit</span><span class="op">$</span><span class="va">data</span><span class="op">$</span><span class="va">time</span><span class="op">)</span>, - ylim <span class="op">=</span> <span class="st">"default"</span>, - col_obs <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">obs_vars</span><span class="op">)</span>, - pch_obs <span class="op">=</span> <span class="va">col_obs</span>, - lty_obs <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">obs_vars</span><span class="op">)</span><span class="op">)</span>, - add <span class="op">=</span> <span class="cn">FALSE</span>, - legend <span class="op">=</span> <span class="op">!</span><span class="va">add</span>, - show_residuals <span class="op">=</span> <span class="cn">FALSE</span>, - show_errplot <span class="op">=</span> <span class="cn">FALSE</span>, - maxabs <span class="op">=</span> <span class="st">"auto"</span>, - sep_obs <span class="op">=</span> <span class="cn">FALSE</span>, - rel.height.middle <span class="op">=</span> <span class="fl">0.9</span>, - row_layout <span class="op">=</span> <span class="cn">FALSE</span>, - lpos <span class="op">=</span> <span class="st">"topright"</span>, - inset <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.05</span>, <span class="fl">0.05</span><span class="op">)</span>, - show_errmin <span class="op">=</span> <span class="cn">FALSE</span>, - errmin_digits <span class="op">=</span> <span class="fl">3</span>, - frame <span class="op">=</span> <span class="cn">TRUE</span>, - <span class="va">...</span> -<span class="op">)</span> - -<span class="fu">plot_sep</span><span class="op">(</span> - <span class="va">fit</span>, - show_errmin <span class="op">=</span> <span class="cn">TRUE</span>, - show_residuals <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/identical.html" class="external-link">identical</a></span><span class="op">(</span><span class="va">fit</span><span class="op">$</span><span class="va">err_mod</span>, <span class="st">"const"</span><span class="op">)</span>, <span class="cn">TRUE</span>, <span class="st">"standardized"</span><span class="op">)</span>, - <span class="va">...</span> -<span class="op">)</span> - -<span class="fu">plot_res</span><span class="op">(</span> - <span class="va">fit</span>, - sep_obs <span class="op">=</span> <span class="cn">FALSE</span>, - show_errmin <span class="op">=</span> <span class="va">sep_obs</span>, - standardized <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/identical.html" class="external-link">identical</a></span><span class="op">(</span><span class="va">fit</span><span class="op">$</span><span class="va">err_mod</span>, <span class="st">"const"</span><span class="op">)</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span><span class="op">)</span>, - <span class="va">...</span> -<span class="op">)</span> - -<span class="fu">plot_err</span><span class="op">(</span><span class="va">fit</span>, sep_obs <span class="op">=</span> <span class="cn">FALSE</span>, show_errmin <span class="op">=</span> <span class="va">sep_obs</span>, <span class="va">...</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span> +<span> <span class="va">x</span>,</span> +<span> fit <span class="op">=</span> <span class="va">x</span>,</span> +<span> obs_vars <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">fit</span><span class="op">$</span><span class="va">mkinmod</span><span class="op">$</span><span class="va">map</span><span class="op">)</span>,</span> +<span> xlab <span class="op">=</span> <span class="st">"Time"</span>,</span> +<span> ylab <span class="op">=</span> <span class="st">"Residue"</span>,</span> +<span> xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/range.html" class="external-link">range</a></span><span class="op">(</span><span class="va">fit</span><span class="op">$</span><span class="va">data</span><span class="op">$</span><span class="va">time</span><span class="op">)</span>,</span> +<span> ylim <span class="op">=</span> <span class="st">"default"</span>,</span> +<span> col_obs <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">obs_vars</span><span class="op">)</span>,</span> +<span> pch_obs <span class="op">=</span> <span class="va">col_obs</span>,</span> +<span> lty_obs <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="va">obs_vars</span><span class="op">)</span><span class="op">)</span>,</span> +<span> add <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> legend <span class="op">=</span> <span class="op">!</span><span class="va">add</span>,</span> +<span> show_residuals <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> show_errplot <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> maxabs <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> sep_obs <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> rel.height.middle <span class="op">=</span> <span class="fl">0.9</span>,</span> +<span> row_layout <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> lpos <span class="op">=</span> <span class="st">"topright"</span>,</span> +<span> inset <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.05</span>, <span class="fl">0.05</span><span class="op">)</span>,</span> +<span> show_errmin <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> errmin_digits <span class="op">=</span> <span class="fl">3</span>,</span> +<span> frame <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span> +<span></span> +<span><span class="fu">plot_sep</span><span class="op">(</span></span> +<span> <span class="va">fit</span>,</span> +<span> show_errmin <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> show_residuals <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/identical.html" class="external-link">identical</a></span><span class="op">(</span><span class="va">fit</span><span class="op">$</span><span class="va">err_mod</span>, <span class="st">"const"</span><span class="op">)</span>, <span class="cn">TRUE</span>, <span class="st">"standardized"</span><span class="op">)</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span> +<span></span> +<span><span class="fu">plot_res</span><span class="op">(</span></span> +<span> <span class="va">fit</span>,</span> +<span> sep_obs <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> show_errmin <span class="op">=</span> <span class="va">sep_obs</span>,</span> +<span> standardized <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/identical.html" class="external-link">identical</a></span><span class="op">(</span><span class="va">fit</span><span class="op">$</span><span class="va">err_mod</span>, <span class="st">"const"</span><span class="op">)</span>, <span class="cn">FALSE</span>, <span class="cn">TRUE</span><span class="op">)</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span> +<span></span> +<span><span class="fu">plot_err</span><span class="op">(</span><span class="va">fit</span>, sep_obs <span class="op">=</span> <span class="cn">FALSE</span>, show_errmin <span class="op">=</span> <span class="va">sep_obs</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> @@ -142,32 +148,56 @@ observed data together with the solution of the fitted model.</p> <dl><dt>x</dt> <dd><p>Alias for fit introduced for compatibility with the generic S3 method.</p></dd> + + <dt>fit</dt> <dd><p>An object of class <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd> + + <dt>obs_vars</dt> <dd><p>A character vector of names of the observed variables for which the data and the model should be plotted. Defauls to all observed variables in the model.</p></dd> + + <dt>xlab</dt> <dd><p>Label for the x axis.</p></dd> + + <dt>ylab</dt> <dd><p>Label for the y axis.</p></dd> + + <dt>xlim</dt> <dd><p>Plot range in x direction.</p></dd> + + <dt>ylim</dt> <dd><p>Plot range in y direction. If given as a list, plot ranges for the different plot rows can be given for row layout.</p></dd> + + <dt>col_obs</dt> <dd><p>Colors used for plotting the observed data and the corresponding model prediction lines.</p></dd> + + <dt>pch_obs</dt> <dd><p>Symbols to be used for plotting the data.</p></dd> + + <dt>lty_obs</dt> <dd><p>Line types to be used for the model predictions.</p></dd> + + <dt>add</dt> <dd><p>Should the plot be added to an existing plot?</p></dd> + + <dt>legend</dt> <dd><p>Should a legend be added to the plot?</p></dd> + + <dt>show_residuals</dt> <dd><p>Should residuals be shown? If only one plot of the fits is shown, the residual plot is in the lower third of the plot. @@ -175,47 +205,74 @@ Otherwise, i.e. if "sep_obs" is given, the residual plots will be located to the right of the plots of the fitted curves. If this is set to 'standardized', a plot of the residuals divided by the standard deviation given by the fitted error model will be shown.</p></dd> + + <dt>show_errplot</dt> <dd><p>Should squared residuals and the error model be shown? If only one plot of the fits is shown, this plot is in the lower third of the plot. Otherwise, i.e. if "sep_obs" is given, the residual plots will be located to the right of the plots of the fitted curves.</p></dd> + + <dt>maxabs</dt> <dd><p>Maximum absolute value of the residuals. This is used for the scaling of the y axis and defaults to "auto".</p></dd> + + <dt>sep_obs</dt> <dd><p>Should the observed variables be shown in separate subplots? If yes, residual plots requested by "show_residuals" will be shown next to, not below the plot of the fits.</p></dd> + + <dt>rel.height.middle</dt> <dd><p>The relative height of the middle plot, if more than two rows of plots are shown.</p></dd> + + <dt>row_layout</dt> <dd><p>Should we use a row layout where the residual plot or the error model plot is shown to the right?</p></dd> + + <dt>lpos</dt> <dd><p>Position(s) of the legend(s). Passed to <code><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></code> as the first argument. If not length one, this should be of the same length as the obs_var argument.</p></dd> + + <dt>inset</dt> <dd><p>Passed to <code><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></code> if applicable.</p></dd> + + <dt>show_errmin</dt> <dd><p>Should the FOCUS chi2 error value be shown in the upper margin of the plot?</p></dd> + + <dt>errmin_digits</dt> <dd><p>The number of significant digits for rounding the FOCUS chi2 error percentage.</p></dd> + + <dt>frame</dt> <dd><p>Should a frame be drawn around the plots?</p></dd> + + <dt>...</dt> <dd><p>Further arguments passed to <code><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></code>.</p></dd> + + <dt>standardized</dt> <dd><p>When calling 'plot_res', should the residuals be standardized in the residual plot?</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>The function is called for its side effect.</p> + + +<p>The function is called for its side effect.</p> </div> <div id="details"> <h2>Details</h2> @@ -230,41 +287,41 @@ latex is being used for the formatting of the chi2 error level, if <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"><span class="co"># One parent compound, one metabolite, both single first order, path from</span></span> -<span class="r-in"><span class="co"># parent to sink included</span></span> -<span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span>, full <span class="op">=</span> <span class="st">"Parent"</span><span class="op">)</span>,</span> -<span class="r-in"> m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, full <span class="op">=</span> <span class="st">"Metabolite M1"</span> <span class="op">)</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># One parent compound, one metabolite, both single first order, path from</span></span></span> +<span class="r-in"><span><span class="co"># parent to sink included</span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span>, full <span class="op">=</span> <span class="st">"Parent"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, full <span class="op">=</span> <span class="st">"Metabolite M1"</span> <span class="op">)</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span class="r-in"><span><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span> -<span class="r-in"><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span> +<span class="r-in"><span><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span> <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="plot.mkinfit-1.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="fu">plot_res</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu">plot_res</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="plot.mkinfit-2.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="fu">plot_res</span><span class="op">(</span><span class="va">fit</span>, standardized <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu">plot_res</span><span class="op">(</span><span class="va">fit</span>, standardized <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="plot.mkinfit-3.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="fu">plot_err</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu">plot_err</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="plot.mkinfit-4.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># Show the observed variables separately, with residuals</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fit</span>, sep_obs <span class="op">=</span> <span class="cn">TRUE</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"bottomright"</span><span class="op">)</span>,</span> -<span class="r-in"> show_errmin <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Show the observed variables separately, with residuals</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fit</span>, sep_obs <span class="op">=</span> <span class="cn">TRUE</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"bottomright"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> show_errmin <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="plot.mkinfit-5.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># The same can be obtained with less typing, using the convenience function plot_sep</span></span> -<span class="r-in"><span class="fu">plot_sep</span><span class="op">(</span><span class="va">fit</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"bottomright"</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># The same can be obtained with less typing, using the convenience function plot_sep</span></span></span> +<span class="r-in"><span><span class="fu">plot_sep</span><span class="op">(</span><span class="va">fit</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"bottomright"</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="plot.mkinfit-6.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># Show the observed variables separately, with the error model</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fit</span>, sep_obs <span class="op">=</span> <span class="cn">TRUE</span>, show_errplot <span class="op">=</span> <span class="cn">TRUE</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"bottomright"</span><span class="op">)</span>,</span> -<span class="r-in"> show_errmin <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Show the observed variables separately, with the error model</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fit</span>, sep_obs <span class="op">=</span> <span class="cn">TRUE</span>, show_errplot <span class="op">=</span> <span class="cn">TRUE</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"bottomright"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> show_errmin <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="plot.mkinfit-7.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="co"># }</span></span> -<span class="r-in"></span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -279,7 +336,7 @@ latex is being used for the formatting of the chi2 error level, if </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/plot.mmkin-2.png b/docs/reference/plot.mmkin-2.png Binary files differindex f5768c40..7af84edf 100644 --- a/docs/reference/plot.mmkin-2.png +++ b/docs/reference/plot.mmkin-2.png diff --git a/docs/reference/plot.mmkin-3.png b/docs/reference/plot.mmkin-3.png Binary files differindex c3f77d3d..56bfac50 100644 --- a/docs/reference/plot.mmkin-3.png +++ b/docs/reference/plot.mmkin-3.png diff --git a/docs/reference/plot.mmkin-4.png b/docs/reference/plot.mmkin-4.png Binary files differindex 29439156..5da05f40 100644 --- a/docs/reference/plot.mmkin-4.png +++ b/docs/reference/plot.mmkin-4.png diff --git a/docs/reference/plot.mmkin-5.png b/docs/reference/plot.mmkin-5.png Binary files differindex 89a111b0..3ec224f4 100644 --- a/docs/reference/plot.mmkin-5.png +++ b/docs/reference/plot.mmkin-5.png diff --git a/docs/reference/plot.mmkin.html b/docs/reference/plot.mmkin.html index 3dd0203b..30d0406e 100644 --- a/docs/reference/plot.mmkin.html +++ b/docs/reference/plot.mmkin.html @@ -21,7 +21,7 @@ the fit of at least one model to the same dataset is shown."><!-- mathjax --><sc </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -48,19 +48,25 @@ the fit of at least one model to the same dataset is shown."><!-- mathjax --><sc <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -94,22 +100,22 @@ the fit of at least one model to the same dataset is shown.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># S3 method for mmkin</span> -<span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span> - <span class="va">x</span>, - main <span class="op">=</span> <span class="st">"auto"</span>, - legends <span class="op">=</span> <span class="fl">1</span>, - resplot <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"time"</span>, <span class="st">"errmod"</span><span class="op">)</span>, - ylab <span class="op">=</span> <span class="st">"Residue"</span>, - standardized <span class="op">=</span> <span class="cn">FALSE</span>, - show_errmin <span class="op">=</span> <span class="cn">TRUE</span>, - errmin_var <span class="op">=</span> <span class="st">"All data"</span>, - errmin_digits <span class="op">=</span> <span class="fl">3</span>, - cex <span class="op">=</span> <span class="fl">0.7</span>, - rel.height.middle <span class="op">=</span> <span class="fl">0.9</span>, - ymax <span class="op">=</span> <span class="st">"auto"</span>, - <span class="va">...</span> -<span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span></span> +<span> <span class="va">x</span>,</span> +<span> main <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> legends <span class="op">=</span> <span class="fl">1</span>,</span> +<span> resplot <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"time"</span>, <span class="st">"errmod"</span><span class="op">)</span>,</span> +<span> ylab <span class="op">=</span> <span class="st">"Residue"</span>,</span> +<span> standardized <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> show_errmin <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> errmin_var <span class="op">=</span> <span class="st">"All data"</span>,</span> +<span> errmin_digits <span class="op">=</span> <span class="fl">3</span>,</span> +<span> cex <span class="op">=</span> <span class="fl">0.7</span>,</span> +<span> rel.height.middle <span class="op">=</span> <span class="fl">0.9</span>,</span> +<span> ymax <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> @@ -117,43 +123,70 @@ the fit of at least one model to the same dataset is shown.</p> <dl><dt>x</dt> <dd><p>An object of class <code><a href="mmkin.html">mmkin</a></code>, with either one row or one column.</p></dd> + + <dt>main</dt> <dd><p>The main title placed on the outer margin of the plot.</p></dd> + + <dt>legends</dt> <dd><p>An index for the fits for which legends should be shown.</p></dd> + + <dt>resplot</dt> <dd><p>Should the residuals plotted against time, using <code><a href="mkinresplot.html">mkinresplot</a></code>, or as squared residuals against predicted values, with the error model, using <code><a href="mkinerrplot.html">mkinerrplot</a></code>.</p></dd> + + <dt>ylab</dt> <dd><p>Label for the y axis.</p></dd> + + <dt>standardized</dt> <dd><p>Should the residuals be standardized? This option is passed to <code><a href="mkinresplot.html">mkinresplot</a></code>, it only takes effect if <code>resplot = "time"</code>.</p></dd> + + <dt>show_errmin</dt> <dd><p>Should the chi2 error level be shown on top of the plots to the left?</p></dd> + + <dt>errmin_var</dt> <dd><p>The variable for which the FOCUS chi2 error value should be shown.</p></dd> + + <dt>errmin_digits</dt> <dd><p>The number of significant digits for rounding the FOCUS chi2 error percentage.</p></dd> + + <dt>cex</dt> <dd><p>Passed to the plot functions and <code><a href="https://rdrr.io/r/graphics/mtext.html" class="external-link">mtext</a></code>.</p></dd> + + <dt>rel.height.middle</dt> <dd><p>The relative height of the middle plot, if more than two rows of plots are shown.</p></dd> + + <dt>ymax</dt> <dd><p>Maximum y axis value for <code><a href="plot.mkinfit.html">plot.mkinfit</a></code>.</p></dd> + + <dt>...</dt> <dd><p>Further arguments passed to <code><a href="plot.mkinfit.html">plot.mkinfit</a></code> and <code><a href="mkinresplot.html">mkinresplot</a></code>.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>The function is called for its side effect.</p> + + +<p>The function is called for its side effect.</p> </div> <div id="details"> <h2>Details</h2> @@ -167,32 +200,32 @@ latex is being used for the formatting of the chi2 error level.</p> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"> <span class="co"># \dontrun{</span></span> -<span class="r-in"> <span class="co"># Only use one core not to offend CRAN checks</span></span> -<span class="r-in"> <span class="va">fits</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="st">"HS"</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS B"</span> <span class="op">=</span> <span class="va">FOCUS_2006_B</span>, <span class="st">"FOCUS C"</span> <span class="op">=</span> <span class="va">FOCUS_2006_C</span><span class="op">)</span>, <span class="co"># named list for titles</span></span> -<span class="r-in"> cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span> <span class="co"># Only use one core not to offend CRAN checks</span></span></span> +<span class="r-in"><span> <span class="va">fits</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="st">"HS"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS B"</span> <span class="op">=</span> <span class="va">FOCUS_2006_B</span>, <span class="st">"FOCUS C"</span> <span class="op">=</span> <span class="va">FOCUS_2006_C</span><span class="op">)</span>, <span class="co"># named list for titles</span></span></span> +<span class="r-in"><span> cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span> <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Optimisation did not converge:</span> <span class="r-wrn co"><span class="r-pr">#></span> iteration limit reached without convergence (10)</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits</span><span class="op">[</span>, <span class="st">"FOCUS C"</span><span class="op">]</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits</span><span class="op">[</span>, <span class="st">"FOCUS C"</span><span class="op">]</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="plot.mmkin-1.png" alt="" width="700" height="433"></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="plot.mmkin-2.png" alt="" width="700" height="433"></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span>, show_errmin <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span>, show_errmin <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="plot.mmkin-3.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> -<span class="r-in"> <span class="co"># We can also plot a single fit, if we like the way plot.mmkin works, but then the plot</span></span> -<span class="r-in"> <span class="co"># height should be smaller than the plot width (this is not possible for the html pages</span></span> -<span class="r-in"> <span class="co"># generated by pkgdown, as far as I know).</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="st">"FOCUS C"</span><span class="op">]</span><span class="op">)</span> <span class="co"># same as plot(fits[1, 2])</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># We can also plot a single fit, if we like the way plot.mmkin works, but then the plot</span></span></span> +<span class="r-in"><span> <span class="co"># height should be smaller than the plot width (this is not possible for the html pages</span></span></span> +<span class="r-in"><span> <span class="co"># generated by pkgdown, as far as I know).</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="st">"FOCUS C"</span><span class="op">]</span><span class="op">)</span> <span class="co"># same as plot(fits[1, 2])</span></span></span> <span class="r-plt img"><img src="plot.mmkin-4.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> -<span class="r-in"> <span class="co"># Show the error models</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span>, resplot <span class="op">=</span> <span class="st">"errmod"</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># Show the error models</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span>, resplot <span class="op">=</span> <span class="st">"errmod"</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="plot.mmkin-5.png" alt="" width="700" height="433"></span> -<span class="r-in"> <span class="co"># }</span></span> -<span class="r-in"></span> +<span class="r-in"><span> <span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -207,7 +240,7 @@ latex is being used for the formatting of the chi2 error level.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/plot.nafta.html b/docs/reference/plot.nafta.html index af65d8eb..a7939f90 100644 --- a/docs/reference/plot.nafta.html +++ b/docs/reference/plot.nafta.html @@ -18,7 +18,7 @@ function (SFO, then IORE, then DFOP)."><!-- mathjax --><script src="https://cdnj </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -45,19 +45,25 @@ function (SFO, then IORE, then DFOP)."><!-- mathjax --><script src="https://cdnj <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -89,24 +95,33 @@ function (SFO, then IORE, then DFOP).</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># S3 method for nafta</span> -<span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, legend <span class="op">=</span> <span class="cn">FALSE</span>, main <span class="op">=</span> <span class="st">"auto"</span>, <span class="va">...</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for nafta</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">x</span>, legend <span class="op">=</span> <span class="cn">FALSE</span>, main <span class="op">=</span> <span class="st">"auto"</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>x</dt> <dd><p>An object of class <code><a href="nafta.html">nafta</a></code>.</p></dd> + + <dt>legend</dt> <dd><p>Should a legend be added?</p></dd> + + <dt>main</dt> <dd><p>Possibility to override the main title of the plot.</p></dd> + + <dt>...</dt> <dd><p>Further arguments passed to <code><a href="plot.mmkin.html">plot.mmkin</a></code>.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>The function is called for its side effect.</p> + + +<p>The function is called for its side effect.</p> </div> <div id="details"> <h2>Details</h2> @@ -129,7 +144,7 @@ function (SFO, then IORE, then DFOP).</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/read_spreadsheet.html b/docs/reference/read_spreadsheet.html new file mode 100644 index 00000000..d2873cee --- /dev/null +++ b/docs/reference/read_spreadsheet.html @@ -0,0 +1,179 @@ +<!DOCTYPE html> +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Read datasets and relevant meta information from a spreadsheet file — read_spreadsheet"><meta property="og:description" content="This function imports one dataset from each sheet of a spreadsheet file. +These sheets are selected based on the contents of a sheet 'Datasets', with +a column called 'Dataset Number', containing numbers identifying the dataset +sheets to be read in. In the second column there must be a grouping +variable, which will often be named 'Soil'. Optionally, time normalization +factors can be given in columns named 'Temperature' and 'Moisture'."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> +<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> +<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + + + <div class="container template-reference-topic"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <div class="container"> + <div class="navbar-header"> + <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> + <span class="sr-only">Toggle navigation</span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + </button> + <span class="navbar-brand"> + <a class="navbar-link" href="../index.html">mkin</a> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + </span> + </div> + + <div id="navbar" class="navbar-collapse collapse"> + <ul class="nav navbar-nav"><li> + <a href="../reference/index.html">Functions and data</a> +</li> +<li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> + Articles + + <span class="caret"></span> + </a> + <ul class="dropdown-menu" role="menu"><li> + <a href="../articles/mkin.html">Introduction to mkin</a> + </li> + <li> + <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> + </li> + <li> + <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> + </li> + <li> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + </li> + <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> + <li> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> +<li> + <a href="../news/index.html">News</a> +</li> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> + <span class="fab fa-github fa-lg"></span> + + </a> +</li> + </ul></div><!--/.nav-collapse --> + </div><!--/.container --> +</div><!--/.navbar --> + + + + </header><div class="row"> + <div class="col-md-9 contents"> + <div class="page-header"> + <h1>Read datasets and relevant meta information from a spreadsheet file</h1> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/read_spreadsheet.R" class="external-link"><code>R/read_spreadsheet.R</code></a></small> + <div class="hidden name"><code>read_spreadsheet.Rd</code></div> + </div> + + <div class="ref-description"> + <p>This function imports one dataset from each sheet of a spreadsheet file. +These sheets are selected based on the contents of a sheet 'Datasets', with +a column called 'Dataset Number', containing numbers identifying the dataset +sheets to be read in. In the second column there must be a grouping +variable, which will often be named 'Soil'. Optionally, time normalization +factors can be given in columns named 'Temperature' and 'Moisture'.</p> + </div> + + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">read_spreadsheet</span><span class="op">(</span></span> +<span> <span class="va">path</span>,</span> +<span> valid_datasets <span class="op">=</span> <span class="st">"all"</span>,</span> +<span> parent_only <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> normalize <span class="op">=</span> <span class="cn">TRUE</span></span> +<span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>path</dt> +<dd><p>Absolute or relative path to the spreadsheet file</p></dd> + + +<dt>valid_datasets</dt> +<dd><p>Optional numeric index of the valid datasets, default is +to use all datasets</p></dd> + + +<dt>parent_only</dt> +<dd><p>Should only the parent data be used?</p></dd> + + +<dt>normalize</dt> +<dd><p>Should the time scale be normalized using temperature +and moisture normalisation factors in the sheet 'Datasets'?</p></dd> + +</dl></div> + <div id="details"> + <h2>Details</h2> + <p>There must be a sheet 'Compounds', with columns 'Name' and 'Acronym'. +The first row read after the header read in from this sheet is assumed +to contain name and acronym of the parent compound.</p> +<p>The dataset sheets should be named using the dataset numbers read in from +the 'Datasets' sheet, i.e. '1', '2', ... . In each dataset sheet, name +of the observed variable (e.g. the acronym of the parent compound or +one of its transformation products) should be in the first column, +the time values should be in the second colum, and the observed value +in the third column.</p> +<p>In case relevant covariate data are available, they should be given +in a sheet 'Covariates', containing one line for each value of the grouping +variable specified in 'Datasets'. These values should be in the first +column and the column must have the same name as the second column in +'Datasets'. Covariates will be read in from columns four and higher. +Their names should preferably not contain special characters like spaces, +so they can be easily used for specifying covariate models.</p> +<p>An similar data structure is defined as the R6 class <a href="mkindsg.html">mkindsg</a>, but +is probably more complicated to use.</p> + </div> + + </div> + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> +</div> + + + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +</div> + + </footer></div> + + + + + + + </body></html> + diff --git a/docs/reference/reexports.html b/docs/reference/reexports.html index 4da7092a..24b9771e 100644 --- a/docs/reference/reexports.html +++ b/docs/reference/reexports.html @@ -28,7 +28,7 @@ intervals, nlme </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -55,19 +55,25 @@ intervals, nlme <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -120,7 +126,7 @@ below to see their documentation.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/residuals.mkinfit.html b/docs/reference/residuals.mkinfit.html index 7d9e50de..07395436 100644 --- a/docs/reference/residuals.mkinfit.html +++ b/docs/reference/residuals.mkinfit.html @@ -17,7 +17,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -44,19 +44,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -87,28 +93,33 @@ </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># S3 method for mkinfit</span> -<span class="fu"><a href="https://rdrr.io/r/stats/residuals.html" class="external-link">residuals</a></span><span class="op">(</span><span class="va">object</span>, standardized <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/stats/residuals.html" class="external-link">residuals</a></span><span class="op">(</span><span class="va">object</span>, standardized <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>object</dt> <dd><p>A <code><a href="mkinfit.html">mkinfit</a></code> object</p></dd> + + <dt>standardized</dt> <dd><p>Should the residuals be standardized by dividing by the standard deviation obtained from the fitted error model?</p></dd> + + <dt>...</dt> <dd><p>Not used</p></dd> + </dl></div> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/stats/residuals.html" class="external-link">residuals</a></span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/residuals.html" class="external-link">residuals</a></span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> [1] 0.09726374 -0.13912142 -0.15351210 0.73388322 -0.08657004 -0.93204702</span> <span class="r-out co"><span class="r-pr">#></span> [7] -0.03269080 1.45347823 -0.88423697</span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/stats/residuals.html" class="external-link">residuals</a></span><span class="op">(</span><span class="va">f</span>, standardized <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/residuals.html" class="external-link">residuals</a></span><span class="op">(</span><span class="va">f</span>, standardized <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> [1] 0.13969917 -0.19981904 -0.22048826 1.05407091 -0.12433989 -1.33869208</span> <span class="r-out co"><span class="r-pr">#></span> [7] -0.04695355 2.08761977 -1.27002287</span> </code></pre></div> @@ -125,7 +136,7 @@ standard deviation obtained from the fitted error model?</p></dd> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/saem-1.png b/docs/reference/saem-1.png Binary files differindex 08939d4f..9e310252 100644 --- a/docs/reference/saem-1.png +++ b/docs/reference/saem-1.png diff --git a/docs/reference/saem-2.png b/docs/reference/saem-2.png Binary files differindex b737db03..de1bcf57 100644 --- a/docs/reference/saem-2.png +++ b/docs/reference/saem-2.png diff --git a/docs/reference/saem-3.png b/docs/reference/saem-3.png Binary files differindex 08e0f544..de569ce0 100644 --- a/docs/reference/saem-3.png +++ b/docs/reference/saem-3.png diff --git a/docs/reference/saem-4.png b/docs/reference/saem-4.png Binary files differindex bb775c25..0f2ee3e7 100644 --- a/docs/reference/saem-4.png +++ b/docs/reference/saem-4.png diff --git a/docs/reference/saem.html b/docs/reference/saem.html index 1bc6308e..957c098e 100644 --- a/docs/reference/saem.html +++ b/docs/reference/saem.html @@ -19,7 +19,7 @@ Expectation Maximisation algorithm (SAEM)."><!-- mathjax --><script src="https:/ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.1</span> </span> </div> @@ -46,19 +46,25 @@ Expectation Maximisation algorithm (SAEM)."><!-- mathjax --><script src="https:/ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -97,14 +103,20 @@ Expectation Maximisation algorithm (SAEM).</p> <span><span class="fu">saem</span><span class="op">(</span></span> <span> <span class="va">object</span>,</span> <span> transformations <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"mkin"</span>, <span class="st">"saemix"</span><span class="op">)</span>,</span> +<span> error_model <span class="op">=</span> <span class="st">"auto"</span>,</span> <span> degparms_start <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric</a></span><span class="op">(</span><span class="op">)</span>,</span> <span> test_log_parms <span class="op">=</span> <span class="cn">TRUE</span>,</span> <span> conf.level <span class="op">=</span> <span class="fl">0.6</span>,</span> <span> solution_type <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> covariance.model <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> omega.init <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> covariates <span class="op">=</span> <span class="cn">NULL</span>,</span> +<span> covariate_models <span class="op">=</span> <span class="cn">NULL</span>,</span> +<span> no_random_effect <span class="op">=</span> <span class="cn">NULL</span>,</span> +<span> error.init <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">)</span>,</span> <span> nbiter.saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">300</span>, <span class="fl">100</span><span class="op">)</span>,</span> <span> control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>displayProgress <span class="op">=</span> <span class="cn">FALSE</span>, print <span class="op">=</span> <span class="cn">FALSE</span>, nbiter.saemix <span class="op">=</span> <span class="va">nbiter.saemix</span>,</span> <span> save <span class="op">=</span> <span class="cn">FALSE</span>, save.graphs <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> -<span> fail_with_errors <span class="op">=</span> <span class="cn">TRUE</span>,</span> <span> verbose <span class="op">=</span> <span class="cn">FALSE</span>,</span> <span> quiet <span class="op">=</span> <span class="cn">FALSE</span>,</span> <span> <span class="va">...</span></span> @@ -117,14 +129,24 @@ Expectation Maximisation algorithm (SAEM).</p> <span> <span class="va">object</span>,</span> <span> solution_type <span class="op">=</span> <span class="st">"auto"</span>,</span> <span> transformations <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"mkin"</span>, <span class="st">"saemix"</span><span class="op">)</span>,</span> +<span> error_model <span class="op">=</span> <span class="st">"auto"</span>,</span> <span> degparms_start <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric</a></span><span class="op">(</span><span class="op">)</span>,</span> +<span> covariance.model <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> no_random_effect <span class="op">=</span> <span class="cn">NULL</span>,</span> +<span> omega.init <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> covariates <span class="op">=</span> <span class="cn">NULL</span>,</span> +<span> covariate_models <span class="op">=</span> <span class="cn">NULL</span>,</span> +<span> error.init <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric</a></span><span class="op">(</span><span class="op">)</span>,</span> <span> test_log_parms <span class="op">=</span> <span class="cn">FALSE</span>,</span> <span> conf.level <span class="op">=</span> <span class="fl">0.6</span>,</span> <span> verbose <span class="op">=</span> <span class="cn">FALSE</span>,</span> <span> <span class="va">...</span></span> <span><span class="op">)</span></span> <span></span> -<span><span class="fu">saemix_data</span><span class="op">(</span><span class="va">object</span>, verbose <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> +<span><span class="fu">saemix_data</span><span class="op">(</span><span class="va">object</span>, covariates <span class="op">=</span> <span class="cn">NULL</span>, verbose <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for saem.mmkin</span></span> +<span><span class="fu"><a href="parms.html">parms</a></span><span class="op">(</span><span class="va">object</span>, ci <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> @@ -146,6 +168,10 @@ SFO, FOMC, DFOP and HS without fixing <code>parent_0</code>, and SFO or DFOP wit one SFO metabolite.</p></dd> +<dt>error_model</dt> +<dd><p>Possibility to override the error model used in the mmkin object</p></dd> + + <dt>degparms_start</dt> <dd><p>Parameter values given as a named numeric vector will be used to override the starting values obtained from the 'mmkin' object.</p></dd> @@ -168,6 +194,43 @@ for parameter that are tested if requested by 'test_log_parms'.</p></dd> automatic choice is not desired</p></dd> +<dt>covariance.model</dt> +<dd><p>Will be passed to <code><a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel()</a></code>. Per +default, uncorrelated random effects are specified for all degradation +parameters.</p></dd> + + +<dt>omega.init</dt> +<dd><p>Will be passed to <code><a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel()</a></code>. If using +mkin transformations and the default covariance model with optionally +excluded random effects, the variances of the degradation parameters +are estimated using <a href="mean_degparms.html">mean_degparms</a>, with testing of untransformed +log parameters for significant difference from zero. If not using +mkin transformations or a custom covariance model, the default +initialisation of <a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel</a> is used for omega.init.</p></dd> + + +<dt>covariates</dt> +<dd><p>A data frame with covariate data for use in +'covariate_models', with dataset names as row names.</p></dd> + + +<dt>covariate_models</dt> +<dd><p>A list containing linear model formulas with one explanatory +variable, i.e. of the type 'parameter ~ covariate'. Covariates must be available +in the 'covariates' data frame.</p></dd> + + +<dt>no_random_effect</dt> +<dd><p>Character vector of degradation parameters for +which there should be no variability over the groups. Only used +if the covariance model is not explicitly specified.</p></dd> + + +<dt>error.init</dt> +<dd><p>Will be passed to <code><a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel()</a></code>.</p></dd> + + <dt>nbiter.saemix</dt> <dd><p>Convenience option to increase the number of iterations</p></dd> @@ -177,11 +240,6 @@ iterations</p></dd> <dd><p>Passed to <a href="https://rdrr.io/pkg/saemix/man/saemix.html" class="external-link">saemix::saemix</a>.</p></dd> -<dt>fail_with_errors</dt> -<dd><p>Should a failure to compute standard errors -from the inverse of the Fisher Information Matrix be a failure?</p></dd> - - <dt>verbose</dt> <dd><p>Should we print information about created objects of type <a href="https://rdrr.io/pkg/saemix/man/SaemixModel-class.html" class="external-link">saemix::SaemixModel</a> and <a href="https://rdrr.io/pkg/saemix/man/SaemixData-class.html" class="external-link">saemix::SaemixData</a>?</p></dd> @@ -199,6 +257,11 @@ and the end of the optimisation process?</p></dd> <dt>digits</dt> <dd><p>Number of digits to use for printing</p></dd> + +<dt>ci</dt> +<dd><p>Should a matrix with estimates and confidence interval boundaries +be returned? If FALSE (default), a vector of estimates is returned.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> @@ -241,40 +304,43 @@ using <a href="mmkin.html">mmkin</a>.</p> <span class="r-in"><span><span class="va">f_saem_sfo</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="va">f_saem_fomc</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="va">f_saem_dfop</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span></span></span> -<span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_dfop</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> [1] "sd(g_qlogis)"</span> -<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_dfop</span>, covariance.model <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/diag.html" class="external-link">diag</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> Kinetic nonlinear mixed-effects model fit by SAEM</span> -<span class="r-out co"><span class="r-pr">#></span> Structural model:</span> -<span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span> -<span class="r-out co"><span class="r-pr">#></span> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span> -<span class="r-out co"><span class="r-pr">#></span> * parent</span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_sfo</span>, <span class="va">f_saem_fomc</span>, <span class="va">f_saem_dfop</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Data: 90 observations of 1 variable(s) grouped in 5 datasets</span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Data:</span> -<span class="r-out co"><span class="r-pr">#></span> 90 observations of 1 variable(s) grouped in 5 datasets</span> +<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik</span> +<span class="r-out co"><span class="r-pr">#></span> f_saem_sfo 5 624.33 622.38 -307.17</span> +<span class="r-out co"><span class="r-pr">#></span> f_saem_fomc 7 467.85 465.11 -226.92</span> +<span class="r-out co"><span class="r-pr">#></span> f_saem_dfop 9 493.76 490.24 -237.88</span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_sfo</span>, <span class="va">f_saem_dfop</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Data: 90 observations of 1 variable(s) grouped in 5 datasets</span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Likelihood computed by importance sampling</span> -<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> -<span class="r-out co"><span class="r-pr">#></span> 490.6 487.5 -237.3</span> +<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik Chisq Df Pr(>Chisq) </span> +<span class="r-out co"><span class="r-pr">#></span> f_saem_sfo 5 624.33 622.38 -307.17 </span> +<span class="r-out co"><span class="r-pr">#></span> f_saem_dfop 9 493.76 490.24 -237.88 138.57 4 < 2.2e-16 ***</span> +<span class="r-out co"><span class="r-pr">#></span> ---</span> +<span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> +<span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_dfop</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] "sd(g_qlogis)"</span> +<span class="r-in"><span><span class="va">f_saem_dfop_red</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_dfop</span>, no_random_effect <span class="op">=</span> <span class="st">"g_qlogis"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_dfop</span>, <span class="va">f_saem_dfop_red</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Data: 90 observations of 1 variable(s) grouped in 5 datasets</span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> estimate lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 93.902 91.3695 96.4339</span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.936 -3.9950 -1.8762</span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 -3.091 -4.9290 -1.2523</span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.366 -0.6484 -0.0836</span> -<span class="r-out co"><span class="r-pr">#></span> a.1 2.385 2.0033 2.7664</span> -<span class="r-out co"><span class="r-pr">#></span> SD.parent_0 2.476 0.3890 4.5623</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 1.195 0.4381 1.9517</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 2.092 0.7906 3.3932</span> -<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_saem_dfop</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> [1] 493.9811</span> +<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik Chisq Df Pr(>Chisq)</span> +<span class="r-out co"><span class="r-pr">#></span> f_saem_dfop_red 8 488.68 485.55 -236.34 </span> +<span class="r-out co"><span class="r-pr">#></span> f_saem_dfop 9 493.76 490.24 -237.88 0 1 1</span> <span class="r-in"><span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_sfo</span>, <span class="va">f_saem_fomc</span>, <span class="va">f_saem_dfop</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Data: 90 observations of 1 variable(s) grouped in 5 datasets</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik</span> +<span class="r-out co"><span class="r-pr">#></span> f_saem_sfo 5 624.33 622.38 -307.17</span> +<span class="r-out co"><span class="r-pr">#></span> f_saem_fomc 7 467.85 465.11 -226.92</span> +<span class="r-out co"><span class="r-pr">#></span> f_saem_dfop 9 493.76 490.24 -237.88</span> <span class="r-in"><span><span class="co"># The returned saem.mmkin object contains an SaemixObject, therefore we can use</span></span></span> <span class="r-in"><span><span class="co"># functions from saemix</span></span></span> <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Loading required package: npde</span> -<span class="r-msg co"><span class="r-pr">#></span> Package saemix, version 3.1</span> +<span class="r-msg co"><span class="r-pr">#></span> Package saemix, version 3.2</span> <span class="r-msg co"><span class="r-pr">#></span> please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr</span> <span class="r-msg co"><span class="r-pr">#></span> </span> <span class="r-msg co"><span class="r-pr">#></span> Attaching package: ‘saemix’</span> @@ -284,9 +350,9 @@ using <a href="mmkin.html">mmkin</a>.</p> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/compare.saemix.html" class="external-link">compare.saemix</a></span><span class="op">(</span><span class="va">f_saem_sfo</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_saem_dfop</span><span class="op">$</span><span class="va">so</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Likelihoods calculated by importance sampling</span> <span class="r-out co"><span class="r-pr">#></span> AIC BIC</span> -<span class="r-out co"><span class="r-pr">#></span> 1 624.2598 622.3070</span> -<span class="r-out co"><span class="r-pr">#></span> 2 467.8664 465.1324</span> -<span class="r-out co"><span class="r-pr">#></span> 3 493.9811 490.4660</span> +<span class="r-out co"><span class="r-pr">#></span> 1 624.3316 622.3788</span> +<span class="r-out co"><span class="r-pr">#></span> 2 467.8472 465.1132</span> +<span class="r-out co"><span class="r-pr">#></span> 3 493.7592 490.2441</span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="saem-1.png" alt="" width="700" height="433"></span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"individual.fit"</span><span class="op">)</span></span></span> @@ -300,11 +366,12 @@ using <a href="mmkin.html">mmkin</a>.</p> <span class="r-in"><span></span></span> <span class="r-in"><span><span class="va">f_mmkin_parent_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="va">f_saem_fomc_tc</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent_tc</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span><span class="op">)</span></span></span> -<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/compare.saemix.html" class="external-link">compare.saemix</a></span><span class="op">(</span><span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_saem_fomc_tc</span><span class="op">$</span><span class="va">so</span><span class="op">)</span></span></span> -<span class="r-msg co"><span class="r-pr">#></span> Likelihoods calculated by importance sampling</span> -<span class="r-out co"><span class="r-pr">#></span> AIC BIC</span> -<span class="r-out co"><span class="r-pr">#></span> 1 467.8664 465.1324</span> -<span class="r-out co"><span class="r-pr">#></span> 2 469.9096 466.7851</span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_fomc</span>, <span class="va">f_saem_fomc_tc</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Data: 90 observations of 1 variable(s) grouped in 5 datasets</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik Chisq Df Pr(>Chisq)</span> +<span class="r-out co"><span class="r-pr">#></span> f_saem_fomc 7 467.85 465.11 -226.92 </span> +<span class="r-out co"><span class="r-pr">#></span> f_saem_fomc_tc 8 469.83 466.71 -226.92 0.015 1 0.9027</span> <span class="r-in"><span></span></span> <span class="r-in"><span><span class="va">sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span></span> <span class="r-in"><span> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span> @@ -341,32 +408,32 @@ using <a href="mmkin.html">mmkin</a>.</p> <span class="r-out co"><span class="r-pr">#></span> 170 observations of 2 variable(s) grouped in 5 datasets</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Likelihood computed by importance sampling</span> -<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> -<span class="r-out co"><span class="r-pr">#></span> 842 836.9 -408</span> +<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> +<span class="r-out co"><span class="r-pr">#></span> 839.2 834.1 -406.6</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Fitted parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> estimate lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 93.7701 91.1458 96.3945</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_A1 -5.8116 -7.5998 -4.0234</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.9608 -1.3654 -0.5562</span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.5841 -3.6876 -1.4805</span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 -3.5228 -5.3254 -1.7203</span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.1027 -0.8719 0.6665</span> -<span class="r-out co"><span class="r-pr">#></span> a.1 1.8856 1.6676 2.1037</span> -<span class="r-out co"><span class="r-pr">#></span> SD.parent_0 2.7682 0.7668 4.7695</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k_A1 1.7447 0.4047 3.0848</span> -<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.4525 0.1620 0.7431</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 1.2423 0.4560 2.0285</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 2.0390 0.7601 3.3180</span> -<span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.4439 -0.3069 1.1947</span> +<span class="r-out co"><span class="r-pr">#></span> estimate lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 93.70402 91.04104 96.3670</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_A1 -5.83760 -7.66452 -4.0107</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.95718 -1.35955 -0.5548</span> +<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.35514 -3.39402 -1.3163</span> +<span class="r-out co"><span class="r-pr">#></span> log_k2 -3.79634 -5.64009 -1.9526</span> +<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.02108 -0.66463 0.6225</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 1.88191 1.66491 2.0989</span> +<span class="r-out co"><span class="r-pr">#></span> SD.parent_0 2.81628 0.78922 4.8433</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_A1 1.78751 0.42105 3.1540</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.45016 0.16116 0.7391</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 1.06923 0.31676 1.8217</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 2.03768 0.70938 3.3660</span> +<span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.44024 -0.09262 0.9731</span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="saem-4.png" alt="" width="700" height="433"></span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> saemix version used for fitting: 3.1 </span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.1.2 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.1 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Wed Aug 10 13:15:20 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Wed Aug 10 13:15:20 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> saemix version used for fitting: 3.2 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.2.1 </span> +<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Fri Nov 18 19:19:25 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Fri Nov 18 19:19:25 2022 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span> @@ -381,7 +448,7 @@ using <a href="mmkin.html">mmkin</a>.</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type analytical </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted in 9.258 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted in 9.068 s</span> <span class="r-out co"><span class="r-pr">#></span> Using 300, 100 iterations and 10 chains</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Variance model: Constant variance </span> @@ -398,230 +465,237 @@ using <a href="mmkin.html">mmkin</a>.</p> <span class="r-out co"><span class="r-pr">#></span> Results:</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Likelihood computed by importance sampling</span> -<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> -<span class="r-out co"><span class="r-pr">#></span> 842 836.9 -408</span> +<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> +<span class="r-out co"><span class="r-pr">#></span> 839.2 834.1 -406.6</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Optimised parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 93.7701 91.1458 96.3945</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_A1 -5.8116 -7.5998 -4.0234</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.9608 -1.3654 -0.5562</span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.5841 -3.6876 -1.4805</span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 -3.5228 -5.3254 -1.7203</span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.1027 -0.8719 0.6665</span> +<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 93.70402 91.04104 96.3670</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_A1 -5.83760 -7.66452 -4.0107</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.95718 -1.35955 -0.5548</span> +<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.35514 -3.39402 -1.3163</span> +<span class="r-out co"><span class="r-pr">#></span> log_k2 -3.79634 -5.64009 -1.9526</span> +<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.02108 -0.66463 0.6225</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 1.88191 1.66491 2.0989</span> +<span class="r-out co"><span class="r-pr">#></span> SD.parent_0 2.81628 0.78922 4.8433</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_A1 1.78751 0.42105 3.1540</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.45016 0.16116 0.7391</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 1.06923 0.31676 1.8217</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 2.03768 0.70938 3.3660</span> +<span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.44024 -0.09262 0.9731</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Correlation: </span> <span class="r-out co"><span class="r-pr">#></span> parnt_0 lg_k_A1 f_prnt_ log_k1 log_k2 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_A1 -0.0160 </span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.0263 0.0612 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 0.0100 -0.0014 -0.0033 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 0.0131 0.0050 -0.0011 0.0071 </span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.0419 -0.0199 0.0026 -0.0765 -0.0707</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_A1 -0.0147 </span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.0269 0.0573 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k1 0.0263 -0.0011 -0.0040 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k2 0.0020 0.0065 -0.0002 -0.0776 </span> +<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.0248 -0.0180 -0.0004 -0.0903 -0.0603</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Random effects:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> SD.parent_0 2.7682 0.7668 4.7695</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k_A1 1.7447 0.4047 3.0848</span> -<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.4525 0.1620 0.7431</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 1.2423 0.4560 2.0285</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 2.0390 0.7601 3.3180</span> -<span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.4439 -0.3069 1.1947</span> +<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> SD.parent_0 2.8163 0.78922 4.8433</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_A1 1.7875 0.42105 3.1540</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.4502 0.16116 0.7391</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 1.0692 0.31676 1.8217</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 2.0377 0.70938 3.3660</span> +<span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.4402 -0.09262 0.9731</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Variance model:</span> <span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> a.1 1.886 1.668 2.104</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 1.882 1.665 2.099</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Backtransformed parameters:</span> <span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 93.770115 9.115e+01 96.39447</span> -<span class="r-out co"><span class="r-pr">#></span> k_A1 0.002993 5.005e-04 0.01789</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_to_A1 0.276720 2.034e-01 0.36443</span> -<span class="r-out co"><span class="r-pr">#></span> k1 0.075467 2.503e-02 0.22753</span> -<span class="r-out co"><span class="r-pr">#></span> k2 0.029516 4.867e-03 0.17902</span> -<span class="r-out co"><span class="r-pr">#></span> g 0.474353 2.949e-01 0.66073</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 93.704015 9.104e+01 96.36699</span> +<span class="r-out co"><span class="r-pr">#></span> k_A1 0.002916 4.692e-04 0.01812</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_A1 0.277443 2.043e-01 0.36475</span> +<span class="r-out co"><span class="r-pr">#></span> k1 0.094880 3.357e-02 0.26813</span> +<span class="r-out co"><span class="r-pr">#></span> k2 0.022453 3.553e-03 0.14191</span> +<span class="r-out co"><span class="r-pr">#></span> g 0.494731 3.397e-01 0.65078</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Resulting formation fractions:</span> <span class="r-out co"><span class="r-pr">#></span> ff</span> -<span class="r-out co"><span class="r-pr">#></span> parent_A1 0.2767</span> -<span class="r-out co"><span class="r-pr">#></span> parent_sink 0.7233</span> +<span class="r-out co"><span class="r-pr">#></span> parent_A1 0.2774</span> +<span class="r-out co"><span class="r-pr">#></span> parent_sink 0.7226</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Estimated disappearance times:</span> -<span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_k1 DT50_k2</span> -<span class="r-out co"><span class="r-pr">#></span> parent 14.56 58.26 17.54 9.185 23.48</span> -<span class="r-out co"><span class="r-pr">#></span> A1 231.62 769.41 NA NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_k1 DT50_k2</span> +<span class="r-out co"><span class="r-pr">#></span> parent 14.0 72.38 21.79 7.306 30.87</span> +<span class="r-out co"><span class="r-pr">#></span> A1 237.7 789.68 NA NA NA</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Data:</span> <span class="r-out co"><span class="r-pr">#></span> ds name time observed predicted residual std standardized</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 0 97.2 95.78623 1.41377 1.886 0.749758</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 0 96.4 95.78623 0.61377 1.886 0.325498</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 3 71.1 71.34666 -0.24666 1.886 -0.130812</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 3 69.2 71.34666 -2.14666 1.886 -1.138429</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 6 58.1 56.49768 1.60232 1.886 0.849749</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 6 56.6 56.49768 0.10232 1.886 0.054262</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 10 44.4 44.53511 -0.13511 1.886 -0.071650</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 10 43.4 44.53511 -1.13511 1.886 -0.601974</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 20 33.3 29.77451 3.52549 1.886 1.869656</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 20 29.2 29.77451 -0.57451 1.886 -0.304675</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 34 17.6 19.32540 -1.72540 1.886 -0.915023</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 34 18.0 19.32540 -1.32540 1.886 -0.702894</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 55 10.5 10.42781 0.07219 1.886 0.038282</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 55 9.3 10.42781 -1.12781 1.886 -0.598107</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 90 4.5 3.74190 0.75810 1.886 0.402037</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 90 4.7 3.74190 0.95810 1.886 0.508102</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 112 3.0 1.96485 1.03515 1.886 0.548966</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 112 3.4 1.96485 1.43515 1.886 0.761096</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 132 2.3 1.09395 1.20605 1.886 0.639596</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 132 2.7 1.09395 1.60605 1.886 0.851726</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 3 4.3 4.72702 -0.42702 1.886 -0.226458</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 3 4.6 4.72702 -0.12702 1.886 -0.067361</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 6 7.0 7.51314 -0.51314 1.886 -0.272128</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 6 7.2 7.51314 -0.31314 1.886 -0.166063</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 10 8.2 9.63719 -1.43719 1.886 -0.762179</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 10 8.0 9.63719 -1.63719 1.886 -0.868244</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 20 11.0 11.84931 -0.84931 1.886 -0.450409</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 20 13.7 11.84931 1.85069 1.886 0.981468</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 34 11.5 12.82336 -1.32336 1.886 -0.701808</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 34 12.7 12.82336 -0.12336 1.886 -0.065418</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 55 14.9 12.89456 2.00544 1.886 1.063533</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 55 14.5 12.89456 1.60544 1.886 0.851403</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 90 12.1 11.55919 0.54081 1.886 0.286806</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 90 12.3 11.55919 0.74081 1.886 0.392871</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 112 9.9 10.42334 -0.52334 1.886 -0.277539</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 112 10.2 10.42334 -0.22334 1.886 -0.118442</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 132 8.8 9.37987 -0.57987 1.886 -0.307519</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 132 7.8 9.37987 -1.57987 1.886 -0.837844</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 0 93.6 90.95702 2.64298 1.886 1.401639</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 0 92.3 90.95702 1.34298 1.886 0.712217</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 3 87.0 84.77506 2.22494 1.886 1.179942</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 3 82.2 84.77506 -2.57506 1.886 -1.365616</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 7 74.0 77.60962 -3.60962 1.886 -1.914268</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 7 73.9 77.60962 -3.70962 1.886 -1.967301</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 14 64.2 67.50646 -3.30646 1.886 -1.753499</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 14 69.5 67.50646 1.99354 1.886 1.057221</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 30 54.0 52.48909 1.51091 1.886 0.801271</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 30 54.6 52.48909 2.11091 1.886 1.119465</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 60 41.1 39.54372 1.55628 1.886 0.825335</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 60 38.4 39.54372 -1.14372 1.886 -0.606542</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 90 32.5 33.87968 -1.37968 1.886 -0.731676</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 90 35.5 33.87968 1.62032 1.886 0.859298</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 120 28.1 30.41071 -2.31071 1.886 -1.225427</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 120 29.0 30.41071 -1.41071 1.886 -0.748135</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 180 26.5 25.36386 1.13614 1.886 0.602524</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 180 27.6 25.36386 2.23614 1.886 1.185881</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 3 3.9 2.74863 1.15137 1.886 0.610600</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 3 3.1 2.74863 0.35137 1.886 0.186341</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 7 6.9 5.92686 0.97314 1.886 0.516081</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 7 6.6 5.92686 0.67314 1.886 0.356983</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 14 10.4 10.38800 0.01200 1.886 0.006362</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 14 8.3 10.38800 -2.08800 1.886 -1.107320</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 30 14.4 16.93529 -2.53529 1.886 -1.344524</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 30 13.7 16.93529 -3.23529 1.886 -1.715751</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 60 22.1 22.33044 -0.23044 1.886 -0.122209</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 60 22.3 22.33044 -0.03044 1.886 -0.016144</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 90 27.5 24.42300 3.07700 1.886 1.631809</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 90 25.4 24.42300 0.97700 1.886 0.518127</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 120 28.0 25.51140 2.48860 1.886 1.319768</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 120 26.6 25.51140 1.08860 1.886 0.577313</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 180 25.8 26.80282 -1.00282 1.886 -0.531818</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 180 25.3 26.80282 -1.50282 1.886 -0.796981</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 0 91.9 91.08733 0.81267 1.886 0.430980</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 0 90.8 91.08733 -0.28733 1.886 -0.152377</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 1 64.9 67.55332 -2.65332 1.886 -1.407123</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 1 66.2 67.55332 -1.35332 1.886 -0.717701</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 3 43.5 41.65811 1.84189 1.886 0.976800</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 3 44.1 41.65811 2.44189 1.886 1.294994</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 8 18.3 19.65773 -1.35773 1.886 -0.720038</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 8 18.1 19.65773 -1.55773 1.886 -0.826103</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 14 10.2 10.65118 -0.45118 1.886 -0.239269</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 14 10.8 10.65118 0.14882 1.886 0.078925</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 27 4.9 3.11694 1.78306 1.886 0.945601</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 27 3.3 3.11694 0.18306 1.886 0.097082</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 48 1.6 0.43165 1.16835 1.886 0.619603</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 48 1.5 0.43165 1.06835 1.886 0.566570</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 70 1.1 0.05441 1.04559 1.886 0.554503</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 70 0.9 0.05441 0.84559 1.886 0.448438</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 1 9.6 7.66431 1.93569 1.886 1.026546</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 1 7.7 7.66431 0.03569 1.886 0.018930</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 3 15.0 15.57948 -0.57948 1.886 -0.307311</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 3 15.1 15.57948 -0.47948 1.886 -0.254279</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 8 21.2 20.38988 0.81012 1.886 0.429625</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 8 21.1 20.38988 0.71012 1.886 0.376593</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 14 19.7 20.16439 -0.46439 1.886 -0.246276</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 14 18.9 20.16439 -1.26439 1.886 -0.670535</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 27 17.5 16.40918 1.09082 1.886 0.578489</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 27 15.9 16.40918 -0.50918 1.886 -0.270030</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 48 9.5 10.12011 -0.62011 1.886 -0.328861</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 48 9.8 10.12011 -0.32011 1.886 -0.169764</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 70 6.2 5.79080 0.40920 1.886 0.217011</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 70 6.1 5.79080 0.30920 1.886 0.163979</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 0 99.8 97.38786 2.41214 1.886 1.279218</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 0 98.3 97.38786 0.91214 1.886 0.483731</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 1 77.1 79.25431 -2.15431 1.886 -1.142481</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 1 77.2 79.25431 -2.05431 1.886 -1.089449</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 3 59.0 55.69866 3.30134 1.886 1.750781</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 3 58.1 55.69866 2.40134 1.886 1.273489</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 8 27.4 31.64893 -4.24893 1.886 -2.253314</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 8 29.2 31.64893 -2.44893 1.886 -1.298729</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 14 19.1 22.57316 -3.47316 1.886 -1.841901</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 14 29.6 22.57316 7.02684 1.886 3.726507</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 27 10.1 14.11345 -4.01345 1.886 -2.128430</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 27 18.2 14.11345 4.08655 1.886 2.167199</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 48 4.5 6.95586 -2.45586 1.886 -1.302400</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 48 9.1 6.95586 2.14414 1.886 1.137093</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 70 2.3 3.31753 -1.01753 1.886 -0.539619</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 70 2.9 3.31753 -0.41753 1.886 -0.221424</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 91 2.0 1.63642 0.36358 1.886 0.192816</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 91 1.8 1.63642 0.16358 1.886 0.086751</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 120 2.0 0.61667 1.38333 1.886 0.733614</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 120 2.2 0.61667 1.58333 1.886 0.839679</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 1 4.2 3.67247 0.52753 1.886 0.279763</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 1 3.9 3.67247 0.22753 1.886 0.120666</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 3 7.4 8.36240 -0.96240 1.886 -0.510385</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 3 7.9 8.36240 -0.46240 1.886 -0.245223</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 8 14.5 12.80590 1.69410 1.886 0.898422</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 8 13.7 12.80590 0.89410 1.886 0.474162</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 14 14.2 13.99625 0.20375 1.886 0.108053</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 14 12.2 13.99625 -1.79625 1.886 -0.952596</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 27 13.7 14.22730 -0.52730 1.886 -0.279641</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 27 13.2 14.22730 -1.02730 1.886 -0.544803</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 48 13.6 13.33713 0.26287 1.886 0.139406</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 48 15.4 13.33713 2.06287 1.886 1.093991</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 70 10.4 11.84008 -1.44008 1.886 -0.763708</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 70 11.6 11.84008 -0.24008 1.886 -0.127318</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 91 10.0 10.30732 -0.30732 1.886 -0.162980</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 91 9.5 10.30732 -0.80732 1.886 -0.428142</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 120 9.1 8.33981 0.76019 1.886 0.403149</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 120 9.0 8.33981 0.66019 1.886 0.350117</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 0 96.1 93.70349 2.39651 1.886 1.270926</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 0 94.3 93.70349 0.59651 1.886 0.316342</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 8 73.9 77.86253 -3.96253 1.886 -2.101429</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 8 73.9 77.86253 -3.96253 1.886 -2.101429</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 14 69.4 70.18665 -0.78665 1.886 -0.417182</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 14 73.1 70.18665 2.91335 1.886 1.545019</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 21 65.6 64.03245 1.56755 1.886 0.831308</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 21 65.3 64.03245 1.26755 1.886 0.672210</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 41 55.9 54.71491 1.18509 1.886 0.628480</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 41 54.4 54.71491 -0.31491 1.886 -0.167007</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 63 47.0 49.63436 -2.63436 1.886 -1.397065</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 63 49.3 49.63436 -0.33436 1.886 -0.177319</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 91 44.7 45.08853 -0.38853 1.886 -0.206049</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 91 46.7 45.08853 1.61147 1.886 0.854600</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 120 42.1 41.07653 1.02347 1.886 0.542772</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 120 41.3 41.07653 0.22347 1.886 0.118513</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 8 3.3 4.08295 -0.78295 1.886 -0.415218</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 8 3.4 4.08295 -0.68295 1.886 -0.362186</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 14 3.9 6.04367 -2.14367 1.886 -1.136841</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 14 2.9 6.04367 -3.14367 1.886 -1.667165</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 21 6.4 7.59693 -1.19693 1.886 -0.634761</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 21 7.2 7.59693 -0.39693 1.886 -0.210502</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 41 9.1 9.86436 -0.76436 1.886 -0.405361</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 41 8.5 9.86436 -1.36436 1.886 -0.723555</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 63 11.7 10.99397 0.70603 1.886 0.374425</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 63 12.0 10.99397 1.00603 1.886 0.533522</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 91 13.3 11.91274 1.38726 1.886 0.735696</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 91 13.2 11.91274 1.28726 1.886 0.682663</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 120 14.3 12.66519 1.63481 1.886 0.866981</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 120 12.1 12.66519 -0.56519 1.886 -0.299733</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 0 97.2 95.70025 1.49975 1.882 0.79693</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 0 96.4 95.70025 0.69975 1.882 0.37183</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 3 71.1 71.44670 -0.34670 1.882 -0.18423</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 3 69.2 71.44670 -2.24670 1.882 -1.19384</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 6 58.1 56.59283 1.50717 1.882 0.80087</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 6 56.6 56.59283 0.00717 1.882 0.00381</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 10 44.4 44.56648 -0.16648 1.882 -0.08847</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 10 43.4 44.56648 -1.16648 1.882 -0.61984</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 20 33.3 29.76020 3.53980 1.882 1.88096</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 20 29.2 29.76020 -0.56020 1.882 -0.29767</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 34 17.6 19.39208 -1.79208 1.882 -0.95226</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 34 18.0 19.39208 -1.39208 1.882 -0.73971</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 55 10.5 10.55761 -0.05761 1.882 -0.03061</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 55 9.3 10.55761 -1.25761 1.882 -0.66826</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 90 4.5 3.84742 0.65258 1.882 0.34676</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 90 4.7 3.84742 0.85258 1.882 0.45304</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 112 3.0 2.03997 0.96003 1.882 0.51013</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 112 3.4 2.03997 1.36003 1.882 0.72268</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 132 2.3 1.14585 1.15415 1.882 0.61328</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 132 2.7 1.14585 1.55415 1.882 0.82583</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 3 4.3 4.86054 -0.56054 1.882 -0.29786</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 3 4.6 4.86054 -0.26054 1.882 -0.13844</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 6 7.0 7.74179 -0.74179 1.882 -0.39417</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 6 7.2 7.74179 -0.54179 1.882 -0.28789</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 10 8.2 9.94048 -1.74048 1.882 -0.92485</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 10 8.0 9.94048 -1.94048 1.882 -1.03112</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 20 11.0 12.19109 -1.19109 1.882 -0.63291</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 20 13.7 12.19109 1.50891 1.882 0.80180</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 34 11.5 13.10706 -1.60706 1.882 -0.85395</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 34 12.7 13.10706 -0.40706 1.882 -0.21630</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 55 14.9 13.06131 1.83869 1.882 0.97703</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 55 14.5 13.06131 1.43869 1.882 0.76448</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 90 12.1 11.54495 0.55505 1.882 0.29494</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 90 12.3 11.54495 0.75505 1.882 0.40122</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 112 9.9 10.31533 -0.41533 1.882 -0.22070</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 112 10.2 10.31533 -0.11533 1.882 -0.06128</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 132 8.8 9.20222 -0.40222 1.882 -0.21373</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 132 7.8 9.20222 -1.40222 1.882 -0.74510</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 0 93.6 90.82357 2.77643 1.882 1.47532</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 0 92.3 90.82357 1.47643 1.882 0.78453</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 3 87.0 84.73448 2.26552 1.882 1.20384</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 3 82.2 84.73448 -2.53448 1.882 -1.34675</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 7 74.0 77.65013 -3.65013 1.882 -1.93958</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 7 73.9 77.65013 -3.75013 1.882 -1.99272</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 14 64.2 67.60639 -3.40639 1.882 -1.81007</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 14 69.5 67.60639 1.89361 1.882 1.00621</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 30 54.0 52.53663 1.46337 1.882 0.77760</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 30 54.6 52.53663 2.06337 1.882 1.09642</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 60 41.1 39.42728 1.67272 1.882 0.88884</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 60 38.4 39.42728 -1.02728 1.882 -0.54587</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 90 32.5 33.76360 -1.26360 1.882 -0.67144</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 90 35.5 33.76360 1.73640 1.882 0.92268</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 120 28.1 30.39975 -2.29975 1.882 -1.22203</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 120 29.0 30.39975 -1.39975 1.882 -0.74379</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 180 26.5 25.62379 0.87621 1.882 0.46559</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 180 27.6 25.62379 1.97621 1.882 1.05010</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 3 3.9 2.70005 1.19995 1.882 0.63762</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 3 3.1 2.70005 0.39995 1.882 0.21252</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 7 6.9 5.83475 1.06525 1.882 0.56605</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 7 6.6 5.83475 0.76525 1.882 0.40663</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 14 10.4 10.26142 0.13858 1.882 0.07364</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 14 8.3 10.26142 -1.96142 1.882 -1.04225</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 30 14.4 16.82999 -2.42999 1.882 -1.29123</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 30 13.7 16.82999 -3.12999 1.882 -1.66319</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 60 22.1 22.32486 -0.22486 1.882 -0.11949</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 60 22.3 22.32486 -0.02486 1.882 -0.01321</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 90 27.5 24.45927 3.04073 1.882 1.61576</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 90 25.4 24.45927 0.94073 1.882 0.49988</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 120 28.0 25.54862 2.45138 1.882 1.30260</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 120 26.6 25.54862 1.05138 1.882 0.55868</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 180 25.8 26.82277 -1.02277 1.882 -0.54347</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 180 25.3 26.82277 -1.52277 1.882 -0.80916</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 0 91.9 91.16791 0.73209 1.882 0.38901</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 0 90.8 91.16791 -0.36791 1.882 -0.19550</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 1 64.9 67.58358 -2.68358 1.882 -1.42598</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 1 66.2 67.58358 -1.38358 1.882 -0.73520</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 3 43.5 41.62086 1.87914 1.882 0.99853</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 3 44.1 41.62086 2.47914 1.882 1.31735</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 8 18.3 19.60116 -1.30116 1.882 -0.69140</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 8 18.1 19.60116 -1.50116 1.882 -0.79768</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 14 10.2 10.63101 -0.43101 1.882 -0.22903</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 14 10.8 10.63101 0.16899 1.882 0.08980</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 27 4.9 3.12435 1.77565 1.882 0.94354</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 27 3.3 3.12435 0.17565 1.882 0.09334</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 48 1.6 0.43578 1.16422 1.882 0.61864</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 48 1.5 0.43578 1.06422 1.882 0.56550</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 70 1.1 0.05534 1.04466 1.882 0.55510</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 70 0.9 0.05534 0.84466 1.882 0.44883</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 1 9.6 7.63450 1.96550 1.882 1.04442</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 1 7.7 7.63450 0.06550 1.882 0.03481</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 3 15.0 15.52593 -0.52593 1.882 -0.27947</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 3 15.1 15.52593 -0.42593 1.882 -0.22633</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 8 21.2 20.32192 0.87808 1.882 0.46659</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 8 21.1 20.32192 0.77808 1.882 0.41345</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 14 19.7 20.09721 -0.39721 1.882 -0.21107</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 14 18.9 20.09721 -1.19721 1.882 -0.63617</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 27 17.5 16.37477 1.12523 1.882 0.59792</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 27 15.9 16.37477 -0.47477 1.882 -0.25228</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 48 9.5 10.13141 -0.63141 1.882 -0.33551</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 48 9.8 10.13141 -0.33141 1.882 -0.17610</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 70 6.2 5.81827 0.38173 1.882 0.20284</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 70 6.1 5.81827 0.28173 1.882 0.14970</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 0 99.8 97.48728 2.31272 1.882 1.22892</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 0 98.3 97.48728 0.81272 1.882 0.43186</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 1 77.1 79.29476 -2.19476 1.882 -1.16624</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 1 77.2 79.29476 -2.09476 1.882 -1.11310</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 3 59.0 55.67060 3.32940 1.882 1.76915</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 3 58.1 55.67060 2.42940 1.882 1.29092</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 8 27.4 31.57871 -4.17871 1.882 -2.22046</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 8 29.2 31.57871 -2.37871 1.882 -1.26398</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 14 19.1 22.51546 -3.41546 1.882 -1.81489</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 14 29.6 22.51546 7.08454 1.882 3.76454</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 27 10.1 14.09074 -3.99074 1.882 -2.12057</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 27 18.2 14.09074 4.10926 1.882 2.18355</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 48 4.5 6.95747 -2.45747 1.882 -1.30584</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 48 9.1 6.95747 2.14253 1.882 1.13848</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 70 2.3 3.32472 -1.02472 1.882 -0.54451</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 70 2.9 3.32472 -0.42472 1.882 -0.22569</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 91 2.0 1.64300 0.35700 1.882 0.18970</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 91 1.8 1.64300 0.15700 1.882 0.08343</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 120 2.0 0.62073 1.37927 1.882 0.73291</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 120 2.2 0.62073 1.57927 1.882 0.83918</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 1 4.2 3.64568 0.55432 1.882 0.29455</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 1 3.9 3.64568 0.25432 1.882 0.13514</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 3 7.4 8.30173 -0.90173 1.882 -0.47916</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 3 7.9 8.30173 -0.40173 1.882 -0.21347</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 8 14.5 12.71589 1.78411 1.882 0.94803</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 8 13.7 12.71589 0.98411 1.882 0.52293</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 14 14.2 13.90452 0.29548 1.882 0.15701</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 14 12.2 13.90452 -1.70452 1.882 -0.90574</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 27 13.7 14.15523 -0.45523 1.882 -0.24190</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 27 13.2 14.15523 -0.95523 1.882 -0.50759</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 48 13.6 13.31038 0.28962 1.882 0.15389</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 48 15.4 13.31038 2.08962 1.882 1.11037</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 70 10.4 11.85965 -1.45965 1.882 -0.77562</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 70 11.6 11.85965 -0.25965 1.882 -0.13797</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 91 10.0 10.36294 -0.36294 1.882 -0.19286</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 91 9.5 10.36294 -0.86294 1.882 -0.45855</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 120 9.1 8.43003 0.66997 1.882 0.35601</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 120 9.0 8.43003 0.56997 1.882 0.30287</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 0 96.1 93.95603 2.14397 1.882 1.13925</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 0 94.3 93.95603 0.34397 1.882 0.18278</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 8 73.9 77.70592 -3.80592 1.882 -2.02237</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 8 73.9 77.70592 -3.80592 1.882 -2.02237</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 14 69.4 70.04570 -0.64570 1.882 -0.34311</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 14 73.1 70.04570 3.05430 1.882 1.62298</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 21 65.6 64.01710 1.58290 1.882 0.84111</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 21 65.3 64.01710 1.28290 1.882 0.68170</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 41 55.9 54.98434 0.91566 1.882 0.48656</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 41 54.4 54.98434 -0.58434 1.882 -0.31050</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 63 47.0 49.87137 -2.87137 1.882 -1.52577</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 63 49.3 49.87137 -0.57137 1.882 -0.30361</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 91 44.7 45.06727 -0.36727 1.882 -0.19516</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 91 46.7 45.06727 1.63273 1.882 0.86759</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 120 42.1 40.76402 1.33598 1.882 0.70991</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 120 41.3 40.76402 0.53598 1.882 0.28481</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 8 3.3 4.14599 -0.84599 1.882 -0.44954</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 8 3.4 4.14599 -0.74599 1.882 -0.39640</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 14 3.9 6.08478 -2.18478 1.882 -1.16093</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 14 2.9 6.08478 -3.18478 1.882 -1.69231</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 21 6.4 7.59411 -1.19411 1.882 -0.63452</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 21 7.2 7.59411 -0.39411 1.882 -0.20942</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 41 9.1 9.78292 -0.68292 1.882 -0.36289</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 41 8.5 9.78292 -1.28292 1.882 -0.68171</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 63 11.7 10.93274 0.76726 1.882 0.40770</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 63 12.0 10.93274 1.06726 1.882 0.56711</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 91 13.3 11.93986 1.36014 1.882 0.72274</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 91 13.2 11.93986 1.26014 1.882 0.66961</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 120 14.3 12.79238 1.50762 1.882 0.80111</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 120 12.1 12.79238 -0.69238 1.882 -0.36791</span> <span class="r-in"><span></span></span> <span class="r-in"><span><span class="co"># The following takes about 6 minutes</span></span></span> <span class="r-in"><span><span class="co">#f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve",</span></span></span> diff --git a/docs/reference/schaefer07_complex_case.html b/docs/reference/schaefer07_complex_case.html index e1032453..365f2e4c 100644 --- a/docs/reference/schaefer07_complex_case.html +++ b/docs/reference/schaefer07_complex_case.html @@ -19,7 +19,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -46,19 +46,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -91,7 +97,7 @@ </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="va">schaefer07_complex_case</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">schaefer07_complex_case</span></span></code></pre></div> </div> <div id="format"> @@ -127,19 +133,19 @@ <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="va">data</span> <span class="op"><-</span> <span class="fu"><a href="mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">schaefer07_complex_case</span>, time <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span> -<span class="r-in"><span class="va">model</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span> -<span class="r-in"> parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"A1"</span>, <span class="st">"B1"</span>, <span class="st">"C1"</span><span class="op">)</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> -<span class="r-in"> A1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"A2"</span><span class="op">)</span>,</span> -<span class="r-in"> B1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> C1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> A2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">data</span> <span class="op"><-</span> <span class="fu"><a href="mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">schaefer07_complex_case</span>, time <span class="op">=</span> <span class="st">"time"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">model</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"A1"</span>, <span class="st">"B1"</span>, <span class="st">"C1"</span><span class="op">)</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> A1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"A2"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> B1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> C1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> A2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"> <span class="co"># \dontrun{</span></span> -<span class="r-in"> <span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span> <span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="schaefer07_complex_case-1.png" alt="" width="700" height="433"></span> -<span class="r-in"> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> $ff</span> <span class="r-out co"><span class="r-pr">#></span> parent_A1 parent_B1 parent_C1 parent_sink A1_A2 A1_sink </span> <span class="r-out co"><span class="r-pr">#></span> 0.3809620 0.1954667 0.4235713 0.0000000 0.4479619 0.5520381 </span> @@ -152,9 +158,9 @@ <span class="r-out co"><span class="r-pr">#></span> C1 11.23131 37.30961</span> <span class="r-out co"><span class="r-pr">#></span> A2 28.50624 94.69567</span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-in"> <span class="co"># }</span></span> -<span class="r-in"> <span class="co"># Compare with the results obtained in the original publication</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">schaefer07_complex_results</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="co"># }</span></span></span> +<span class="r-in"><span> <span class="co"># Compare with the results obtained in the original publication</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">schaefer07_complex_results</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> compound parameter KinGUI ModelMaker deviation</span> <span class="r-out co"><span class="r-pr">#></span> 1 parent degradation rate 0.0496 0.0506 2.0</span> <span class="r-out co"><span class="r-pr">#></span> 2 parent DT50 13.9900 13.6900 2.2</span> @@ -184,7 +190,7 @@ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/set_nd_nq.html b/docs/reference/set_nd_nq.html new file mode 100644 index 00000000..a16c02d7 --- /dev/null +++ b/docs/reference/set_nd_nq.html @@ -0,0 +1,261 @@ +<!DOCTYPE html> +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Set non-detects and unquantified values in residue series without replicates — set_nd_nq • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" 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set_nd_nq"><meta property="og:description" content="This function automates replacing unquantified values in residue time and +depth series. For time series, the function performs part of the residue +processing proposed in the FOCUS kinetics guidance for parent compounds +and metabolites. For two-dimensional residue series over time and depth, +it automates the proposal of Boesten et al (2015)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> +<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> +<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + + + <div class="container template-reference-topic"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <div class="container"> + <div class="navbar-header"> + <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> + <span class="sr-only">Toggle navigation</span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + </button> + <span class="navbar-brand"> + <a class="navbar-link" href="../index.html">mkin</a> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + </span> + </div> + + <div id="navbar" class="navbar-collapse collapse"> + <ul class="nav navbar-nav"><li> + <a href="../reference/index.html">Functions and data</a> +</li> +<li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> + Articles + + <span class="caret"></span> + </a> + <ul class="dropdown-menu" role="menu"><li> + <a href="../articles/mkin.html">Introduction to mkin</a> + </li> + <li> + <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> + </li> + <li> + <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> + </li> + <li> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + </li> + <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> + <li> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> +<li> + <a href="../news/index.html">News</a> +</li> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> + <span class="fab fa-github fa-lg"></span> + + </a> +</li> + </ul></div><!--/.nav-collapse --> + </div><!--/.container --> +</div><!--/.navbar --> + + + + </header><div class="row"> + <div class="col-md-9 contents"> + <div class="page-header"> + <h1>Set non-detects and unquantified values in residue series without replicates</h1> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/set_nd_nq.R" class="external-link"><code>R/set_nd_nq.R</code></a></small> + <div class="hidden name"><code>set_nd_nq.Rd</code></div> + </div> + + <div class="ref-description"> + <p>This function automates replacing unquantified values in residue time and +depth series. For time series, the function performs part of the residue +processing proposed in the FOCUS kinetics guidance for parent compounds +and metabolites. For two-dimensional residue series over time and depth, +it automates the proposal of Boesten et al (2015).</p> + </div> + + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">set_nd_nq</span><span class="op">(</span><span class="va">res_raw</span>, <span class="va">lod</span>, loq <span class="op">=</span> <span class="cn">NA</span>, time_zero_presence <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span> +<span></span> +<span><span class="fu">set_nd_nq_focus</span><span class="op">(</span></span> +<span> <span class="va">res_raw</span>,</span> +<span> <span class="va">lod</span>,</span> +<span> loq <span class="op">=</span> <span class="cn">NA</span>,</span> +<span> set_first_sample_nd <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> first_sample_nd_value <span class="op">=</span> <span class="fl">0</span>,</span> +<span> ignore_below_loq_after_first_nd <span class="op">=</span> <span class="cn">TRUE</span></span> +<span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>res_raw</dt> +<dd><p>Character vector of a residue time series, or matrix of +residue values with rows representing depth profiles for a specific sampling +time, and columns representing time series of residues at the same depth. +Values below the limit of detection (lod) have to be coded as "nd", values +between the limit of detection and the limit of quantification, if any, have +to be coded as "nq". Samples not analysed have to be coded as "na". All +values that are not "na", "nd" or "nq" have to be coercible to numeric</p></dd> + + +<dt>lod</dt> +<dd><p>Limit of detection (numeric)</p></dd> + + +<dt>loq</dt> +<dd><p>Limit of quantification(numeric). Must be specified if the FOCUS rule to +stop after the first non-detection is to be applied</p></dd> + + +<dt>time_zero_presence</dt> +<dd><p>Do we assume that residues occur at time zero? +This only affects samples from the first sampling time that have been +reported as "nd" (not detected).</p></dd> + + +<dt>set_first_sample_nd</dt> +<dd><p>Should the first sample be set to "first_sample_nd_value" +in case it is a non-detection?</p></dd> + + +<dt>first_sample_nd_value</dt> +<dd><p>Value to be used for the first sample if it is a non-detection</p></dd> + + +<dt>ignore_below_loq_after_first_nd</dt> +<dd><p>Should we ignore values below the LOQ after the first +non-detection that occurs after the quantified values?</p></dd> + +</dl></div> + <div id="value"> + <h2>Value</h2> + + +<p>A numeric vector, if a vector was supplied, or a numeric matrix otherwise</p> + </div> + <div id="functions"> + <h2>Functions</h2> + +<ul><li><p><code>set_nd_nq_focus()</code>: Set non-detects in residue time series according to FOCUS rules</p></li> +</ul></div> + <div id="references"> + <h2>References</h2> + <p>Boesten, J. J. T. I., van der Linden, A. M. A., Beltman, W. H. +J. and Pol, J. W. (2015). Leaching of plant protection products and their +transformation products; Proposals for improving the assessment of leaching +to groundwater in the Netherlands — Version 2. Alterra report 2630, Alterra +Wageningen UR (University & Research centre)</p> +<p>FOCUS (2014) Generic Guidance for Estimating Persistence and Degradation +Kinetics from Environmental Fate Studies on Pesticides in EU Registration, Version 1.1, +18 December 2014, p. 251</p> + </div> + + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># FOCUS (2014) p. 75/76 and 131/132</span></span></span> +<span class="r-in"><span><span class="va">parent_1</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">.12</span>, <span class="fl">.09</span>, <span class="fl">.05</span>, <span class="fl">.03</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu">set_nd_nq</span><span class="op">(</span><span class="va">parent_1</span>, <span class="fl">0.02</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] 0.12 0.09 0.05 0.03 0.01 NA NA NA NA NA</span> +<span class="r-in"><span><span class="va">parent_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">.12</span>, <span class="fl">.09</span>, <span class="fl">.05</span>, <span class="fl">.03</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="fl">.03</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu">set_nd_nq</span><span class="op">(</span><span class="va">parent_2</span>, <span class="fl">0.02</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] 0.12 0.09 0.05 0.03 0.01 0.01 0.03 0.01 NA NA</span> +<span class="r-in"><span><span class="fu">set_nd_nq_focus</span><span class="op">(</span><span class="va">parent_2</span>, <span class="fl">0.02</span>, loq <span class="op">=</span> <span class="fl">0.05</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] 0.12 0.09 0.05 0.03 0.01 NA NA NA NA NA</span> +<span class="r-in"><span><span class="va">parent_3</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">.12</span>, <span class="fl">.09</span>, <span class="fl">.05</span>, <span class="fl">.03</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="fl">.06</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu">set_nd_nq</span><span class="op">(</span><span class="va">parent_3</span>, <span class="fl">0.02</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] 0.12 0.09 0.05 0.03 0.01 0.01 0.06 0.01 NA NA</span> +<span class="r-in"><span><span class="fu">set_nd_nq_focus</span><span class="op">(</span><span class="va">parent_3</span>, <span class="fl">0.02</span>, loq <span class="op">=</span> <span class="fl">0.05</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] 0.12 0.09 0.05 0.03 0.01 0.01 0.06 0.01 NA NA</span> +<span class="r-in"><span><span class="va">metabolite</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>, <span class="fl">0.03</span>, <span class="fl">0.06</span>, <span class="fl">0.10</span>, <span class="fl">0.11</span>, <span class="fl">0.10</span>, <span class="fl">0.09</span>, <span class="fl">0.05</span>, <span class="fl">0.03</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu">set_nd_nq</span><span class="op">(</span><span class="va">metabolite</span>, <span class="fl">0.02</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] NA NA 0.01 0.03 0.06 0.10 0.11 0.10 0.09 0.05 0.03 0.01 NA</span> +<span class="r-in"><span><span class="fu">set_nd_nq_focus</span><span class="op">(</span><span class="va">metabolite</span>, <span class="fl">0.02</span>, <span class="fl">0.05</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [1] 0.00 NA 0.01 0.03 0.06 0.10 0.11 0.10 0.09 0.05 0.03 0.01 NA</span> +<span class="r-in"><span><span class="co">#</span></span></span> +<span class="r-in"><span><span class="co"># Boesten et al. (2015), p. 57/58</span></span></span> +<span class="r-in"><span><span class="va">table_8</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">10</span>, <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="fl">4</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fl">10</span>, <span class="fl">10</span>, <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nq"</span>, <span class="fl">2</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="fl">2</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fl">10</span>, <span class="fl">10</span>, <span class="fl">10</span>, <span class="st">"nq"</span>, <span class="st">"nd"</span>, <span class="st">"nd"</span>,</span></span> +<span class="r-in"><span> <span class="st">"nq"</span>, <span class="fl">10</span>, <span class="st">"nq"</span>, <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="fl">3</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="st">"nd"</span>, <span class="st">"nq"</span>, <span class="st">"nq"</span>, <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="fl">3</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="fl">6</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="fl">6</span><span class="op">)</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> ncol <span class="op">=</span> <span class="fl">6</span>, byrow <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu">set_nd_nq</span><span class="op">(</span><span class="va">table_8</span>, <span class="fl">0.5</span>, <span class="fl">1.5</span>, time_zero_presence <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [,1] [,2] [,3] [,4] [,5] [,6]</span> +<span class="r-out co"><span class="r-pr">#></span> [1,] 10.00 10.00 0.25 0.25 NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> [2,] 10.00 10.00 1.00 1.00 0.25 NA</span> +<span class="r-out co"><span class="r-pr">#></span> [3,] 10.00 10.00 10.00 1.00 0.25 NA</span> +<span class="r-out co"><span class="r-pr">#></span> [4,] 1.00 10.00 1.00 0.25 NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> [5,] 0.25 1.00 1.00 0.25 NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> [6,] NA 0.25 0.25 NA NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> [7,] NA NA NA NA NA NA</span> +<span class="r-in"><span><span class="va">table_10</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">10</span>, <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="fl">4</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fl">10</span>, <span class="fl">10</span>, <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="fl">4</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fl">10</span>, <span class="fl">10</span>, <span class="fl">10</span>, <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="fl">3</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="st">"nd"</span>, <span class="fl">10</span>, <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="fl">4</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="st">"nd"</span>, <span class="fl">18</span><span class="op">)</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> ncol <span class="op">=</span> <span class="fl">6</span>, byrow <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu">set_nd_nq</span><span class="op">(</span><span class="va">table_10</span>, <span class="fl">0.5</span>, time_zero_presence <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> [,1] [,2] [,3] [,4] [,5] [,6]</span> +<span class="r-out co"><span class="r-pr">#></span> [1,] 10.00 10.00 0.25 NA NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> [2,] 10.00 10.00 0.25 NA NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> [3,] 10.00 10.00 10.00 0.25 NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> [4,] 0.25 10.00 0.25 NA NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> [5,] NA 0.25 NA NA NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> [6,] NA NA NA NA NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> [7,] NA NA NA NA NA NA</span> +</code></pre></div> + </div> + </div> + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> +</div> + + + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +</div> + + </footer></div> + + + + + + + </body></html> + diff --git a/docs/reference/sigma_twocomp.html b/docs/reference/sigma_twocomp.html index 23102f17..c1db0d30 100644 --- a/docs/reference/sigma_twocomp.html +++ b/docs/reference/sigma_twocomp.html @@ -18,7 +18,7 @@ dependence of the measured value \(y\):"><!-- mathjax --><script src="https://cd </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -45,19 +45,25 @@ dependence of the measured value \(y\):"><!-- mathjax --><script src="https://cd <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -89,23 +95,30 @@ dependence of the measured value \(y\):</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">sigma_twocomp</span><span class="op">(</span><span class="va">y</span>, <span class="va">sigma_low</span>, <span class="va">rsd_high</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">sigma_twocomp</span><span class="op">(</span><span class="va">y</span>, <span class="va">sigma_low</span>, <span class="va">rsd_high</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>y</dt> <dd><p>The magnitude of the observed value</p></dd> + + <dt>sigma_low</dt> <dd><p>The asymptotic minimum of the standard deviation for low observed values</p></dd> + + <dt>rsd_high</dt> <dd><p>The coefficient describing the increase of the standard deviation with the magnitude of the observed value</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>The standard deviation of the response variable.</p> + + +<p>The standard deviation of the response variable.</p> </div> <div id="details"> <h2>Details</h2> @@ -131,26 +144,26 @@ Degradation Data. <em>Environments</em> 6(12) 124 <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="va">times</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span> -<span class="r-in"><span class="va">d_pred</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>time <span class="op">=</span> <span class="va">times</span>, parent <span class="op">=</span> <span class="fl">100</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">exp</a></span><span class="op">(</span><span class="op">-</span> <span class="fl">0.03</span> <span class="op">*</span> <span class="va">times</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123456</span><span class="op">)</span></span> -<span class="r-in"><span class="va">d_syn</span> <span class="op"><-</span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_pred</span>, <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="fu">sigma_twocomp</span><span class="op">(</span><span class="va">y</span>, <span class="fl">1</span>, <span class="fl">0.07</span><span class="op">)</span>,</span> -<span class="r-in"> reps <span class="op">=</span> <span class="fl">2</span>, n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span> -<span class="r-in"><span class="va">f_nls</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/nls.html" class="external-link">nls</a></span><span class="op">(</span><span class="va">value</span> <span class="op">~</span> <span class="fu"><a href="https://rdrr.io/r/stats/SSasymp.html" class="external-link">SSasymp</a></span><span class="op">(</span><span class="va">time</span>, <span class="fl">0</span>, <span class="va">parent_0</span>, <span class="va">lrc</span><span class="op">)</span>, data <span class="op">=</span> <span class="va">d_syn</span>,</span> -<span class="r-in"> start <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>parent_0 <span class="op">=</span> <span class="fl">100</span>, lrc <span class="op">=</span> <span class="op">-</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://svn.r-project.org/R-packages/trunk/nlme/" class="external-link">nlme</a></span><span class="op">)</span></span> -<span class="r-in"><span class="va">f_gnls</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/gnls.html" class="external-link">gnls</a></span><span class="op">(</span><span class="va">value</span> <span class="op">~</span> <span class="fu"><a href="https://rdrr.io/r/stats/SSasymp.html" class="external-link">SSasymp</a></span><span class="op">(</span><span class="va">time</span>, <span class="fl">0</span>, <span class="va">parent_0</span>, <span class="va">lrc</span><span class="op">)</span>,</span> -<span class="r-in"> data <span class="op">=</span> <span class="va">d_syn</span>, na.action <span class="op">=</span> <span class="va">na.omit</span>,</span> -<span class="r-in"> start <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>parent_0 <span class="op">=</span> <span class="fl">100</span>, lrc <span class="op">=</span> <span class="op">-</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="fu">findFunction</span><span class="op">(</span><span class="st">"varConstProp"</span><span class="op">)</span><span class="op">)</span> <span class="op">></span> <span class="fl">0</span><span class="op">)</span> <span class="op">{</span></span> -<span class="r-in"> <span class="va">f_gnls_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_gnls</span>, weights <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/varConstProp.html" class="external-link">varConstProp</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"> <span class="va">f_gnls_tc_sf</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_gnls_tc</span>, control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>sigma <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"><span class="op">}</span></span> -<span class="r-in"><span class="va">f_mkin</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">d_syn</span>, error_model <span class="op">=</span> <span class="st">"const"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="va">f_mkin_tc</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">d_syn</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">f_mkin_tc</span>, standardized <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">times</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">d_pred</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>time <span class="op">=</span> <span class="va">times</span>, parent <span class="op">=</span> <span class="fl">100</span> <span class="op">*</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">exp</a></span><span class="op">(</span><span class="op">-</span> <span class="fl">0.03</span> <span class="op">*</span> <span class="va">times</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123456</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">d_syn</span> <span class="op"><-</span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_pred</span>, <span class="kw">function</span><span class="op">(</span><span class="va">y</span><span class="op">)</span> <span class="fu">sigma_twocomp</span><span class="op">(</span><span class="va">y</span>, <span class="fl">1</span>, <span class="fl">0.07</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> reps <span class="op">=</span> <span class="fl">2</span>, n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span></span> +<span class="r-in"><span><span class="va">f_nls</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/nls.html" class="external-link">nls</a></span><span class="op">(</span><span class="va">value</span> <span class="op">~</span> <span class="fu"><a href="https://rdrr.io/r/stats/SSasymp.html" class="external-link">SSasymp</a></span><span class="op">(</span><span class="va">time</span>, <span class="fl">0</span>, <span class="va">parent_0</span>, <span class="va">lrc</span><span class="op">)</span>, data <span class="op">=</span> <span class="va">d_syn</span>,</span></span> +<span class="r-in"><span> start <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>parent_0 <span class="op">=</span> <span class="fl">100</span>, lrc <span class="op">=</span> <span class="op">-</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://svn.r-project.org/R-packages/trunk/nlme/" class="external-link">nlme</a></span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">f_gnls</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/gnls.html" class="external-link">gnls</a></span><span class="op">(</span><span class="va">value</span> <span class="op">~</span> <span class="fu"><a href="https://rdrr.io/r/stats/SSasymp.html" class="external-link">SSasymp</a></span><span class="op">(</span><span class="va">time</span>, <span class="fl">0</span>, <span class="va">parent_0</span>, <span class="va">lrc</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> data <span class="op">=</span> <span class="va">d_syn</span>, na.action <span class="op">=</span> <span class="va">na.omit</span>,</span></span> +<span class="r-in"><span> start <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>parent_0 <span class="op">=</span> <span class="fl">100</span>, lrc <span class="op">=</span> <span class="op">-</span><span class="fl">3</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="fu">findFunction</span><span class="op">(</span><span class="st">"varConstProp"</span><span class="op">)</span><span class="op">)</span> <span class="op">></span> <span class="fl">0</span><span class="op">)</span> <span class="op">{</span></span></span> +<span class="r-in"><span> <span class="va">f_gnls_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_gnls</span>, weights <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/varConstProp.html" class="external-link">varConstProp</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="va">f_gnls_tc_sf</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_gnls_tc</span>, control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>sigma <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="op">}</span></span></span> +<span class="r-in"><span><span class="va">f_mkin</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">d_syn</span>, error_model <span class="op">=</span> <span class="st">"const"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">f_mkin_tc</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">d_syn</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">f_mkin_tc</span>, standardized <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="sigma_twocomp-1.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_nls</span>, <span class="va">f_gnls</span>, <span class="va">f_gnls_tc</span>, <span class="va">f_gnls_tc_sf</span>, <span class="va">f_mkin</span>, <span class="va">f_mkin_tc</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_nls</span>, <span class="va">f_gnls</span>, <span class="va">f_gnls_tc</span>, <span class="va">f_gnls_tc_sf</span>, <span class="va">f_mkin</span>, <span class="va">f_mkin_tc</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> df AIC</span> <span class="r-out co"><span class="r-pr">#></span> f_nls 3 114.4817</span> <span class="r-out co"><span class="r-pr">#></span> f_gnls 3 114.4817</span> @@ -172,7 +185,7 @@ Degradation Data. <em>Environments</em> 6(12) 124 </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/status.html b/docs/reference/status.html new file mode 100644 index 00000000..8adf0113 --- /dev/null +++ b/docs/reference/status.html @@ -0,0 +1,174 @@ +<!DOCTYPE html> +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Method to get status information for fit array objects — status • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" 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--><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> +<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> +<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + + + <div class="container template-reference-topic"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <div class="container"> + <div class="navbar-header"> + <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> + <span class="sr-only">Toggle navigation</span> + <span 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class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mmkin</span></span> +<span><span class="fu">status</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for status.mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for mhmkin</span></span> +<span><span class="fu">status</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for status.mhmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>object</dt> +<dd><p>The object to investigate</p></dd> + + +<dt>...</dt> +<dd><p>For potential future extensions</p></dd> + + +<dt>x</dt> +<dd><p>The object to be printed</p></dd> + +</dl></div> + <div id="value"> + <h2>Value</h2> + + +<p>An object with the same dimensions as the fit array +suitable printing method.</p> + </div> + + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="va">fits</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS A"</span> <span class="op">=</span> <span class="va">FOCUS_2006_A</span>,</span></span> +<span class="r-in"><span> <span class="st">"FOCUS B"</span> <span class="op">=</span> <span class="va">FOCUS_2006_C</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu">status</span><span class="op">(</span><span class="va">fits</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> dataset</span> +<span class="r-out co"><span class="r-pr">#></span> model FOCUS A FOCUS B</span> +<span class="r-out co"><span class="r-pr">#></span> SFO OK OK </span> +<span class="r-out co"><span class="r-pr">#></span> FOMC OK OK </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> OK: No warnings</span> +<span class="r-in"><span><span class="co"># }</span></span></span> +</code></pre></div> + </div> + </div> + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> +</div> + + + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +</div> + + </footer></div> + + + + + + + </body></html> + diff --git a/docs/reference/summary.mkinfit.html b/docs/reference/summary.mkinfit.html index 17ecdfe4..0bb7f424 100644 --- a/docs/reference/summary.mkinfit.html +++ b/docs/reference/summary.mkinfit.html @@ -21,7 +21,7 @@ values."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mat </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -48,19 +48,25 @@ values."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mat <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -96,7 +102,7 @@ values.</p> <div id="ref-usage"> <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span> -<span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, data <span class="op">=</span> <span class="cn">TRUE</span>, distimes <span class="op">=</span> <span class="cn">TRUE</span>, alpha <span class="op">=</span> <span class="fl">0.05</span>, <span class="va">...</span><span class="op">)</span></span> +<span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, data <span class="op">=</span> <span class="cn">TRUE</span>, distimes <span class="op">=</span> <span class="cn">TRUE</span>, alpha <span class="op">=</span> <span class="fl">0.05</span>, <span class="va">...</span><span class="op">)</span></span> <span></span> <span><span class="co"># S3 method for summary.mkinfit</span></span> <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> @@ -105,7 +111,7 @@ values.</p> <div id="arguments"> <h2>Arguments</h2> <dl><dt>object</dt> -<dd><p>an object of class <code><a href="mkinfit.html">mkinfit</a></code>.</p></dd> +<dd><p>an object of class <a href="mkinfit.html">mkinfit</a>.</p></dd> <dt>data</dt> @@ -186,7 +192,8 @@ model.</p></dd> <dd><p>The DT50 and DT90 values for each observed variable.</p></dd> <dt>SFORB</dt> -<dd><p>If applicable, eigenvalues of SFORB components of the model.</p></dd> +<dd><p>If applicable, eigenvalues and fractional eigenvector component +g of SFORB systems in the model.</p></dd> </dl><p>The print method is called for its side effect, i.e. printing the summary.</p> </div> @@ -206,11 +213,11 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, <div id="ref-examples"> <h2>Examples</h2> <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> -<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_A</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.1.2 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.1 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Fri Jul 22 11:39:29 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Fri Jul 22 11:39:29 2022 </span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_A</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.2.0 </span> +<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Nov 17 14:03:23 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Nov 17 14:03:23 2022 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - k_parent * parent</span> @@ -296,7 +303,7 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.5.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/summary.mmkin.html b/docs/reference/summary.mmkin.html index dfbd766e..9edf10c0 100644 --- a/docs/reference/summary.mmkin.html +++ b/docs/reference/summary.mmkin.html @@ -1,5 +1,5 @@ <!DOCTYPE html> -<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Summary method for class "mmkin" — summary.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Summary method for class " mmkin summary.mmkin><meta property="og:description" content="Shows convergence information on the mkinfit objects contained in the object +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Summary method for class "mmkin" — summary.mmkin • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Summary method for class " mmkin summary.mmkin><meta property="og:description" content="Shows status information on the mkinfit objects contained in the object and gives an overview of ill-defined parameters calculated by illparms."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> @@ -18,7 +18,7 @@ and gives an overview of ill-defined parameters calculated by illparms."><!-- ma </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -45,19 +45,25 @@ and gives an overview of ill-defined parameters calculated by illparms."><!-- ma <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -84,13 +90,13 @@ and gives an overview of ill-defined parameters calculated by illparms."><!-- ma </div> <div class="ref-description"> - <p>Shows convergence information on the <a href="mkinfit.html">mkinfit</a> objects contained in the object + <p>Shows status information on the <a href="mkinfit.html">mkinfit</a> objects contained in the object and gives an overview of ill-defined parameters calculated by <a href="illparms.html">illparms</a>.</p> </div> <div id="ref-usage"> <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mmkin</span></span> -<span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, conf.level <span class="op">=</span> <span class="fl">0.95</span>, <span class="va">...</span><span class="op">)</span></span> +<span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, conf.level <span class="op">=</span> <span class="fl">0.95</span>, <span class="va">...</span><span class="op">)</span></span> <span></span> <span><span class="co"># S3 method for summary.mmkin</span></span> <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> @@ -127,11 +133,11 @@ and gives an overview of ill-defined parameters calculated by <a href="illparms. <span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"FOCUS A"</span> <span class="op">=</span> <span class="va">FOCUS_2006_A</span>,</span></span> <span class="r-in"><span> <span class="st">"FOCUS C"</span> <span class="op">=</span> <span class="va">FOCUS_2006_C</span><span class="op">)</span>,</span></span> <span class="r-in"><span> quiet <span class="op">=</span> <span class="cn">TRUE</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span> -<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fits</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fits</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Error model: Constant variance </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted in 0.607 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted in 0.835 s</span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Convergence:</span> +<span class="r-out co"><span class="r-pr">#></span> Status:</span> <span class="r-out co"><span class="r-pr">#></span> dataset</span> <span class="r-out co"><span class="r-pr">#></span> model FOCUS A FOCUS C</span> <span class="r-out co"><span class="r-pr">#></span> SFO OK OK </span> @@ -159,7 +165,7 @@ and gives an overview of ill-defined parameters calculated by <a href="illparms. </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.5.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/summary.nlme.mmkin.html b/docs/reference/summary.nlme.mmkin.html index ff014cc1..eb01ef7a 100644 --- a/docs/reference/summary.nlme.mmkin.html +++ b/docs/reference/summary.nlme.mmkin.html @@ -21,7 +21,7 @@ endpoints such as formation fractions and DT50 values. Optionally </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -48,19 +48,25 @@ endpoints such as formation fractions and DT50 values. Optionally <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -95,68 +101,95 @@ endpoints such as formation fractions and DT50 values. Optionally </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># S3 method for nlme.mmkin</span> -<span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span> - <span class="va">object</span>, - data <span class="op">=</span> <span class="cn">FALSE</span>, - verbose <span class="op">=</span> <span class="cn">FALSE</span>, - distimes <span class="op">=</span> <span class="cn">TRUE</span>, - alpha <span class="op">=</span> <span class="fl">0.05</span>, - <span class="va">...</span> -<span class="op">)</span> - -<span class="co"># S3 method for summary.nlme.mmkin</span> -<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, verbose <span class="op">=</span> <span class="va">x</span><span class="op">$</span><span class="va">verbose</span>, <span class="va">...</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for nlme.mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span></span> +<span> <span class="va">object</span>,</span> +<span> data <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> verbose <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> distimes <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> alpha <span class="op">=</span> <span class="fl">0.05</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for summary.nlme.mmkin</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, verbose <span class="op">=</span> <span class="va">x</span><span class="op">$</span><span class="va">verbose</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>object</dt> <dd><p>an object of class <a href="nlme.mmkin.html">nlme.mmkin</a></p></dd> + + <dt>data</dt> <dd><p>logical, indicating whether the full data should be included in the summary.</p></dd> + + <dt>verbose</dt> <dd><p>Should the summary be verbose?</p></dd> + + <dt>distimes</dt> <dd><p>logical, indicating whether DT50 and DT90 values should be included.</p></dd> + + <dt>alpha</dt> <dd><p>error level for confidence interval estimation from the t distribution</p></dd> + + <dt>...</dt> <dd><p>optional arguments passed to methods like <code>print</code>.</p></dd> + + <dt>x</dt> <dd><p>an object of class summary.nlme.mmkin</p></dd> + + <dt>digits</dt> <dd><p>Number of digits to use for printing</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>The summary function returns a list based on the <a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a> object + + +<p>The summary function returns a list based on the <a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a> object obtained in the fit, with at least the following additional components</p> <dl><dt>nlmeversion, mkinversion, Rversion</dt> <dd><p>The nlme, mkin and R versions used</p></dd> + <dt>date.fit, date.summary</dt> <dd><p>The dates where the fit and the summary were produced</p></dd> + <dt>diffs</dt> <dd><p>The differential equations used in the degradation model</p></dd> + <dt>use_of_ff</dt> <dd><p>Was maximum or minimum use made of formation fractions</p></dd> + <dt>data</dt> <dd><p>The data</p></dd> + <dt>confint_trans</dt> <dd><p>Transformed parameters as used in the optimisation, with confidence intervals</p></dd> + <dt>confint_back</dt> <dd><p>Backtransformed parameters, with confidence intervals if available</p></dd> + <dt>ff</dt> <dd><p>The estimated formation fractions derived from the fitted model.</p></dd> + <dt>distimes</dt> <dd><p>The DT50 and DT90 values for each observed variable.</p></dd> + <dt>SFORB</dt> <dd><p>If applicable, eigenvalues of SFORB components of the model.</p></dd> + </dl><p>The print method is called for its side effect, i.e. printing the summary.</p> </div> <div id="author"> @@ -167,44 +200,46 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"><span class="co"># Generate five datasets following SFO kinetics</span></span> -<span class="r-in"><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span> -<span class="r-in"><span class="va">dt50_sfo_in_pop</span> <span class="op"><-</span> <span class="fl">50</span></span> -<span class="r-in"><span class="va">k_in_pop</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span> <span class="op">/</span> <span class="va">dt50_sfo_in_pop</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">1234</span><span class="op">)</span></span> -<span class="r-in"><span class="va">k_in</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="va">k_in_pop</span><span class="op">)</span>, <span class="fl">0.5</span><span class="op">)</span></span> -<span class="r-in"><span class="va">SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">pred_sfo</span> <span class="op"><-</span> <span class="kw">function</span><span class="op">(</span><span class="va">k</span><span class="op">)</span> <span class="op">{</span></span> -<span class="r-in"> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">SFO</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="va">k</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="va">sampling_times</span><span class="op">)</span></span> -<span class="r-in"><span class="op">}</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">ds_sfo_mean</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">k_in</span>, <span class="va">pred_sfo</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">ds_sfo_mean</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"ds"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">12345</span><span class="op">)</span></span> -<span class="r-in"><span class="va">ds_sfo_syn</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">ds_sfo_mean</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op">{</span></span> -<span class="r-in"> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">ds</span>,</span> -<span class="r-in"> sdfunc <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="fl">1</span><span class="op">^</span><span class="fl">2</span> <span class="op">+</span> <span class="va">value</span><span class="op">^</span><span class="fl">2</span> <span class="op">*</span> <span class="fl">0.07</span><span class="op">^</span><span class="fl">2</span><span class="op">)</span>,</span> -<span class="r-in"> n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span> -<span class="r-in"><span class="op">}</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"><span class="co"># Evaluate using mmkin and nlme</span></span> -<span class="r-in"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://svn.r-project.org/R-packages/trunk/nlme/" class="external-link">nlme</a></span><span class="op">)</span></span> -<span class="r-in"><span class="va">f_mmkin</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">ds_sfo_syn</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span> -<span class="r-in"><span class="va">f_nlme</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Generate five datasets following SFO kinetics</span></span></span> +<span class="r-in"><span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">dt50_sfo_in_pop</span> <span class="op"><-</span> <span class="fl">50</span></span></span> +<span class="r-in"><span><span class="va">k_in_pop</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span> <span class="op">/</span> <span class="va">dt50_sfo_in_pop</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">1234</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">k_in</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/Lognormal.html" class="external-link">rlnorm</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="va">k_in_pop</span><span class="op">)</span>, <span class="fl">0.5</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">pred_sfo</span> <span class="op"><-</span> <span class="kw">function</span><span class="op">(</span><span class="va">k</span><span class="op">)</span> <span class="op">{</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">SFO</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="va">k</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="va">sampling_times</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="op">}</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">ds_sfo_mean</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">k_in</span>, <span class="va">pred_sfo</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">ds_sfo_mean</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"ds"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">12345</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">ds_sfo_syn</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">ds_sfo_mean</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op">{</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">ds</span>,</span></span> +<span class="r-in"><span> sdfunc <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="fl">1</span><span class="op">^</span><span class="fl">2</span> <span class="op">+</span> <span class="va">value</span><span class="op">^</span><span class="fl">2</span> <span class="op">*</span> <span class="fl">0.07</span><span class="op">^</span><span class="fl">2</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> n <span class="op">=</span> <span class="fl">1</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span></span> +<span class="r-in"><span><span class="op">}</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="co"># Evaluate using mmkin and nlme</span></span></span> +<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://svn.r-project.org/R-packages/trunk/nlme/" class="external-link">nlme</a></span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">f_mmkin</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">ds_sfo_syn</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span> +<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Optimisation did not converge:</span> +<span class="r-wrn co"><span class="r-pr">#></span> iteration limit reached without convergence (10)</span> +<span class="r-in"><span><span class="va">f_nlme</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">)</span></span></span> <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Iteration 4, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_nlme</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-out co"><span class="r-pr">#></span> nlme version used for fitting: 3.1.155 </span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.1.0 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.1 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Jun 30 10:23:57 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Jun 30 10:23:57 2022 </span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_nlme</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> nlme version used for fitting: 3.1.160 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.2.0 </span> +<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Nov 17 14:03:27 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Nov 17 14:03:27 2022 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - k_parent * parent</span> @@ -220,7 +255,7 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Mean of starting values for individual parameters:</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_parent </span> -<span class="r-out co"><span class="r-pr">#></span> 101.612 -4.454 </span> +<span class="r-out co"><span class="r-pr">#></span> 101.569 -4.454 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Fixed degradation parameter values:</span> <span class="r-out co"><span class="r-pr">#></span> None</span> @@ -244,18 +279,18 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> <span class="r-out co"><span class="r-pr">#></span> Level: ds</span> <span class="r-out co"><span class="r-pr">#></span> Structure: Diagonal</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_parent Residual</span> -<span class="r-out co"><span class="r-pr">#></span> StdDev: 6.904e-05 0.5863 1</span> +<span class="r-out co"><span class="r-pr">#></span> StdDev: 6.924e-05 0.5863 1</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Variance function:</span> <span class="r-out co"><span class="r-pr">#></span> Structure: Constant plus proportion of variance covariate</span> <span class="r-out co"><span class="r-pr">#></span> Formula: ~fitted(.) </span> <span class="r-out co"><span class="r-pr">#></span> Parameter estimates:</span> -<span class="r-out co"><span class="r-pr">#></span> const prop </span> -<span class="r-out co"><span class="r-pr">#></span> 0.000121099 0.078996777 </span> +<span class="r-out co"><span class="r-pr">#></span> const prop </span> +<span class="r-out co"><span class="r-pr">#></span> 0.0001208853 0.0789968036 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Backtransformed parameters with asymmetric confidence intervals:</span> <span class="r-out co"><span class="r-pr">#></span> lower est. upper</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 99.370886 101.59243 103.81398</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 99.370882 101.59243 103.81398</span> <span class="r-out co"><span class="r-pr">#></span> k_parent 0.006923 0.01168 0.01972</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Estimated disappearance times:</span> @@ -266,48 +301,48 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> <span class="r-out co"><span class="r-pr">#></span> ds name time observed predicted residual std standardized</span> <span class="r-out co"><span class="r-pr">#></span> ds 1 parent 0 104.1 101.592 2.50757 8.0255 0.312451</span> <span class="r-out co"><span class="r-pr">#></span> ds 1 parent 0 105.0 101.592 3.40757 8.0255 0.424594</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 1 98.5 100.796 -2.29571 7.9625 -0.288314</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 1 98.5 100.796 -2.29571 7.9625 -0.288313</span> <span class="r-out co"><span class="r-pr">#></span> ds 1 parent 1 96.1 100.796 -4.69571 7.9625 -0.589725</span> <span class="r-out co"><span class="r-pr">#></span> ds 1 parent 3 101.9 99.221 2.67904 7.8381 0.341796</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 3 85.2 99.221 -14.02096 7.8381 -1.788813</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 7 99.1 96.145 2.95512 7.5951 0.389080</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 3 85.2 99.221 -14.02096 7.8381 -1.788812</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 7 99.1 96.145 2.95512 7.5951 0.389081</span> <span class="r-out co"><span class="r-pr">#></span> ds 1 parent 7 93.0 96.145 -3.14488 7.5951 -0.414065</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 14 88.1 90.989 -2.88944 7.1879 -0.401988</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 14 84.1 90.989 -6.88944 7.1879 -0.958481</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 14 88.1 90.989 -2.88944 7.1879 -0.401987</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 14 84.1 90.989 -6.88944 7.1879 -0.958480</span> <span class="r-out co"><span class="r-pr">#></span> ds 1 parent 28 80.2 81.493 -1.29305 6.4377 -0.200857</span> <span class="r-out co"><span class="r-pr">#></span> ds 1 parent 28 91.3 81.493 9.80695 6.4377 1.523364</span> <span class="r-out co"><span class="r-pr">#></span> ds 1 parent 60 65.1 63.344 1.75642 5.0039 0.351008</span> <span class="r-out co"><span class="r-pr">#></span> ds 1 parent 60 65.8 63.344 2.45642 5.0039 0.490898</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 90 47.8 50.018 -2.21764 3.9512 -0.561253</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 90 47.8 50.018 -2.21764 3.9512 -0.561252</span> <span class="r-out co"><span class="r-pr">#></span> ds 1 parent 90 53.5 50.018 3.48236 3.9512 0.881335</span> <span class="r-out co"><span class="r-pr">#></span> ds 1 parent 120 37.6 39.495 -1.89515 3.1200 -0.607423</span> <span class="r-out co"><span class="r-pr">#></span> ds 1 parent 120 39.3 39.495 -0.19515 3.1200 -0.062549</span> <span class="r-out co"><span class="r-pr">#></span> ds 2 parent 0 107.9 101.592 6.30757 8.0255 0.785943</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 0 102.1 101.592 0.50757 8.0255 0.063244</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 0 102.1 101.592 0.50757 8.0255 0.063245</span> <span class="r-out co"><span class="r-pr">#></span> ds 2 parent 1 103.8 100.058 3.74159 7.9043 0.473361</span> <span class="r-out co"><span class="r-pr">#></span> ds 2 parent 1 108.6 100.058 8.54159 7.9043 1.080626</span> <span class="r-out co"><span class="r-pr">#></span> ds 2 parent 3 91.0 97.060 -6.05952 7.6674 -0.790297</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 3 84.9 97.060 -12.15952 7.6674 -1.585875</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 7 79.3 91.329 -12.02867 7.2147 -1.667252</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 3 84.9 97.060 -12.15952 7.6674 -1.585874</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 7 79.3 91.329 -12.02867 7.2147 -1.667251</span> <span class="r-out co"><span class="r-pr">#></span> ds 2 parent 7 100.9 91.329 9.57133 7.2147 1.326647</span> <span class="r-out co"><span class="r-pr">#></span> ds 2 parent 14 77.3 82.102 -4.80185 6.4858 -0.740366</span> <span class="r-out co"><span class="r-pr">#></span> ds 2 parent 14 83.5 82.102 1.39815 6.4858 0.215571</span> <span class="r-out co"><span class="r-pr">#></span> ds 2 parent 28 66.8 66.351 0.44945 5.2415 0.085748</span> <span class="r-out co"><span class="r-pr">#></span> ds 2 parent 28 63.3 66.351 -3.05055 5.2415 -0.582002</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 60 40.8 40.775 0.02474 3.2211 0.007681</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 60 44.8 40.775 4.02474 3.2211 1.249487</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 90 27.8 25.832 1.96762 2.0407 0.964200</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 90 27.0 25.832 1.16762 2.0407 0.572173</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 120 15.2 16.366 -1.16561 1.2928 -0.901593</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 120 15.5 16.366 -0.86561 1.2928 -0.669544</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 0 97.7 101.592 -3.89243 8.0255 -0.485010</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 0 88.2 101.592 -13.39243 8.0255 -1.668740</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 1 109.9 99.218 10.68195 7.8379 1.362858</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 1 97.8 99.218 -1.41805 7.8379 -0.180921</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 60 40.8 40.775 0.02474 3.2211 0.007679</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 60 44.8 40.775 4.02474 3.2211 1.249485</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 90 27.8 25.832 1.96762 2.0407 0.964198</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 90 27.0 25.832 1.16762 2.0407 0.572171</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 120 15.2 16.366 -1.16561 1.2928 -0.901595</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 120 15.5 16.366 -0.86561 1.2928 -0.669547</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 0 97.7 101.592 -3.89243 8.0255 -0.485009</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 0 88.2 101.592 -13.39243 8.0255 -1.668739</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 1 109.9 99.218 10.68196 7.8379 1.362858</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 1 97.8 99.218 -1.41804 7.8379 -0.180921</span> <span class="r-out co"><span class="r-pr">#></span> ds 3 parent 3 100.5 94.634 5.86555 7.4758 0.784603</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 3 77.4 94.634 -17.23445 7.4758 -2.305361</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 7 78.3 86.093 -7.79273 6.8010 -1.145814</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 7 90.3 86.093 4.20727 6.8010 0.618620</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 3 77.4 94.634 -17.23445 7.4758 -2.305360</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 7 78.3 86.093 -7.79273 6.8011 -1.145813</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 7 90.3 86.093 4.20727 6.8011 0.618620</span> <span class="r-out co"><span class="r-pr">#></span> ds 3 parent 14 76.0 72.958 3.04222 5.7634 0.527848</span> <span class="r-out co"><span class="r-pr">#></span> ds 3 parent 14 79.1 72.958 6.14222 5.7634 1.065722</span> <span class="r-out co"><span class="r-pr">#></span> ds 3 parent 28 46.0 52.394 -6.39404 4.1390 -1.544842</span> @@ -315,47 +350,47 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> <span class="r-out co"><span class="r-pr">#></span> ds 3 parent 60 25.1 24.582 0.51786 1.9419 0.266676</span> <span class="r-out co"><span class="r-pr">#></span> ds 3 parent 60 21.4 24.582 -3.18214 1.9419 -1.638664</span> <span class="r-out co"><span class="r-pr">#></span> ds 3 parent 90 11.0 12.092 -1.09202 0.9552 -1.143199</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 90 14.2 12.092 2.10798 0.9552 2.206778</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 90 14.2 12.092 2.10798 0.9552 2.206776</span> <span class="r-out co"><span class="r-pr">#></span> ds 3 parent 120 5.8 5.948 -0.14810 0.4699 -0.315178</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 120 6.1 5.948 0.15190 0.4699 0.323283</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 120 6.1 5.948 0.15190 0.4699 0.323282</span> <span class="r-out co"><span class="r-pr">#></span> ds 4 parent 0 95.3 101.592 -6.29243 8.0255 -0.784057</span> <span class="r-out co"><span class="r-pr">#></span> ds 4 parent 0 102.0 101.592 0.40757 8.0255 0.050784</span> <span class="r-out co"><span class="r-pr">#></span> ds 4 parent 1 104.4 101.125 3.27549 7.9885 0.410025</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 1 105.4 101.125 4.27549 7.9885 0.535204</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 1 105.4 101.125 4.27549 7.9885 0.535205</span> <span class="r-out co"><span class="r-pr">#></span> ds 4 parent 3 113.7 100.195 13.50487 7.9151 1.706218</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 3 82.3 100.195 -17.89513 7.9151 -2.260887</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 7 98.1 98.362 -0.26191 7.7703 -0.033706</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 7 87.8 98.362 -10.56191 7.7703 -1.359271</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 14 97.9 95.234 2.66589 7.5232 0.354356</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 14 104.8 95.234 9.56589 7.5232 1.271521</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 28 85.0 89.274 -4.27373 7.0523 -0.606002</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 28 77.2 89.274 -12.07373 7.0523 -1.712019</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 60 82.2 77.013 5.18659 6.0838 0.852523</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 60 86.1 77.013 9.08659 6.0838 1.493568</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 90 70.5 67.053 3.44690 5.2970 0.650729</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 90 61.7 67.053 -5.35310 5.2970 -1.010595</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 120 60.0 58.381 1.61902 4.6119 0.351052</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 120 56.4 58.381 -1.98098 4.6119 -0.429535</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 0 92.6 101.592 -8.99243 8.0255 -1.120486</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 3 82.3 100.195 -17.89513 7.9151 -2.260886</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 7 98.1 98.362 -0.26190 7.7703 -0.033706</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 7 87.8 98.362 -10.56190 7.7703 -1.359270</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 14 97.9 95.234 2.66590 7.5232 0.354357</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 14 104.8 95.234 9.56590 7.5232 1.271521</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 28 85.0 89.274 -4.27372 7.0523 -0.606001</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 28 77.2 89.274 -12.07372 7.0523 -1.712017</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 60 82.2 77.013 5.18661 6.0838 0.852526</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 60 86.1 77.013 9.08661 6.0838 1.493571</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 90 70.5 67.053 3.44692 5.2970 0.650733</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 90 61.7 67.053 -5.35308 5.2970 -1.010591</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 120 60.0 58.381 1.61905 4.6119 0.351058</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 120 56.4 58.381 -1.98095 4.6119 -0.429530</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 0 92.6 101.592 -8.99243 8.0255 -1.120485</span> <span class="r-out co"><span class="r-pr">#></span> ds 5 parent 0 116.5 101.592 14.90757 8.0255 1.857531</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 1 108.0 99.914 8.08560 7.8929 1.024412</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 1 108.0 99.914 8.08560 7.8929 1.024413</span> <span class="r-out co"><span class="r-pr">#></span> ds 5 parent 1 104.9 99.914 4.98560 7.8929 0.631655</span> <span class="r-out co"><span class="r-pr">#></span> ds 5 parent 3 100.5 96.641 3.85898 7.6343 0.505477</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 3 89.5 96.641 -7.14102 7.6343 -0.935383</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 3 89.5 96.641 -7.14102 7.6343 -0.935382</span> <span class="r-out co"><span class="r-pr">#></span> ds 5 parent 7 91.7 90.412 1.28752 7.1423 0.180267</span> <span class="r-out co"><span class="r-pr">#></span> ds 5 parent 7 95.1 90.412 4.68752 7.1423 0.656304</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 14 82.2 80.463 1.73715 6.3563 0.273296</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 14 82.2 80.463 1.73715 6.3563 0.273295</span> <span class="r-out co"><span class="r-pr">#></span> ds 5 parent 14 84.5 80.463 4.03715 6.3563 0.635141</span> <span class="r-out co"><span class="r-pr">#></span> ds 5 parent 28 60.5 63.728 -3.22788 5.0343 -0.641178</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 28 72.8 63.728 9.07212 5.0343 1.802063</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 60 38.3 37.399 0.90062 2.9544 0.304836</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 60 40.7 37.399 3.30062 2.9544 1.117176</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 90 22.5 22.692 -0.19164 1.7926 -0.106910</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 90 20.8 22.692 -1.89164 1.7926 -1.055271</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 120 13.4 13.768 -0.36789 1.0876 -0.338255</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 120 13.8 13.768 0.03211 1.0876 0.029520</span> -<span class="r-in"><span class="co"># }</span></span> -<span class="r-in"></span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 28 72.8 63.728 9.07212 5.0343 1.802062</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 60 38.3 37.399 0.90061 2.9544 0.304835</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 60 40.7 37.399 3.30061 2.9544 1.117174</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 90 22.5 22.692 -0.19165 1.7926 -0.106913</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 90 20.8 22.692 -1.89165 1.7926 -1.055273</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 120 13.4 13.768 -0.36790 1.0876 -0.338259</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 120 13.8 13.768 0.03210 1.0876 0.029517</span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -370,7 +405,7 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/summary.saem.mmkin.html b/docs/reference/summary.saem.mmkin.html index 0ab239cb..dfa0b776 100644 --- a/docs/reference/summary.saem.mmkin.html +++ b/docs/reference/summary.saem.mmkin.html @@ -21,7 +21,7 @@ endpoints such as formation fractions and DT50 values. Optionally </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -48,19 +48,25 @@ endpoints such as formation fractions and DT50 values. Optionally <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -96,7 +102,7 @@ endpoints such as formation fractions and DT50 values. Optionally <div id="ref-usage"> <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for saem.mmkin</span></span> -<span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, data <span class="op">=</span> <span class="cn">FALSE</span>, verbose <span class="op">=</span> <span class="cn">FALSE</span>, distimes <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span> +<span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">object</span>, data <span class="op">=</span> <span class="cn">FALSE</span>, verbose <span class="op">=</span> <span class="cn">FALSE</span>, distimes <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span> <span></span> <span><span class="co"># S3 method for summary.saem.mmkin</span></span> <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, verbose <span class="op">=</span> <span class="va">x</span><span class="op">$</span><span class="va">verbose</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> @@ -240,57 +246,56 @@ saemix authors for the parts inherited from saemix.</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Likelihood computed by importance sampling</span> <span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> -<span class="r-out co"><span class="r-pr">#></span> 828.1 822.7 -400.1</span> +<span class="r-out co"><span class="r-pr">#></span> 810.8 805.4 -391.4</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Fitted parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> estimate lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 100.74378 97.81291 103.67465</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_m1 -4.06168 -4.17104 -3.95231</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.92584 -1.31273 -0.53894</span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.81914 -3.60206 -2.03623</span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 -3.63916 -4.32672 -2.95161</span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.02927 -1.15247 1.09394</span> -<span class="r-out co"><span class="r-pr">#></span> a.1 0.86164 0.67928 1.04400</span> -<span class="r-out co"><span class="r-pr">#></span> b.1 0.07973 0.06437 0.09509</span> -<span class="r-out co"><span class="r-pr">#></span> SD.parent_0 0.73313 -7.46512 8.93137</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k_m1 0.06488 -0.06041 0.19017</span> -<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.41955 0.15206 0.68705</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 0.81750 0.29140 1.34361</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 0.75265 0.27939 1.22590</span> -<span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.34411 -1.70964 2.39786</span> +<span class="r-out co"><span class="r-pr">#></span> estimate lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 100.86947 97.81542 103.92353</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_m1 -4.06947 -4.16944 -3.96950</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.93256 -1.34200 -0.52312</span> +<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.37017 -2.72660 -2.01375</span> +<span class="r-out co"><span class="r-pr">#></span> log_k2 -4.06264 -4.21344 -3.91184</span> +<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.02174 -0.45898 0.41549</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 0.87598 0.67275 1.07922</span> +<span class="r-out co"><span class="r-pr">#></span> b.1 0.07949 0.06389 0.09509</span> +<span class="r-out co"><span class="r-pr">#></span> SD.parent_0 0.19170 -30.36286 30.74626</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_m1 0.01883 -0.28736 0.32502</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.44300 0.16391 0.72209</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 0.35320 0.09661 0.60978</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 0.13707 0.02359 0.25056</span> +<span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.37478 0.04490 0.70467</span> <span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> [1] "sd(parent_0)" "sd(log_k_m1)" "sd(g_qlogis)"</span> +<span class="r-out co"><span class="r-pr">#></span> [1] "sd(parent_0)" "sd(log_k_m1)"</span> <span class="r-in"><span><span class="va">f_saem_dfop_sfo_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>, covariance.model <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/diag.html" class="external-link">diag</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo_2</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> character(0)</span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/intervals.html" class="external-link">intervals</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo_2</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Approximate 95% confidence intervals</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Fixed effects:</span> <span class="r-out co"><span class="r-pr">#></span> lower est. upper</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 97.54844979 100.46239264 103.37633550</span> -<span class="r-out co"><span class="r-pr">#></span> k_m1 0.01575805 0.01729111 0.01897331</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.21014925 0.28626877 0.37680664</span> -<span class="r-out co"><span class="r-pr">#></span> k1 0.02651112 0.05601399 0.11834908</span> -<span class="r-out co"><span class="r-pr">#></span> k2 0.01326524 0.02649799 0.05293107</span> -<span class="r-out co"><span class="r-pr">#></span> g 0.31467778 0.51297098 0.70726363</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 97.57609542 100.73343868 103.89078195</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 0.01549292 0.01714893 0.01898194</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.20720315 0.28358738 0.37481744</span> +<span class="r-out co"><span class="r-pr">#></span> k1 0.06149334 0.08733164 0.12402670</span> +<span class="r-out co"><span class="r-pr">#></span> k2 0.01448390 0.01699942 0.01995184</span> +<span class="r-out co"><span class="r-pr">#></span> g 0.45084762 0.51075839 0.57036168</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Random effects:</span> -<span class="r-out co"><span class="r-pr">#></span> lower est. upper</span> -<span class="r-out co"><span class="r-pr">#></span> sd(f_parent_qlogis) 0.1658367 0.4471180 0.7283993</span> -<span class="r-out co"><span class="r-pr">#></span> sd(log_k1) 0.2768757 0.7929203 1.3089649</span> -<span class="r-out co"><span class="r-pr">#></span> sd(log_k2) 0.2693629 0.7566116 1.2438602</span> +<span class="r-out co"><span class="r-pr">#></span> lower est. upper</span> +<span class="r-out co"><span class="r-pr">#></span> sd(f_parent_qlogis) 0.16606767 0.4479731 0.7298784</span> +<span class="r-out co"><span class="r-pr">#></span> sd(log_k1) 0.12284609 0.3588446 0.5948430</span> +<span class="r-out co"><span class="r-pr">#></span> sd(log_k2) 0.05379723 0.1548780 0.2559588</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> lower est. upper</span> -<span class="r-out co"><span class="r-pr">#></span> a.1 0.70273100 0.88750764 1.07228428</span> -<span class="r-out co"><span class="r-pr">#></span> b.1 0.06781347 0.08328016 0.09874685</span> -<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo_2</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> saemix version used for fitting: 3.1 </span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.1.2 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.1 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Wed Aug 10 14:31:01 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Wed Aug 10 14:31:01 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> lower est. upper</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 0.6811490 0.88503409 1.08891921</span> +<span class="r-out co"><span class="r-pr">#></span> b.1 0.0676515 0.08336272 0.09907394</span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo_2</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> saemix version used for fitting: 3.2 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.2.0 </span> +<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Nov 17 14:04:08 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Nov 17 14:04:08 2022 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span> @@ -305,7 +310,7 @@ saemix authors for the parts inherited from saemix.</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type analytical </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted in 26.786 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted in 26.014 s</span> <span class="r-out co"><span class="r-pr">#></span> Using 300, 100 iterations and 10 chains</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Variance model: Two-component variance function </span> @@ -323,228 +328,233 @@ saemix authors for the parts inherited from saemix.</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Likelihood computed by importance sampling</span> <span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> -<span class="r-out co"><span class="r-pr">#></span> 825.6 821.3 -401.8</span> +<span class="r-out co"><span class="r-pr">#></span> 809.5 805.2 -393.7</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Optimised parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 100.4624 97.5484 103.3763</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_m1 -4.0576 -4.1504 -3.9647</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.9136 -1.3240 -0.5031</span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.8822 -3.6302 -2.1341</span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 -3.6307 -4.3226 -2.9388</span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis 0.0519 -0.7783 0.8821</span> +<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 100.73344 97.57610 103.89078</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_m1 -4.06582 -4.16737 -3.96427</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.92674 -1.34187 -0.51160</span> +<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.43804 -2.78883 -2.08726</span> +<span class="r-out co"><span class="r-pr">#></span> log_k2 -4.07458 -4.23472 -3.91443</span> +<span class="r-out co"><span class="r-pr">#></span> g_qlogis 0.04304 -0.19725 0.28333</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 0.88503 0.68115 1.08892</span> +<span class="r-out co"><span class="r-pr">#></span> b.1 0.08336 0.06765 0.09907</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.44797 0.16607 0.72988</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 0.35884 0.12285 0.59484</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 0.15488 0.05380 0.25596</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Correlation: </span> <span class="r-out co"><span class="r-pr">#></span> parnt_0 lg_k_m1 f_prnt_ log_k1 log_k2 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_m1 -0.4102 </span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.2113 0.2439 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 0.1308 -0.1305 -0.0504 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 -0.0383 0.0592 0.0151 0.0001 </span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.0029 -0.0118 0.0131 -0.2547 -0.1942</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_m1 -0.4698 </span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.2461 0.2709 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k1 0.1572 -0.1517 -0.0648 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k2 -0.0023 0.0835 0.0125 0.1420 </span> +<span class="r-out co"><span class="r-pr">#></span> g_qlogis 0.2314 -0.2337 -0.0755 -0.2762 -0.4797</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Random effects:</span> <span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.4471 0.1658 0.7284</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 0.7929 0.2769 1.3090</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 0.7566 0.2694 1.2439</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.4480 0.1661 0.7299</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 0.3588 0.1228 0.5948</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 0.1549 0.0538 0.2560</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Variance model:</span> <span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> a.1 0.88751 0.70273 1.07228</span> -<span class="r-out co"><span class="r-pr">#></span> b.1 0.08328 0.06781 0.09875</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 0.88503 0.68115 1.08892</span> +<span class="r-out co"><span class="r-pr">#></span> b.1 0.08336 0.06765 0.09907</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Backtransformed parameters:</span> <span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 100.46239 97.54845 103.37634</span> -<span class="r-out co"><span class="r-pr">#></span> k_m1 0.01729 0.01576 0.01897</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.28627 0.21015 0.37681</span> -<span class="r-out co"><span class="r-pr">#></span> k1 0.05601 0.02651 0.11835</span> -<span class="r-out co"><span class="r-pr">#></span> k2 0.02650 0.01327 0.05293</span> -<span class="r-out co"><span class="r-pr">#></span> g 0.51297 0.31468 0.70726</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 100.73344 97.57610 103.89078</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 0.01715 0.01549 0.01898</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.28359 0.20720 0.37482</span> +<span class="r-out co"><span class="r-pr">#></span> k1 0.08733 0.06149 0.12403</span> +<span class="r-out co"><span class="r-pr">#></span> k2 0.01700 0.01448 0.01995</span> +<span class="r-out co"><span class="r-pr">#></span> g 0.51076 0.45085 0.57036</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Resulting formation fractions:</span> <span class="r-out co"><span class="r-pr">#></span> ff</span> -<span class="r-out co"><span class="r-pr">#></span> parent_m1 0.2863</span> -<span class="r-out co"><span class="r-pr">#></span> parent_sink 0.7137</span> +<span class="r-out co"><span class="r-pr">#></span> parent_m1 0.2836</span> +<span class="r-out co"><span class="r-pr">#></span> parent_sink 0.7164</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Estimated disappearance times:</span> <span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_k1 DT50_k2</span> -<span class="r-out co"><span class="r-pr">#></span> parent 17.44 65.15 19.61 12.37 26.16</span> -<span class="r-out co"><span class="r-pr">#></span> m1 40.09 133.17 NA NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> parent 15.94 93.48 28.14 7.937 40.77</span> +<span class="r-out co"><span class="r-pr">#></span> m1 40.42 134.27 NA NA NA</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Data:</span> -<span class="r-out co"><span class="r-pr">#></span> ds name time observed predicted residual std standardized</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 0 89.8 1.005e+02 -10.662393 8.4135 -1.267301</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 0 104.1 1.005e+02 3.637607 8.4135 0.432355</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 1 88.7 9.576e+01 -7.063498 8.0244 -0.880249</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 1 95.5 9.576e+01 -0.263498 8.0244 -0.032837</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 3 81.8 8.717e+01 -5.369491 7.3135 -0.734185</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 3 94.5 8.717e+01 7.330509 7.3135 1.002320</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 7 71.5 7.274e+01 -1.238672 6.1224 -0.202319</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 7 70.3 7.274e+01 -2.438672 6.1224 -0.398322</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 14 54.2 5.418e+01 0.022691 4.5984 0.004935</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 14 49.6 5.418e+01 -4.577309 4.5984 -0.995423</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 28 31.5 3.241e+01 -0.914545 2.8416 -0.321837</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 28 28.8 3.241e+01 -3.614545 2.8416 -1.271993</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 60 12.1 1.283e+01 -0.730904 1.3891 -0.526186</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 60 13.6 1.283e+01 0.769096 1.3891 0.553681</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 90 6.2 6.128e+00 0.071981 1.0238 0.070309</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 90 8.3 6.128e+00 2.171981 1.0238 2.121538</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 120 2.2 3.022e+00 -0.822164 0.9225 -0.891230</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 120 2.4 3.022e+00 -0.622164 0.9225 -0.674429</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 1 0.3 1.163e+00 -0.863423 0.8928 -0.967116</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 1 0.2 1.163e+00 -0.963423 0.8928 -1.079126</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 3 2.2 3.233e+00 -1.032930 0.9274 -1.113734</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 3 3.0 3.233e+00 -0.232930 0.9274 -0.251152</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 7 6.5 6.495e+00 0.005314 1.0393 0.005113</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 7 5.0 6.495e+00 -1.494686 1.0393 -1.438116</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 14 10.2 1.010e+01 0.096372 1.2230 0.078801</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 14 9.5 1.010e+01 -0.603628 1.2230 -0.493572</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 28 12.2 1.269e+01 -0.492073 1.3802 -0.356526</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 28 13.4 1.269e+01 0.707927 1.3802 0.512922</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 60 11.8 1.086e+01 0.944360 1.2669 0.745420</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 60 13.2 1.086e+01 2.344360 1.2669 1.850494</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 90 6.6 7.723e+00 -1.123088 1.0961 -1.024658</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 90 9.3 7.723e+00 1.576912 1.0961 1.438708</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 120 3.5 5.184e+00 -1.683936 0.9869 -1.706219</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 120 5.4 5.184e+00 0.216064 0.9869 0.218923</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 0 118.0 1.005e+02 17.537607 8.4135 2.084469</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 0 99.8 1.005e+02 -0.662393 8.4135 -0.078730</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 1 90.2 9.566e+01 -5.456414 8.0156 -0.680727</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 1 94.6 9.566e+01 -1.056414 8.0156 -0.131795</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 3 96.1 8.702e+01 9.082833 7.3009 1.244062</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 3 78.4 8.702e+01 -8.617167 7.3009 -1.180281</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 7 77.9 7.298e+01 4.919834 6.1423 0.800981</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 7 77.7 7.298e+01 4.719834 6.1423 0.768420</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 14 56.0 5.588e+01 0.124003 4.7372 0.026176</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 14 54.7 5.588e+01 -1.175997 4.7372 -0.248245</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 28 36.6 3.719e+01 -0.587869 3.2217 -0.182474</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 28 36.8 3.719e+01 -0.387869 3.2217 -0.120394</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 60 22.1 2.013e+01 1.973728 1.8966 1.040673</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 60 24.7 2.013e+01 4.573728 1.8966 2.411556</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 90 12.4 1.259e+01 -0.185933 1.3734 -0.135379</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 90 10.8 1.259e+01 -1.785933 1.3734 -1.300347</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 120 6.8 7.981e+00 -1.180542 1.1088 -1.064723</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 120 7.9 7.981e+00 -0.080542 1.1088 -0.072640</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 1 1.3 1.306e+00 -0.006246 0.8941 -0.006986</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 3 3.7 3.589e+00 0.110879 0.9365 0.118399</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 3 4.7 3.589e+00 1.110879 0.9365 1.186217</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 7 8.1 7.062e+00 1.038045 1.0647 0.974978</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 7 7.9 7.062e+00 0.838045 1.0647 0.787129</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 14 10.1 1.065e+01 -0.553713 1.2549 -0.441227</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 14 10.3 1.065e+01 -0.353713 1.2549 -0.281857</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 28 10.7 1.284e+01 -2.144854 1.3900 -1.543111</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 28 12.2 1.284e+01 -0.644854 1.3900 -0.463939</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 60 10.7 1.082e+01 -0.115278 1.2645 -0.091165</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 60 12.5 1.082e+01 1.684722 1.2645 1.332337</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 90 9.1 8.014e+00 1.085607 1.1105 0.977610</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 90 7.4 8.014e+00 -0.614393 1.1105 -0.553272</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 120 6.1 5.736e+00 0.363593 1.0079 0.360737</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 120 4.5 5.736e+00 -1.236407 1.0079 -1.226697</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 0 106.2 1.005e+02 5.737607 8.4135 0.681955</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 0 106.9 1.005e+02 6.437607 8.4135 0.765155</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 1 107.4 9.343e+01 13.972212 7.8311 1.784188</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 1 96.1 9.343e+01 2.672212 7.8311 0.341229</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 3 79.4 8.160e+01 -2.196297 6.8531 -0.320484</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 3 82.6 8.160e+01 1.003703 6.8531 0.146460</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 7 63.9 6.464e+01 -0.737220 5.4557 -0.135129</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 7 62.4 6.464e+01 -2.237220 5.4557 -0.410072</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 14 51.0 4.772e+01 3.278433 4.0722 0.805086</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 14 47.1 4.772e+01 -0.621567 4.0722 -0.152638</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 28 36.1 3.303e+01 3.070676 2.8903 1.062400</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 28 36.6 3.303e+01 3.570676 2.8903 1.235391</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 60 20.1 1.929e+01 0.808039 1.8355 0.440235</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 60 19.8 1.929e+01 0.508039 1.8355 0.276789</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 90 11.3 1.209e+01 -0.794443 1.3425 -0.591785</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 90 10.7 1.209e+01 -1.394443 1.3425 -1.038728</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 120 8.2 7.590e+00 0.610002 1.0896 0.559843</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 120 7.3 7.590e+00 -0.289998 1.0896 -0.266152</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 0 0.8 -4.263e-14 0.800000 0.8875 0.901401</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 1 1.8 1.692e+00 0.107665 0.8986 0.119811</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 1 2.3 1.692e+00 0.607665 0.8986 0.676214</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 3 4.2 4.455e+00 -0.255347 0.9619 -0.265449</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 3 4.1 4.455e+00 -0.355347 0.9619 -0.369404</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 7 6.8 8.124e+00 -1.324338 1.1160 -1.186685</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 7 10.1 8.124e+00 1.975662 1.1160 1.770309</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 14 11.4 1.104e+01 0.361860 1.2778 0.283196</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 14 12.8 1.104e+01 1.761860 1.2778 1.378852</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 28 11.5 1.177e+01 -0.272554 1.3225 -0.206097</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 28 10.6 1.177e+01 -1.172554 1.3225 -0.886648</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 60 7.5 9.242e+00 -1.741667 1.1747 -1.482591</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 60 8.6 9.242e+00 -0.641667 1.1747 -0.546218</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 90 7.3 6.837e+00 0.463318 1.0544 0.439398</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 90 8.1 6.837e+00 1.263318 1.0544 1.198095</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 120 5.3 4.906e+00 0.394322 0.9770 0.403595</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 120 3.8 4.906e+00 -1.105678 0.9770 -1.131677</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 0 104.7 1.005e+02 4.237607 8.4135 0.503670</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 0 88.3 1.005e+02 -12.162393 8.4135 -1.445587</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 1 94.2 9.723e+01 -3.029220 8.1458 -0.371877</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 1 94.6 9.723e+01 -2.629220 8.1458 -0.322772</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 3 78.1 9.114e+01 -13.041804 7.6420 -1.706592</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 3 96.5 9.114e+01 5.358196 7.6420 0.701150</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 7 76.2 8.033e+01 -4.133084 6.7488 -0.612421</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 7 77.8 8.033e+01 -2.533084 6.7488 -0.375340</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 14 70.8 6.504e+01 5.757987 5.4889 1.049017</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 14 67.3 6.504e+01 2.257987 5.4889 0.411371</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 28 43.1 4.418e+01 -1.080806 3.7849 -0.285557</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 28 45.1 4.418e+01 0.919194 3.7849 0.242858</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 60 21.3 2.110e+01 0.200596 1.9686 0.101899</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 60 23.5 2.110e+01 2.400596 1.9686 1.219459</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 90 11.8 1.183e+01 -0.034206 1.3263 -0.025791</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 90 12.1 1.183e+01 0.265794 1.3263 0.200408</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 120 7.0 6.985e+00 0.014647 1.0612 0.013803</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 120 6.2 6.985e+00 -0.785353 1.0612 -0.740078</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 0 1.6 -1.705e-13 1.600000 0.8875 1.802801</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 1 0.9 6.803e-01 0.219655 0.8893 0.246994</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 3 3.7 1.927e+00 1.773027 0.9019 1.965880</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 3 2.0 1.927e+00 0.073027 0.9019 0.080970</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 7 3.6 4.013e+00 -0.412926 0.9483 -0.435417</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 7 3.8 4.013e+00 -0.212926 0.9483 -0.224523</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 14 7.1 6.604e+00 0.495843 1.0441 0.474896</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 14 6.6 6.604e+00 -0.004157 1.0441 -0.003981</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 28 9.5 9.077e+00 0.422700 1.1658 0.362576</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 28 9.3 9.077e+00 0.222700 1.1658 0.191024</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 60 8.3 8.818e+00 -0.518498 1.1520 -0.450099</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 60 9.0 8.818e+00 0.181502 1.1520 0.157559</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 90 6.6 6.738e+00 -0.137785 1.0500 -0.131222</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 90 7.7 6.738e+00 0.962215 1.0500 0.916383</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 120 3.7 4.794e+00 -1.093754 0.9732 -1.123914</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 120 3.5 4.794e+00 -1.293754 0.9732 -1.329429</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 0 110.4 1.005e+02 9.937607 8.4135 1.181155</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 0 112.1 1.005e+02 11.637607 8.4135 1.383212</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 1 93.5 9.372e+01 -0.215694 7.8550 -0.027460</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 1 91.0 9.372e+01 -2.715694 7.8550 -0.345730</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 3 71.0 8.226e+01 -11.257156 6.9076 -1.629667</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 3 89.7 8.226e+01 7.442844 6.9076 1.077480</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 7 60.4 6.553e+01 -5.128464 5.5289 -0.927571</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 7 59.1 6.553e+01 -6.428464 5.5289 -1.162699</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 14 56.5 4.835e+01 8.146351 4.1235 1.975572</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 14 47.0 4.835e+01 -1.353649 4.1235 -0.328273</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 28 30.2 3.300e+01 -2.803303 2.8883 -0.970586</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 28 23.9 3.300e+01 -9.103303 2.8883 -3.151832</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 60 17.0 1.891e+01 -1.905909 1.8074 -1.054506</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 60 18.7 1.891e+01 -0.205909 1.8074 -0.113926</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 90 11.3 1.172e+01 -0.423434 1.3194 -0.320923</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 90 11.9 1.172e+01 0.176566 1.3194 0.133820</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 120 9.0 7.281e+00 1.719138 1.0749 1.599402</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 120 8.1 7.281e+00 0.819138 1.0749 0.762086</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 0 0.7 -2.842e-13 0.700000 0.8875 0.788726</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 1 3.0 3.252e+00 -0.252227 0.9279 -0.271821</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 1 2.6 3.252e+00 -0.652227 0.9279 -0.702895</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 3 5.1 8.615e+00 -3.515326 1.1413 -3.080237</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 3 7.5 8.615e+00 -1.115326 1.1413 -0.977283</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 7 16.5 1.588e+01 0.619041 1.5928 0.388661</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 7 19.0 1.588e+01 3.119041 1.5928 1.958272</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 14 22.9 2.189e+01 1.014705 2.0272 0.500543</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 14 23.2 2.189e+01 1.314705 2.0272 0.648529</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 28 22.2 2.369e+01 -1.487604 2.1632 -0.687701</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 28 24.4 2.369e+01 0.712396 2.1632 0.329332</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 60 15.5 1.869e+01 -3.193942 1.7920 -1.782295</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 60 19.8 1.869e+01 1.106058 1.7920 0.617206</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 90 14.9 1.380e+01 1.103454 1.4518 0.760041</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 90 14.2 1.380e+01 0.403454 1.4518 0.277892</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 120 10.9 9.864e+00 1.035963 1.2093 0.856637</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 120 10.4 9.864e+00 0.535963 1.2093 0.443187</span> +<span class="r-out co"><span class="r-pr">#></span> ds name time observed predicted residual std standardized</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 0 89.8 1.007e+02 -10.93344 8.4439 -1.29483</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 0 104.1 1.007e+02 3.36656 8.4439 0.39870</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 1 88.7 9.591e+01 -7.20789 8.0440 -0.89606</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 1 95.5 9.591e+01 -0.40789 8.0440 -0.05071</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 3 81.8 8.712e+01 -5.31561 7.3159 -0.72658</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 3 94.5 8.712e+01 7.38439 7.3159 1.00936</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 7 71.5 7.246e+01 -0.95675 6.1047 -0.15672</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 7 70.3 7.246e+01 -2.15675 6.1047 -0.35329</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 14 54.2 5.382e+01 0.38143 4.5729 0.08341</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 14 49.6 5.382e+01 -4.21857 4.5729 -0.92251</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 28 31.5 3.230e+01 -0.80120 2.8344 -0.28267</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 28 28.8 3.230e+01 -3.50120 2.8344 -1.23524</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 60 12.1 1.307e+01 -0.97165 1.4038 -0.69215</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 60 13.6 1.307e+01 0.52835 1.4038 0.37637</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 90 6.2 6.353e+00 -0.15285 1.0314 -0.14820</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 90 8.3 6.353e+00 1.94715 1.0314 1.88790</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 120 2.2 3.175e+00 -0.97462 0.9238 -1.05506</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 120 2.4 3.175e+00 -0.77462 0.9238 -0.83855</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 1 0.3 1.183e+00 -0.88350 0.8905 -0.99212</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 1 0.2 1.183e+00 -0.98350 0.8905 -1.10441</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 3 2.2 3.281e+00 -1.08106 0.9263 -1.16703</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 3 3.0 3.281e+00 -0.28106 0.9263 -0.30341</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 7 6.5 6.564e+00 -0.06353 1.0405 -0.06106</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 7 5.0 6.564e+00 -1.56353 1.0405 -1.50266</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 14 10.2 1.015e+01 0.05147 1.2243 0.04204</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 14 9.5 1.015e+01 -0.64853 1.2243 -0.52970</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 28 12.2 1.265e+01 -0.44824 1.3766 -0.32561</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 28 13.4 1.265e+01 0.75176 1.3766 0.54610</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 60 11.8 1.078e+01 1.02355 1.2611 0.81165</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 60 13.2 1.078e+01 2.42355 1.2611 1.92181</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 90 6.6 7.698e+00 -1.09840 1.0932 -1.00474</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 90 9.3 7.698e+00 1.60160 1.0932 1.46502</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 120 3.5 5.199e+00 -1.69853 0.9854 -1.72363</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 120 5.4 5.199e+00 0.20147 0.9854 0.20445</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 0 118.0 1.007e+02 17.26656 8.4439 2.04485</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 0 99.8 1.007e+02 -0.93344 8.4439 -0.11055</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 1 90.2 9.584e+01 -5.63852 8.0382 -0.70146</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 1 94.6 9.584e+01 -1.23852 8.0382 -0.15408</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 3 96.1 8.706e+01 9.04068 7.3113 1.23654</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 3 78.4 8.706e+01 -8.65932 7.3113 -1.18438</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 7 77.9 7.286e+01 5.04438 6.1376 0.82188</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 7 77.7 7.286e+01 4.84438 6.1376 0.78930</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 14 56.0 5.567e+01 0.33336 4.7242 0.07057</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 14 54.7 5.567e+01 -0.96664 4.7242 -0.20462</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 28 36.6 3.705e+01 -0.44800 3.2127 -0.13944</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 28 36.8 3.705e+01 -0.24800 3.2127 -0.07719</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 60 22.1 2.008e+01 2.01984 1.8935 1.06672</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 60 24.7 2.008e+01 4.61984 1.8935 2.43984</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 90 12.4 1.253e+01 -0.12814 1.3689 -0.09360</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 90 10.8 1.253e+01 -1.72814 1.3689 -1.26238</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 120 6.8 7.916e+00 -1.11595 1.1040 -1.01085</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 120 7.9 7.916e+00 -0.01595 1.1040 -0.01445</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 1 1.3 1.317e+00 -0.01669 0.8918 -0.01871</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 3 3.7 3.613e+00 0.08699 0.9349 0.09305</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 3 4.7 3.613e+00 1.08699 0.9349 1.16270</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 7 8.1 7.092e+00 1.00781 1.0643 0.94688</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 7 7.9 7.092e+00 0.80781 1.0643 0.75897</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 14 10.1 1.066e+01 -0.56458 1.2545 -0.45006</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 14 10.3 1.066e+01 -0.36458 1.2545 -0.29063</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 28 10.7 1.281e+01 -2.11106 1.3870 -1.52201</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 28 12.2 1.281e+01 -0.61106 1.3870 -0.44055</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 60 10.7 1.078e+01 -0.08464 1.2616 -0.06709</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 60 12.5 1.078e+01 1.71536 1.2616 1.35970</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 90 9.1 8.013e+00 1.08684 1.1088 0.98016</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 90 7.4 8.013e+00 -0.61316 1.1088 -0.55298</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 120 6.1 5.749e+00 0.35063 1.0065 0.34838</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 120 4.5 5.749e+00 -1.24937 1.0065 -1.24133</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 0 106.2 1.007e+02 5.46656 8.4439 0.64740</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 0 106.9 1.007e+02 6.16656 8.4439 0.73030</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 1 107.4 9.369e+01 13.70530 7.8606 1.74354</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 1 96.1 9.369e+01 2.40530 7.8606 0.30599</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 3 79.4 8.185e+01 -2.45363 6.8807 -0.35660</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 3 82.6 8.185e+01 0.74637 6.8807 0.10847</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 7 63.9 6.487e+01 -0.97153 5.4798 -0.17729</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 7 62.4 6.487e+01 -2.47153 5.4798 -0.45103</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 14 51.0 4.791e+01 3.09024 4.0908 0.75542</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 14 47.1 4.791e+01 -0.80976 4.0908 -0.19795</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 28 36.1 3.313e+01 2.97112 2.9001 1.02450</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 28 36.6 3.313e+01 3.47112 2.9001 1.19691</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 60 20.1 1.927e+01 0.83265 1.8339 0.45404</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 60 19.8 1.927e+01 0.53265 1.8339 0.29045</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 90 11.3 1.203e+01 -0.72783 1.3374 -0.54421</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 90 10.7 1.203e+01 -1.32783 1.3374 -0.99284</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 120 8.2 7.516e+00 0.68382 1.0844 0.63061</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 120 7.3 7.516e+00 -0.21618 1.0844 -0.19936</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 0 0.8 -9.948e-14 0.80000 0.8850 0.90392</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 1 1.8 1.682e+00 0.11759 0.8961 0.13123</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 1 2.3 1.682e+00 0.61759 0.8961 0.68921</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 3 4.2 4.431e+00 -0.23052 0.9590 -0.24037</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 3 4.1 4.431e+00 -0.33052 0.9590 -0.34465</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 7 6.8 8.084e+00 -1.28422 1.1124 -1.15445</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 7 10.1 8.084e+00 2.01578 1.1124 1.81208</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 14 11.4 1.100e+01 0.40274 1.2743 0.31606</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 14 12.8 1.100e+01 1.80274 1.2743 1.41474</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 28 11.5 1.176e+01 -0.25977 1.3207 -0.19669</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 28 10.6 1.176e+01 -1.15977 1.3207 -0.87813</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 60 7.5 9.277e+00 -1.77696 1.1753 -1.51190</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 60 8.6 9.277e+00 -0.67696 1.1753 -0.57598</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 90 7.3 6.883e+00 0.41708 1.0548 0.39542</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 90 8.1 6.883e+00 1.21708 1.0548 1.15389</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 120 5.3 4.948e+00 0.35179 0.9764 0.36028</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 120 3.8 4.948e+00 -1.14821 0.9764 -1.17591</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 0 104.7 1.007e+02 3.96656 8.4439 0.46975</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 0 88.3 1.007e+02 -12.43344 8.4439 -1.47247</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 1 94.2 9.738e+01 -3.18358 8.1663 -0.38985</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 1 94.6 9.738e+01 -2.78358 8.1663 -0.34086</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 3 78.1 9.110e+01 -12.99595 7.6454 -1.69984</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 3 96.5 9.110e+01 5.40405 7.6454 0.70684</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 7 76.2 8.000e+01 -3.79797 6.7273 -0.56456</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 7 77.8 8.000e+01 -2.19797 6.7273 -0.32672</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 14 70.8 6.446e+01 6.34396 5.4456 1.16496</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 14 67.3 6.446e+01 2.84396 5.4456 0.52225</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 28 43.1 4.359e+01 -0.48960 3.7400 -0.13091</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 28 45.1 4.359e+01 1.51040 3.7400 0.40385</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 60 21.3 2.095e+01 0.35282 1.9577 0.18022</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 60 23.5 2.095e+01 2.55282 1.9577 1.30400</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 90 11.8 1.188e+01 -0.07874 1.3281 -0.05929</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 90 12.1 1.188e+01 0.22126 1.3281 0.16660</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 120 7.0 7.072e+00 -0.07245 1.0634 -0.06813</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 120 6.2 7.072e+00 -0.87245 1.0634 -0.82041</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 0 1.6 5.684e-14 1.60000 0.8850 1.80784</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 1 0.9 6.960e-01 0.20399 0.8869 0.23000</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 3 3.7 1.968e+00 1.73240 0.9001 1.92466</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 3 2.0 1.968e+00 0.03240 0.9001 0.03599</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 7 3.6 4.083e+00 -0.48287 0.9482 -0.50924</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 7 3.8 4.083e+00 -0.28287 0.9482 -0.29832</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 14 7.1 6.682e+00 0.41836 1.0457 0.40007</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 14 6.6 6.682e+00 -0.08164 1.0457 -0.07807</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 28 9.5 9.103e+00 0.39733 1.1658 0.34082</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 28 9.3 9.103e+00 0.19733 1.1658 0.16926</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 60 8.3 8.750e+00 -0.44979 1.1469 -0.39218</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 60 9.0 8.750e+00 0.25021 1.1469 0.21817</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 90 6.6 6.673e+00 -0.07285 1.0453 -0.06969</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 90 7.7 6.673e+00 1.02715 1.0453 0.98261</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 120 3.7 4.757e+00 -1.05747 0.9698 -1.09036</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 120 3.5 4.757e+00 -1.25747 0.9698 -1.29658</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 0 110.4 1.007e+02 9.66656 8.4439 1.14480</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 0 112.1 1.007e+02 11.36656 8.4439 1.34612</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 1 93.5 9.395e+01 -0.45394 7.8821 -0.05759</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 1 91.0 9.395e+01 -2.95394 7.8821 -0.37477</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 3 71.0 8.245e+01 -11.44783 6.9298 -1.65197</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 3 89.7 8.245e+01 7.25217 6.9298 1.04652</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 7 60.4 6.567e+01 -5.27002 5.5455 -0.95032</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 7 59.1 6.567e+01 -6.57002 5.5455 -1.18475</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 14 56.5 4.847e+01 8.03029 4.1364 1.94139</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 14 47.0 4.847e+01 -1.46971 4.1364 -0.35532</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 28 30.2 3.309e+01 -2.89206 2.8971 -0.99825</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 28 23.9 3.309e+01 -9.19206 2.8971 -3.17281</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 60 17.0 1.891e+01 -1.90623 1.8076 -1.05458</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 60 18.7 1.891e+01 -0.20623 1.8076 -0.11409</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 90 11.3 1.168e+01 -0.38263 1.3160 -0.29076</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 90 11.9 1.168e+01 0.21737 1.3160 0.16518</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 120 9.0 7.230e+00 1.77031 1.0708 1.65333</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 120 8.1 7.230e+00 0.87031 1.0708 0.81280</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 0 0.7 -5.116e-13 0.70000 0.8850 0.79093</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 1 3.0 3.244e+00 -0.24430 0.9254 -0.26398</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 1 2.6 3.244e+00 -0.64430 0.9254 -0.69621</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 3 5.1 8.592e+00 -3.49175 1.1385 -3.06686</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 3 7.5 8.592e+00 -1.09175 1.1385 -0.95890</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 7 16.5 1.583e+01 0.66887 1.5890 0.42093</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 7 19.0 1.583e+01 3.16887 1.5890 1.99424</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 14 22.9 2.181e+01 1.08658 2.0224 0.53728</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 14 23.2 2.181e+01 1.38658 2.0224 0.68562</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 28 22.2 2.364e+01 -1.43659 2.1600 -0.66508</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 28 24.4 2.364e+01 0.76341 2.1600 0.35342</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 60 15.5 1.873e+01 -3.23377 1.7950 -1.80150</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 60 19.8 1.873e+01 1.06623 1.7950 0.59398</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 90 14.9 1.387e+01 1.03117 1.4560 0.70822</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 90 14.2 1.387e+01 0.33117 1.4560 0.22745</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 120 10.9 9.937e+00 0.96270 1.2122 0.79415</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 120 10.4 9.937e+00 0.46270 1.2122 0.38169</span> <span class="r-in"><span><span class="co"># }</span></span></span> <span class="r-in"><span></span></span> </code></pre></div> diff --git a/docs/reference/synthetic_data_for_UBA_2014-1.png b/docs/reference/synthetic_data_for_UBA_2014-1.png Binary files differindex 8d747ffe..132380a8 100644 --- a/docs/reference/synthetic_data_for_UBA_2014-1.png +++ b/docs/reference/synthetic_data_for_UBA_2014-1.png diff --git a/docs/reference/synthetic_data_for_UBA_2014.html b/docs/reference/synthetic_data_for_UBA_2014.html index e9251de3..c00d1b55 100644 --- a/docs/reference/synthetic_data_for_UBA_2014.html +++ b/docs/reference/synthetic_data_for_UBA_2014.html @@ -32,7 +32,7 @@ Compare also the code in the example section to see the degradation models."><!- </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -59,19 +59,25 @@ Compare also the code in the example section to see the degradation models."><!- <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -117,7 +123,7 @@ Compare also the code in the example section to see the degradation models."><!- </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="va">synthetic_data_for_UBA_2014</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">synthetic_data_for_UBA_2014</span></span></code></pre></div> </div> <div id="format"> @@ -141,115 +147,115 @@ Compare also the code in the example section to see the degradation models."><!- <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"><span class="co"># The data have been generated using the following kinetic models</span></span> -<span class="r-in"><span class="va">m_synth_SFO_lin</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M1"</span><span class="op">)</span>,</span> -<span class="r-in"> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M2"</span><span class="op">)</span>,</span> -<span class="r-in"> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="co"># The data have been generated using the following kinetic models</span></span></span> +<span class="r-in"><span><span class="va">m_synth_SFO_lin</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M2"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">m_synth_SFO_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span> -<span class="r-in"> sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> -<span class="r-in"> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">m_synth_SFO_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"></span> -<span class="r-in"><span class="va">m_synth_DFOP_lin</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="st">"M1"</span><span class="op">)</span>,</span> -<span class="r-in"> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M2"</span><span class="op">)</span>,</span> -<span class="r-in"> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">m_synth_DFOP_lin</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="st">"M1"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M2"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"></span> -<span class="r-in"><span class="va">m_synth_DFOP_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span> -<span class="r-in"> sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> -<span class="r-in"> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">m_synth_DFOP_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># The model predictions without intentional error were generated as follows</span></span> -<span class="r-in"><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">d_synth_SFO_lin</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_synth_SFO_lin</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.7</span>, f_parent_to_M1 <span class="op">=</span> <span class="fl">0.8</span>,</span> -<span class="r-in"> k_M1 <span class="op">=</span> <span class="fl">0.3</span>, f_M1_to_M2 <span class="op">=</span> <span class="fl">0.7</span>,</span> -<span class="r-in"> k_M2 <span class="op">=</span> <span class="fl">0.02</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span>, M2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="va">sampling_times</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">d_synth_DFOP_lin</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_synth_DFOP_lin</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k1 <span class="op">=</span> <span class="fl">0.2</span>, k2 <span class="op">=</span> <span class="fl">0.02</span>, g <span class="op">=</span> <span class="fl">0.5</span>,</span> -<span class="r-in"> f_parent_to_M1 <span class="op">=</span> <span class="fl">0.5</span>, k_M1 <span class="op">=</span> <span class="fl">0.3</span>,</span> -<span class="r-in"> f_M1_to_M2 <span class="op">=</span> <span class="fl">0.7</span>, k_M2 <span class="op">=</span> <span class="fl">0.02</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span>, M2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="va">sampling_times</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">d_synth_SFO_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_synth_SFO_par</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.2</span>,</span> -<span class="r-in"> f_parent_to_M1 <span class="op">=</span> <span class="fl">0.8</span>, k_M1 <span class="op">=</span> <span class="fl">0.01</span>,</span> -<span class="r-in"> f_parent_to_M2 <span class="op">=</span> <span class="fl">0.2</span>, k_M2 <span class="op">=</span> <span class="fl">0.02</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span>, M2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="va">sampling_times</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">d_synth_DFOP_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_synth_DFOP_par</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k1 <span class="op">=</span> <span class="fl">0.3</span>, k2 <span class="op">=</span> <span class="fl">0.02</span>, g <span class="op">=</span> <span class="fl">0.7</span>,</span> -<span class="r-in"> f_parent_to_M1 <span class="op">=</span> <span class="fl">0.6</span>, k_M1 <span class="op">=</span> <span class="fl">0.04</span>,</span> -<span class="r-in"> f_parent_to_M2 <span class="op">=</span> <span class="fl">0.4</span>, k_M2 <span class="op">=</span> <span class="fl">0.01</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span>, M2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span> -<span class="r-in"> <span class="va">sampling_times</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># Construct names for datasets with errors</span></span> -<span class="r-in"><span class="va">d_synth_names</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="st">"d_synth_"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO_lin"</span>, <span class="st">"SFO_par"</span>,</span> -<span class="r-in"> <span class="st">"DFOP_lin"</span>, <span class="st">"DFOP_par"</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># Original function used or adding errors. The add_err function now published</span></span> -<span class="r-in"><span class="co"># with this package is a slightly generalised version where the names of</span></span> -<span class="r-in"><span class="co"># secondary compartments that should have an initial value of zero (M1 and M2</span></span> -<span class="r-in"><span class="co"># in this case) are not hardcoded any more.</span></span> -<span class="r-in"><span class="co"># add_err = function(d, sdfunc, LOD = 0.1, reps = 2, seed = 123456789)</span></span> -<span class="r-in"><span class="co"># {</span></span> -<span class="r-in"><span class="co"># set.seed(seed)</span></span> -<span class="r-in"><span class="co"># d_long = mkin_wide_to_long(d, time = "time")</span></span> -<span class="r-in"><span class="co"># d_rep = data.frame(lapply(d_long, rep, each = 2))</span></span> -<span class="r-in"><span class="co"># d_rep$value = rnorm(length(d_rep$value), d_rep$value, sdfunc(d_rep$value))</span></span> -<span class="r-in"><span class="co">#</span></span> -<span class="r-in"><span class="co"># d_rep[d_rep$time == 0 & d_rep$name %in% c("M1", "M2"), "value"] <- 0</span></span> -<span class="r-in"><span class="co"># d_NA <- transform(d_rep, value = ifelse(value < LOD, NA, value))</span></span> -<span class="r-in"><span class="co"># d_NA$value <- round(d_NA$value, 1)</span></span> -<span class="r-in"><span class="co"># return(d_NA)</span></span> -<span class="r-in"><span class="co"># }</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># The following is the simplified version of the two-component model of Rocke</span></span> -<span class="r-in"><span class="co"># and Lorenzato (1995)</span></span> -<span class="r-in"><span class="va">sdfunc_twocomp</span> <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">value</span>, <span class="va">sd_low</span>, <span class="va">rsd_high</span><span class="op">)</span> <span class="op">{</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">sd_low</span><span class="op">^</span><span class="fl">2</span> <span class="op">+</span> <span class="va">value</span><span class="op">^</span><span class="fl">2</span> <span class="op">*</span> <span class="va">rsd_high</span><span class="op">^</span><span class="fl">2</span><span class="op">)</span></span> -<span class="r-in"><span class="op">}</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># Add the errors.</span></span> -<span class="r-in"><span class="kw">for</span> <span class="op">(</span><span class="va">d_synth_name</span> <span class="kw">in</span> <span class="va">d_synth_names</span><span class="op">)</span></span> -<span class="r-in"><span class="op">{</span></span> -<span class="r-in"> <span class="va">d_synth</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/get.html" class="external-link">get</a></span><span class="op">(</span><span class="va">d_synth_name</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">d_synth_name</span>, <span class="st">"_a"</span><span class="op">)</span>, <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_synth</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">d_synth_name</span>, <span class="st">"_b"</span><span class="op">)</span>, <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_synth</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">7</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">d_synth_name</span>, <span class="st">"_c"</span><span class="op">)</span>, <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_synth</span>,</span> -<span class="r-in"> <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fu">sdfunc_twocomp</span><span class="op">(</span><span class="va">value</span>, <span class="fl">0.5</span>, <span class="fl">0.07</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="op">}</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">d_synth_err_names</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="va">d_synth_names</span>, each <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">letters</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">]</span>, sep <span class="op">=</span> <span class="st">"_"</span><span class="op">)</span></span> -<span class="r-in"><span class="op">)</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># This is just one example of an evaluation using the kinetic model used for</span></span> -<span class="r-in"><span class="co"># the generation of the data</span></span> -<span class="r-in"> <span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_SFO_lin</span>, <span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>,</span> -<span class="r-in"> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># The model predictions without intentional error were generated as follows</span></span></span> +<span class="r-in"><span><span class="va">sampling_times</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span>, <span class="fl">60</span>, <span class="fl">90</span>, <span class="fl">120</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">d_synth_SFO_lin</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_synth_SFO_lin</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.7</span>, f_parent_to_M1 <span class="op">=</span> <span class="fl">0.8</span>,</span></span> +<span class="r-in"><span> k_M1 <span class="op">=</span> <span class="fl">0.3</span>, f_M1_to_M2 <span class="op">=</span> <span class="fl">0.7</span>,</span></span> +<span class="r-in"><span> k_M2 <span class="op">=</span> <span class="fl">0.02</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span>, M2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="va">sampling_times</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">d_synth_DFOP_lin</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_synth_DFOP_lin</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k1 <span class="op">=</span> <span class="fl">0.2</span>, k2 <span class="op">=</span> <span class="fl">0.02</span>, g <span class="op">=</span> <span class="fl">0.5</span>,</span></span> +<span class="r-in"><span> f_parent_to_M1 <span class="op">=</span> <span class="fl">0.5</span>, k_M1 <span class="op">=</span> <span class="fl">0.3</span>,</span></span> +<span class="r-in"><span> f_M1_to_M2 <span class="op">=</span> <span class="fl">0.7</span>, k_M2 <span class="op">=</span> <span class="fl">0.02</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span>, M2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="va">sampling_times</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">d_synth_SFO_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_synth_SFO_par</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_parent <span class="op">=</span> <span class="fl">0.2</span>,</span></span> +<span class="r-in"><span> f_parent_to_M1 <span class="op">=</span> <span class="fl">0.8</span>, k_M1 <span class="op">=</span> <span class="fl">0.01</span>,</span></span> +<span class="r-in"><span> f_parent_to_M2 <span class="op">=</span> <span class="fl">0.2</span>, k_M2 <span class="op">=</span> <span class="fl">0.02</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span>, M2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="va">sampling_times</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">d_synth_DFOP_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinpredict.html">mkinpredict</a></span><span class="op">(</span><span class="va">m_synth_DFOP_par</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k1 <span class="op">=</span> <span class="fl">0.3</span>, k2 <span class="op">=</span> <span class="fl">0.02</span>, g <span class="op">=</span> <span class="fl">0.7</span>,</span></span> +<span class="r-in"><span> f_parent_to_M1 <span class="op">=</span> <span class="fl">0.6</span>, k_M1 <span class="op">=</span> <span class="fl">0.04</span>,</span></span> +<span class="r-in"><span> f_parent_to_M2 <span class="op">=</span> <span class="fl">0.4</span>, k_M2 <span class="op">=</span> <span class="fl">0.01</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span>, M1 <span class="op">=</span> <span class="fl">0</span>, M2 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> <span class="va">sampling_times</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Construct names for datasets with errors</span></span></span> +<span class="r-in"><span><span class="va">d_synth_names</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="st">"d_synth_"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO_lin"</span>, <span class="st">"SFO_par"</span>,</span></span> +<span class="r-in"><span> <span class="st">"DFOP_lin"</span>, <span class="st">"DFOP_par"</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Original function used or adding errors. The add_err function now published</span></span></span> +<span class="r-in"><span><span class="co"># with this package is a slightly generalised version where the names of</span></span></span> +<span class="r-in"><span><span class="co"># secondary compartments that should have an initial value of zero (M1 and M2</span></span></span> +<span class="r-in"><span><span class="co"># in this case) are not hardcoded any more.</span></span></span> +<span class="r-in"><span><span class="co"># add_err = function(d, sdfunc, LOD = 0.1, reps = 2, seed = 123456789)</span></span></span> +<span class="r-in"><span><span class="co"># {</span></span></span> +<span class="r-in"><span><span class="co"># set.seed(seed)</span></span></span> +<span class="r-in"><span><span class="co"># d_long = mkin_wide_to_long(d, time = "time")</span></span></span> +<span class="r-in"><span><span class="co"># d_rep = data.frame(lapply(d_long, rep, each = 2))</span></span></span> +<span class="r-in"><span><span class="co"># d_rep$value = rnorm(length(d_rep$value), d_rep$value, sdfunc(d_rep$value))</span></span></span> +<span class="r-in"><span><span class="co">#</span></span></span> +<span class="r-in"><span><span class="co"># d_rep[d_rep$time == 0 & d_rep$name %in% c("M1", "M2"), "value"] <- 0</span></span></span> +<span class="r-in"><span><span class="co"># d_NA <- transform(d_rep, value = ifelse(value < LOD, NA, value))</span></span></span> +<span class="r-in"><span><span class="co"># d_NA$value <- round(d_NA$value, 1)</span></span></span> +<span class="r-in"><span><span class="co"># return(d_NA)</span></span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># The following is the simplified version of the two-component model of Rocke</span></span></span> +<span class="r-in"><span><span class="co"># and Lorenzato (1995)</span></span></span> +<span class="r-in"><span><span class="va">sdfunc_twocomp</span> <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">value</span>, <span class="va">sd_low</span>, <span class="va">rsd_high</span><span class="op">)</span> <span class="op">{</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">sqrt</a></span><span class="op">(</span><span class="va">sd_low</span><span class="op">^</span><span class="fl">2</span> <span class="op">+</span> <span class="va">value</span><span class="op">^</span><span class="fl">2</span> <span class="op">*</span> <span class="va">rsd_high</span><span class="op">^</span><span class="fl">2</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="op">}</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Add the errors.</span></span></span> +<span class="r-in"><span><span class="kw">for</span> <span class="op">(</span><span class="va">d_synth_name</span> <span class="kw">in</span> <span class="va">d_synth_names</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="op">{</span></span></span> +<span class="r-in"><span> <span class="va">d_synth</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/get.html" class="external-link">get</a></span><span class="op">(</span><span class="va">d_synth_name</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">d_synth_name</span>, <span class="st">"_a"</span><span class="op">)</span>, <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_synth</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">d_synth_name</span>, <span class="st">"_b"</span><span class="op">)</span>, <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_synth</span>, <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fl">7</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/assign.html" class="external-link">assign</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">d_synth_name</span>, <span class="st">"_c"</span><span class="op">)</span>, <span class="fu"><a href="add_err.html">add_err</a></span><span class="op">(</span><span class="va">d_synth</span>,</span></span> +<span class="r-in"><span> <span class="kw">function</span><span class="op">(</span><span class="va">value</span><span class="op">)</span> <span class="fu">sdfunc_twocomp</span><span class="op">(</span><span class="va">value</span>, <span class="fl">0.5</span>, <span class="fl">0.07</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="op">}</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">d_synth_err_names</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="va">d_synth_names</span>, each <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">letters</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">3</span><span class="op">]</span>, sep <span class="op">=</span> <span class="st">"_"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="op">)</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># This is just one example of an evaluation using the kinetic model used for</span></span></span> +<span class="r-in"><span><span class="co"># the generation of the data</span></span></span> +<span class="r-in"><span> <span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_SFO_lin</span>, <span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>,</span></span> +<span class="r-in"><span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span> <span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="synthetic_data_for_UBA_2014-1.png" alt="" width="700" height="433"></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.1.0 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.0 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Wed May 18 20:42:21 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Wed May 18 20:42:21 2022 </span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.2.0 </span> +<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Nov 17 14:04:10 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Nov 17 14:04:11 2022 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - k_parent * parent</span> @@ -258,7 +264,7 @@ Compare also the code in the example section to see the degradation models."><!- <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type deSolve </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted using 830 model solutions performed in 1.716 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted using 833 model solutions performed in 0.574 s</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Error model: Constant variance </span> <span class="r-out co"><span class="r-pr">#></span> </span> @@ -310,15 +316,15 @@ Compare also the code in the example section to see the degradation models."><!- <span class="r-out co"><span class="r-pr">#></span> log_k_M2 2.819e-02 7.166e-02 -3.929e-01 1.000e+00 -2.658e-01</span> <span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -4.624e-01 -5.682e-01 7.478e-01 -2.658e-01 1.000e+00</span> <span class="r-out co"><span class="r-pr">#></span> f_M1_qlogis 1.614e-01 4.102e-01 -8.109e-01 5.419e-01 -8.605e-01</span> -<span class="r-out co"><span class="r-pr">#></span> sigma 2.063e-07 -1.980e-07 1.088e-07 1.041e-07 7.820e-09</span> +<span class="r-out co"><span class="r-pr">#></span> sigma -2.900e-08 -8.030e-09 -2.741e-08 3.938e-08 -2.681e-08</span> <span class="r-out co"><span class="r-pr">#></span> f_M1_qlogis sigma</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 1.614e-01 2.063e-07</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_parent 4.102e-01 -1.980e-07</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M1 -8.109e-01 1.088e-07</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M2 5.419e-01 1.041e-07</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -8.605e-01 7.820e-09</span> -<span class="r-out co"><span class="r-pr">#></span> f_M1_qlogis 1.000e+00 -6.495e-08</span> -<span class="r-out co"><span class="r-pr">#></span> sigma -6.495e-08 1.000e+00</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 1.614e-01 -2.900e-08</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_parent 4.102e-01 -8.030e-09</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M1 -8.109e-01 -2.741e-08</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M2 5.419e-01 3.938e-08</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -8.605e-01 -2.681e-08</span> +<span class="r-out co"><span class="r-pr">#></span> f_M1_qlogis 1.000e+00 4.971e-08</span> +<span class="r-out co"><span class="r-pr">#></span> sigma 4.971e-08 1.000e+00</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Backtransformed parameters:</span> <span class="r-out co"><span class="r-pr">#></span> Confidence intervals for internally transformed parameters are asymmetric.</span> @@ -355,8 +361,8 @@ Compare also the code in the example section to see the degradation models."><!- <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Data:</span> <span class="r-out co"><span class="r-pr">#></span> time variable observed predicted residual</span> -<span class="r-out co"><span class="r-pr">#></span> 0 parent 101.5 1.021e+02 -0.56249</span> -<span class="r-out co"><span class="r-pr">#></span> 0 parent 101.2 1.021e+02 -0.86249</span> +<span class="r-out co"><span class="r-pr">#></span> 0 parent 101.5 1.021e+02 -0.56248</span> +<span class="r-out co"><span class="r-pr">#></span> 0 parent 101.2 1.021e+02 -0.86248</span> <span class="r-out co"><span class="r-pr">#></span> 1 parent 53.9 4.873e+01 5.17118</span> <span class="r-out co"><span class="r-pr">#></span> 1 parent 47.5 4.873e+01 -1.22882</span> <span class="r-out co"><span class="r-pr">#></span> 3 parent 10.4 1.111e+01 -0.70773</span> @@ -365,8 +371,8 @@ Compare also the code in the example section to see the degradation models."><!- <span class="r-out co"><span class="r-pr">#></span> 7 parent 0.3 5.772e-01 -0.27717</span> <span class="r-out co"><span class="r-pr">#></span> 14 parent 3.5 3.264e-03 3.49674</span> <span class="r-out co"><span class="r-pr">#></span> 28 parent 3.2 1.045e-07 3.20000</span> -<span class="r-out co"><span class="r-pr">#></span> 90 parent 0.6 9.534e-10 0.60000</span> -<span class="r-out co"><span class="r-pr">#></span> 120 parent 3.5 -5.941e-10 3.50000</span> +<span class="r-out co"><span class="r-pr">#></span> 90 parent 0.6 9.530e-10 0.60000</span> +<span class="r-out co"><span class="r-pr">#></span> 120 parent 3.5 -5.940e-10 3.50000</span> <span class="r-out co"><span class="r-pr">#></span> 1 M1 36.4 3.479e+01 1.61088</span> <span class="r-out co"><span class="r-pr">#></span> 1 M1 37.4 3.479e+01 2.61088</span> <span class="r-out co"><span class="r-pr">#></span> 3 M1 34.3 3.937e+01 -5.07027</span> @@ -376,9 +382,9 @@ Compare also the code in the example section to see the degradation models."><!- <span class="r-out co"><span class="r-pr">#></span> 14 M1 5.8 1.995e+00 3.80469</span> <span class="r-out co"><span class="r-pr">#></span> 14 M1 1.2 1.995e+00 -0.79531</span> <span class="r-out co"><span class="r-pr">#></span> 60 M1 0.5 2.111e-06 0.50000</span> -<span class="r-out co"><span class="r-pr">#></span> 90 M1 3.2 -9.675e-10 3.20000</span> -<span class="r-out co"><span class="r-pr">#></span> 120 M1 1.5 7.671e-10 1.50000</span> -<span class="r-out co"><span class="r-pr">#></span> 120 M1 0.6 7.671e-10 0.60000</span> +<span class="r-out co"><span class="r-pr">#></span> 90 M1 3.2 -9.670e-10 3.20000</span> +<span class="r-out co"><span class="r-pr">#></span> 120 M1 1.5 7.670e-10 1.50000</span> +<span class="r-out co"><span class="r-pr">#></span> 120 M1 0.6 7.670e-10 0.60000</span> <span class="r-out co"><span class="r-pr">#></span> 1 M2 4.8 4.455e+00 0.34517</span> <span class="r-out co"><span class="r-pr">#></span> 3 M2 20.9 2.153e+01 -0.62527</span> <span class="r-out co"><span class="r-pr">#></span> 3 M2 19.3 2.153e+01 -2.22527</span> @@ -394,7 +400,7 @@ Compare also the code in the example section to see the degradation models."><!- <span class="r-out co"><span class="r-pr">#></span> 90 M2 10.8 1.013e+01 0.67130</span> <span class="r-out co"><span class="r-pr">#></span> 120 M2 9.8 5.521e+00 4.27893</span> <span class="r-out co"><span class="r-pr">#></span> 120 M2 3.3 5.521e+00 -2.22107</span> -<span class="r-in"><span class="co"># }</span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> </code></pre></div> </div> </div> @@ -409,7 +415,7 @@ Compare also the code in the example section to see the degradation models."><!- </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/test_data_from_UBA_2014.html b/docs/reference/test_data_from_UBA_2014.html index 327e8ae9..a76f23ab 100644 --- a/docs/reference/test_data_from_UBA_2014.html +++ b/docs/reference/test_data_from_UBA_2014.html @@ -18,7 +18,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -45,19 +45,25 @@ <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -89,7 +95,7 @@ </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="va">test_data_from_UBA_2014</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">test_data_from_UBA_2014</span></span></code></pre></div> </div> <div id="format"> @@ -111,21 +117,23 @@ <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"> <span class="co"># \dontrun{</span></span> -<span class="r-in"> <span class="co"># This is a level P-II evaluation of the dataset according to the FOCUS kinetics</span></span> -<span class="r-in"> <span class="co"># guidance. Due to the strong correlation of the parameter estimates, the</span></span> -<span class="r-in"> <span class="co"># covariance matrix is not returned. Note that level P-II evaluations are</span></span> -<span class="r-in"> <span class="co"># generally considered deprecated due to the frequent occurrence of such</span></span> -<span class="r-in"> <span class="co"># large parameter correlations, among other reasons (e.g. the adequacy of the</span></span> -<span class="r-in"> <span class="co"># model).</span></span> -<span class="r-in"> <span class="va">m_ws</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent_w <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"parent_s"</span><span class="op">)</span>,</span> -<span class="r-in"> parent_s <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"parent_w"</span><span class="op">)</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span> <span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span> <span class="co"># This is a level P-II evaluation of the dataset according to the FOCUS kinetics</span></span></span> +<span class="r-in"><span> <span class="co"># guidance. Due to the strong correlation of the parameter estimates, the</span></span></span> +<span class="r-in"><span> <span class="co"># covariance matrix is not returned. Note that level P-II evaluations are</span></span></span> +<span class="r-in"><span> <span class="co"># generally considered deprecated due to the frequent occurrence of such</span></span></span> +<span class="r-in"><span> <span class="co"># large parameter correlations, among other reasons (e.g. the adequacy of the</span></span></span> +<span class="r-in"><span> <span class="co"># model).</span></span></span> +<span class="r-in"><span> <span class="va">m_ws</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent_w <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"parent_s"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> parent_s <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"parent_w"</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"> <span class="va">f_river</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_ws</span>, <span class="va">test_data_from_UBA_2014</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">f_river</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="va">f_river</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_ws</span>, <span class="va">test_data_from_UBA_2014</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span> +<span class="r-in"><span> <span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">f_river</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="test_data_from_UBA_2014-1.png" alt="" width="700" height="433"></span> -<span class="r-in"></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_river</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_river</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></span> +<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Could not calculate correlation; no covariance matrix</span> <span class="r-out co"><span class="r-pr">#></span> Estimate se_notrans t value Pr(>t) Lower Upper</span> <span class="r-out co"><span class="r-pr">#></span> parent_w_0 95.91998118 NA NA NA NA NA</span> <span class="r-out co"><span class="r-pr">#></span> k_parent_w 0.41145375 NA NA NA NA NA</span> @@ -133,25 +141,26 @@ <span class="r-out co"><span class="r-pr">#></span> f_parent_w_to_parent_s 0.12467894 NA NA NA NA NA</span> <span class="r-out co"><span class="r-pr">#></span> f_parent_s_to_parent_w 0.50000000 NA NA NA NA NA</span> <span class="r-out co"><span class="r-pr">#></span> sigma 3.13612618 NA NA NA NA NA</span> -<span class="r-in"> <span class="fu"><a href="mkinerrmin.html">mkinerrmin</a></span><span class="op">(</span><span class="va">f_river</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="fu"><a href="mkinerrmin.html">mkinerrmin</a></span><span class="op">(</span><span class="va">f_river</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> err.min n.optim df</span> <span class="r-out co"><span class="r-pr">#></span> All data 0.1090929 5 6</span> <span class="r-out co"><span class="r-pr">#></span> parent_w 0.0817436 3 3</span> <span class="r-out co"><span class="r-pr">#></span> parent_s 0.1619965 2 3</span> -<span class="r-in"></span> -<span class="r-in"> <span class="co"># This is the evaluation used for the validation of software packages</span></span> -<span class="r-in"> <span class="co"># in the expertise from 2014</span></span> -<span class="r-in"> <span class="va">m_soil</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span> -<span class="r-in"> M1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M3"</span><span class="op">)</span>,</span> -<span class="r-in"> M2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M3"</span><span class="op">)</span>,</span> -<span class="r-in"> M3 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="co"># This is the evaluation used for the validation of software packages</span></span></span> +<span class="r-in"><span> <span class="co"># in the expertise from 2014</span></span></span> +<span class="r-in"><span> <span class="va">m_soil</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M3"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M2 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M3"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> M3 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"></span> -<span class="r-in"> <span class="va">f_soil</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_soil</span>, <span class="va">test_data_from_UBA_2014</span><span class="op">[[</span><span class="fl">3</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"> <span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">f_soil</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"topright"</span>, <span class="st">"topright"</span>, <span class="st">"bottomright"</span><span class="op">)</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span> <span class="va">f_soil</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_soil</span>, <span class="va">test_data_from_UBA_2014</span><span class="op">[[</span><span class="fl">3</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Observations with value of zero were removed from the data</span> +<span class="r-in"><span> <span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">f_soil</span>, lpos <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"topright"</span>, <span class="st">"topright"</span>, <span class="st">"topright"</span>, <span class="st">"bottomright"</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="test_data_from_UBA_2014-2.png" alt="" width="700" height="433"></span> -<span class="r-in"> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_soil</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span> +<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_soil</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Estimate se_notrans t value Pr(>t) Lower</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 76.55425650 0.859186399 89.1008710 1.113861e-26 74.755959418</span> <span class="r-out co"><span class="r-pr">#></span> k_parent 0.12081956 0.004601918 26.2541722 1.077359e-16 0.111561575</span> @@ -174,14 +183,14 @@ <span class="r-out co"><span class="r-pr">#></span> f_M1_to_M3 0.86450775</span> <span class="r-out co"><span class="r-pr">#></span> f_M2_to_M3 0.99489895</span> <span class="r-out co"><span class="r-pr">#></span> sigma 1.45337221</span> -<span class="r-in"> <span class="fu"><a href="mkinerrmin.html">mkinerrmin</a></span><span class="op">(</span><span class="va">f_soil</span><span class="op">)</span></span> +<span class="r-in"><span> <span class="fu"><a href="mkinerrmin.html">mkinerrmin</a></span><span class="op">(</span><span class="va">f_soil</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> err.min n.optim df</span> <span class="r-out co"><span class="r-pr">#></span> All data 0.09649963 9 20</span> <span class="r-out co"><span class="r-pr">#></span> parent 0.04721283 2 6</span> <span class="r-out co"><span class="r-pr">#></span> M1 0.26551208 2 5</span> <span class="r-out co"><span class="r-pr">#></span> M2 0.20327575 2 5</span> <span class="r-out co"><span class="r-pr">#></span> M3 0.05196550 3 4</span> -<span class="r-in"> <span class="co"># }</span></span> +<span class="r-in"><span> <span class="co"># }</span></span></span> </code></pre></div> </div> </div> @@ -196,7 +205,7 @@ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/tex_listing.html b/docs/reference/tex_listing.html new file mode 100644 index 00000000..4b8736c3 --- /dev/null +++ b/docs/reference/tex_listing.html @@ -0,0 +1,143 @@ +<!DOCTYPE html> +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Wrap the output of a summary function in tex listing environment — tex_listing • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link 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src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> +<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> +<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + + + <div class="container template-reference-topic"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <div class="container"> + <div class="navbar-header"> + <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> + <span class="sr-only">Toggle navigation</span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + </button> + <span class="navbar-brand"> + <a class="navbar-link" href="../index.html">mkin</a> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + </span> + </div> + + <div id="navbar" class="navbar-collapse collapse"> + <ul class="nav navbar-nav"><li> + <a href="../reference/index.html">Functions and data</a> +</li> +<li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> + Articles + + <span class="caret"></span> + </a> + <ul class="dropdown-menu" role="menu"><li> + <a href="../articles/mkin.html">Introduction to mkin</a> + </li> + <li> + <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> + </li> + <li> + <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> + </li> + <li> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + </li> + <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> + <li> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + </ul></li> +<li> + <a href="../news/index.html">News</a> +</li> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> + <span class="fab fa-github fa-lg"></span> + + </a> +</li> + </ul></div><!--/.nav-collapse --> + </div><!--/.container --> +</div><!--/.navbar --> + + + + </header><div class="row"> + <div class="col-md-9 contents"> + <div class="page-header"> + <h1>Wrap the output of a summary function in tex listing environment</h1> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/tex_listing.R" class="external-link"><code>R/tex_listing.R</code></a></small> + <div class="hidden name"><code>tex_listing.Rd</code></div> + </div> + + <div class="ref-description"> + <p>This function can be used in a R markdown code chunk with the chunk +option <code>results = "asis"</code>.</p> + </div> + + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">tex_listing</span><span class="op">(</span><span class="va">object</span>, caption <span class="op">=</span> <span class="cn">NULL</span>, label <span class="op">=</span> <span class="cn">NULL</span>, clearpage <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>object</dt> +<dd><p>The object for which the summary is to be listed</p></dd> + + +<dt>caption</dt> +<dd><p>An optional caption</p></dd> + + +<dt>label</dt> +<dd><p>An optional label</p></dd> + + +<dt>clearpage</dt> +<dd><p>Should a new page be started after the listing?</p></dd> + +</dl></div> + + </div> + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> +</div> + + + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +</div> + + </footer></div> + + + + + + + </body></html> + diff --git a/docs/reference/transform_odeparms.html b/docs/reference/transform_odeparms.html index 2da09efe..66e94941 100644 --- a/docs/reference/transform_odeparms.html +++ b/docs/reference/transform_odeparms.html @@ -22,7 +22,7 @@ the ilr transformation is used."><!-- mathjax --><script src="https://cdnjs.clou </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -49,19 +49,25 @@ the ilr transformation is used."><!-- mathjax --><script src="https://cdnjs.clou <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -97,19 +103,19 @@ the <a href="ilr.html">ilr</a> transformation is used.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">transform_odeparms</span><span class="op">(</span> - <span class="va">parms</span>, - <span class="va">mkinmod</span>, - transform_rates <span class="op">=</span> <span class="cn">TRUE</span>, - transform_fractions <span class="op">=</span> <span class="cn">TRUE</span> -<span class="op">)</span> - -<span class="fu">backtransform_odeparms</span><span class="op">(</span> - <span class="va">transparms</span>, - <span class="va">mkinmod</span>, - transform_rates <span class="op">=</span> <span class="cn">TRUE</span>, - transform_fractions <span class="op">=</span> <span class="cn">TRUE</span> -<span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">transform_odeparms</span><span class="op">(</span></span> +<span> <span class="va">parms</span>,</span> +<span> <span class="va">mkinmod</span>,</span> +<span> transform_rates <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> transform_fractions <span class="op">=</span> <span class="cn">TRUE</span></span> +<span><span class="op">)</span></span> +<span></span> +<span><span class="fu">backtransform_odeparms</span><span class="op">(</span></span> +<span> <span class="va">transparms</span>,</span> +<span> <span class="va">mkinmod</span>,</span> +<span> transform_rates <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> transform_fractions <span class="op">=</span> <span class="cn">TRUE</span></span> +<span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> @@ -117,11 +123,15 @@ the <a href="ilr.html">ilr</a> transformation is used.</p> <dl><dt>parms</dt> <dd><p>Parameters of kinetic models as used in the differential equations.</p></dd> + + <dt>mkinmod</dt> <dd><p>The kinetic model of class <a href="mkinmod.html">mkinmod</a>, containing the names of the model variables that are needed for grouping the formation fractions before <a href="ilr.html">ilr</a> transformation, the parameter names and the information if the pathway to sink is included in the model.</p></dd> + + <dt>transform_rates</dt> <dd><p>Boolean specifying if kinetic rate constants should be transformed in the model specification used in the fitting for better @@ -129,6 +139,8 @@ compliance with the assumption of normal distribution of the estimator. If TRUE, also alpha and beta parameters of the FOMC model are log-transformed, as well as k1 and k2 rate constants for the DFOP and HS models and the break point tb of the HS model.</p></dd> + + <dt>transform_fractions</dt> <dd><p>Boolean specifying if formation fractions constants should be transformed in the model specification used in the @@ -140,13 +152,18 @@ target variable e.g. a single metabolite plus a pathway to sink), a logistic transformation is used <code><a href="https://rdrr.io/r/stats/Logistic.html" class="external-link">stats::qlogis()</a></code>. In other cases, i.e. if two or more formation fractions need to be transformed whose sum cannot exceed one, the <a href="ilr.html">ilr</a> transformation is used.</p></dd> + + <dt>transparms</dt> <dd><p>Transformed parameters of kinetic models as used in the fitting procedure.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> - <p>A vector of transformed or backtransformed parameters</p> + + +<p>A vector of transformed or backtransformed parameters</p> </div> <div id="details"> <h2>Details</h2> @@ -161,119 +178,119 @@ This is no problem for the internal use in <a href="mkinfit.html">mkinfit</a>.</ <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"></span> -<span class="r-in"><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span> -<span class="r-in"> parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span>, sink <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span> -<span class="r-in"> m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"min"</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span>, sink <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"min"</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># Fit the model to the FOCUS example dataset D using defaults</span></span> -<span class="r-in"><span class="va">FOCUS_D</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span> <span class="co"># remove zero values to avoid warning</span></span> -<span class="r-in"><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="va">fit.s</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span> -<span class="r-in"><span class="co"># Transformed and backtransformed parameters</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.s</span><span class="op">$</span><span class="va">par</span>, <span class="fl">3</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># Fit the model to the FOCUS example dataset D using defaults</span></span></span> +<span class="r-in"><span><span class="va">FOCUS_D</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span> <span class="co"># remove zero values to avoid warning</span></span></span> +<span class="r-in"><span><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">fit.s</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># Transformed and backtransformed parameters</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.s</span><span class="op">$</span><span class="va">par</span>, <span class="fl">3</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Estimate Std. Error Lower Upper</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 99.60 1.5702 96.40 102.79</span> <span class="r-out co"><span class="r-pr">#></span> log_k_parent_sink -3.04 0.0763 -3.19 -2.88</span> <span class="r-out co"><span class="r-pr">#></span> log_k_parent_m1 -2.98 0.0403 -3.06 -2.90</span> <span class="r-out co"><span class="r-pr">#></span> log_k_m1_sink -5.25 0.1332 -5.52 -4.98</span> <span class="r-out co"><span class="r-pr">#></span> sigma 3.13 0.3585 2.40 3.85</span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.s</span><span class="op">$</span><span class="va">bpar</span>, <span class="fl">3</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.s</span><span class="op">$</span><span class="va">bpar</span>, <span class="fl">3</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Estimate se_notrans t value Pr(>t) Lower Upper</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 99.59848 1.57022 63.43 2.30e-36 96.40384 102.7931</span> <span class="r-out co"><span class="r-pr">#></span> k_parent_sink 0.04792 0.00365 13.11 6.13e-15 0.04103 0.0560</span> <span class="r-out co"><span class="r-pr">#></span> k_parent_m1 0.05078 0.00205 24.80 3.27e-23 0.04678 0.0551</span> <span class="r-out co"><span class="r-pr">#></span> k_m1_sink 0.00526 0.00070 7.51 6.16e-09 0.00401 0.0069</span> <span class="r-out co"><span class="r-pr">#></span> sigma 3.12550 0.35852 8.72 2.24e-10 2.39609 3.8549</span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"><span class="co"># Compare to the version without transforming rate parameters (does not work</span></span> -<span class="r-in"><span class="co"># with analytical solution, we get NA values for m1 in predictions)</span></span> -<span class="r-in"><span class="va">fit.2</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, transform_rates <span class="op">=</span> <span class="cn">FALSE</span>,</span> -<span class="r-in"> solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="va">fit.2.s</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.2</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.2.s</span><span class="op">$</span><span class="va">par</span>, <span class="fl">3</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="co"># Compare to the version without transforming rate parameters (does not work</span></span></span> +<span class="r-in"><span><span class="co"># with analytical solution, we get NA values for m1 in predictions)</span></span></span> +<span class="r-in"><span><span class="va">fit.2</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_D</span>, transform_rates <span class="op">=</span> <span class="cn">FALSE</span>,</span></span> +<span class="r-in"><span> solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">fit.2.s</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.2</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.2.s</span><span class="op">$</span><span class="va">par</span>, <span class="fl">3</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Estimate Std. Error Lower Upper</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 99.59848 1.57022 96.40384 1.03e+02</span> <span class="r-out co"><span class="r-pr">#></span> k_parent_sink 0.04792 0.00365 0.04049 5.54e-02</span> <span class="r-out co"><span class="r-pr">#></span> k_parent_m1 0.05078 0.00205 0.04661 5.49e-02</span> <span class="r-out co"><span class="r-pr">#></span> k_m1_sink 0.00526 0.00070 0.00384 6.69e-03</span> <span class="r-out co"><span class="r-pr">#></span> sigma 3.12550 0.35852 2.39609 3.85e+00</span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.2.s</span><span class="op">$</span><span class="va">bpar</span>, <span class="fl">3</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.2.s</span><span class="op">$</span><span class="va">bpar</span>, <span class="fl">3</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Estimate se_notrans t value Pr(>t) Lower Upper</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 99.59848 1.57022 63.43 2.30e-36 96.40384 1.03e+02</span> <span class="r-out co"><span class="r-pr">#></span> k_parent_sink 0.04792 0.00365 13.11 6.13e-15 0.04049 5.54e-02</span> <span class="r-out co"><span class="r-pr">#></span> k_parent_m1 0.05078 0.00205 24.80 3.27e-23 0.04661 5.49e-02</span> <span class="r-out co"><span class="r-pr">#></span> k_m1_sink 0.00526 0.00070 7.51 6.16e-09 0.00384 6.69e-03</span> <span class="r-out co"><span class="r-pr">#></span> sigma 3.12550 0.35852 8.72 2.24e-10 2.39609 3.85e+00</span> -<span class="r-in"><span class="co"># }</span></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">initials</span> <span class="op"><-</span> <span class="va">fit</span><span class="op">$</span><span class="va">start</span><span class="op">$</span><span class="va">value</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">initials</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="va">fit</span><span class="op">$</span><span class="va">start</span><span class="op">)</span></span> -<span class="r-in"><span class="va">transformed</span> <span class="op"><-</span> <span class="va">fit</span><span class="op">$</span><span class="va">start_transformed</span><span class="op">$</span><span class="va">value</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">transformed</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="va">fit</span><span class="op">$</span><span class="va">start_transformed</span><span class="op">)</span></span> -<span class="r-in"><span class="fu">transform_odeparms</span><span class="op">(</span><span class="va">initials</span>, <span class="va">SFO_SFO</span><span class="op">)</span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">initials</span> <span class="op"><-</span> <span class="va">fit</span><span class="op">$</span><span class="va">start</span><span class="op">$</span><span class="va">value</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">initials</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="va">fit</span><span class="op">$</span><span class="va">start</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">transformed</span> <span class="op"><-</span> <span class="va">fit</span><span class="op">$</span><span class="va">start_transformed</span><span class="op">$</span><span class="va">value</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">transformed</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="va">fit</span><span class="op">$</span><span class="va">start_transformed</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu">transform_odeparms</span><span class="op">(</span><span class="va">initials</span>, <span class="va">SFO_SFO</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink </span> <span class="r-out co"><span class="r-pr">#></span> 100.750000 -2.302585 -2.301586 -2.300587 </span> -<span class="r-in"><span class="fu">backtransform_odeparms</span><span class="op">(</span><span class="va">transformed</span>, <span class="va">SFO_SFO</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu">backtransform_odeparms</span><span class="op">(</span><span class="va">transformed</span>, <span class="va">SFO_SFO</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> parent_0 k_parent_sink k_parent_m1 k_m1_sink </span> <span class="r-out co"><span class="r-pr">#></span> 100.7500 0.1000 0.1001 0.1002 </span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"><span class="co"># The case of formation fractions (this is now the default)</span></span> -<span class="r-in"><span class="va">SFO_SFO.ff</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span> -<span class="r-in"> parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span>, sink <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span> -<span class="r-in"> m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="co"># The case of formation fractions (this is now the default)</span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO.ff</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span>, sink <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"></span> -<span class="r-in"><span class="va">fit.ff</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO.ff</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="va">fit.ff.s</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.ff</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.ff.s</span><span class="op">$</span><span class="va">par</span>, <span class="fl">3</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">fit.ff</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO.ff</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">fit.ff.s</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.ff</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.ff.s</span><span class="op">$</span><span class="va">par</span>, <span class="fl">3</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Estimate Std. Error Lower Upper</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 99.5985 1.5702 96.404 102.79</span> <span class="r-out co"><span class="r-pr">#></span> log_k_parent -2.3157 0.0409 -2.399 -2.23</span> <span class="r-out co"><span class="r-pr">#></span> log_k_m1 -5.2475 0.1332 -5.518 -4.98</span> <span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis 0.0579 0.0893 -0.124 0.24</span> <span class="r-out co"><span class="r-pr">#></span> sigma 3.1255 0.3585 2.396 3.85</span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.ff.s</span><span class="op">$</span><span class="va">bpar</span>, <span class="fl">3</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.ff.s</span><span class="op">$</span><span class="va">bpar</span>, <span class="fl">3</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Estimate se_notrans t value Pr(>t) Lower Upper</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 99.59848 1.57022 63.43 2.30e-36 96.40383 102.7931</span> <span class="r-out co"><span class="r-pr">#></span> k_parent 0.09870 0.00403 24.47 4.96e-23 0.09082 0.1073</span> <span class="r-out co"><span class="r-pr">#></span> k_m1 0.00526 0.00070 7.51 6.16e-09 0.00401 0.0069</span> <span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.51448 0.02230 23.07 3.10e-22 0.46912 0.5596</span> <span class="r-out co"><span class="r-pr">#></span> sigma 3.12550 0.35852 8.72 2.24e-10 2.39609 3.8549</span> -<span class="r-in"><span class="va">initials</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"f_parent_to_m1"</span> <span class="op">=</span> <span class="fl">0.5</span><span class="op">)</span></span> -<span class="r-in"><span class="va">transformed</span> <span class="op"><-</span> <span class="fu">transform_odeparms</span><span class="op">(</span><span class="va">initials</span>, <span class="va">SFO_SFO.ff</span><span class="op">)</span></span> -<span class="r-in"><span class="fu">backtransform_odeparms</span><span class="op">(</span><span class="va">transformed</span>, <span class="va">SFO_SFO.ff</span><span class="op">)</span></span> +<span class="r-in"><span><span class="va">initials</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"f_parent_to_m1"</span> <span class="op">=</span> <span class="fl">0.5</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">transformed</span> <span class="op"><-</span> <span class="fu">transform_odeparms</span><span class="op">(</span><span class="va">initials</span>, <span class="va">SFO_SFO.ff</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu">backtransform_odeparms</span><span class="op">(</span><span class="va">transformed</span>, <span class="va">SFO_SFO.ff</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 </span> <span class="r-out co"><span class="r-pr">#></span> 0.5 </span> -<span class="r-in"></span> -<span class="r-in"><span class="co"># And without sink</span></span> -<span class="r-in"><span class="va">SFO_SFO.ff.2</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span> -<span class="r-in"> parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> -<span class="r-in"> m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span> -<span class="r-in"> use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># And without sink</span></span></span> +<span class="r-in"><span><span class="va">SFO_SFO.ff.2</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span></span> +<span class="r-in"><span> parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"m1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> m1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span></span> +<span class="r-in"><span> use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"></span> -<span class="r-in"></span> -<span class="r-in"><span class="va">fit.ff.2</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO.ff.2</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="va">fit.ff.2.s</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.ff.2</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.ff.2.s</span><span class="op">$</span><span class="va">par</span>, <span class="fl">3</span><span class="op">)</span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span></span></span> +<span class="r-in"><span><span class="va">fit.ff.2</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO.ff.2</span>, <span class="va">FOCUS_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">fit.ff.2.s</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.ff.2</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.ff.2.s</span><span class="op">$</span><span class="va">par</span>, <span class="fl">3</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Estimate Std. Error Lower Upper</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 84.79 3.012 78.67 90.91</span> <span class="r-out co"><span class="r-pr">#></span> log_k_parent -2.76 0.082 -2.92 -2.59</span> <span class="r-out co"><span class="r-pr">#></span> log_k_m1 -4.21 0.123 -4.46 -3.96</span> <span class="r-out co"><span class="r-pr">#></span> sigma 8.22 0.943 6.31 10.14</span> -<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.ff.2.s</span><span class="op">$</span><span class="va">bpar</span>, <span class="fl">3</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">fit.ff.2.s</span><span class="op">$</span><span class="va">bpar</span>, <span class="fl">3</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Estimate se_notrans t value Pr(>t) Lower Upper</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 84.7916 3.01203 28.15 1.92e-25 78.6704 90.913</span> <span class="r-out co"><span class="r-pr">#></span> k_parent 0.0635 0.00521 12.19 2.91e-14 0.0538 0.075</span> <span class="r-out co"><span class="r-pr">#></span> k_m1 0.0148 0.00182 8.13 8.81e-10 0.0115 0.019</span> <span class="r-out co"><span class="r-pr">#></span> sigma 8.2229 0.94323 8.72 1.73e-10 6.3060 10.140</span> -<span class="r-in"><span class="co"># }</span></span> -<span class="r-in"></span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> </code></pre></div> </div> </div> @@ -288,7 +305,7 @@ This is no problem for the internal use in <a href="mkinfit.html">mkinfit</a>.</ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/reference/update.mkinfit.html b/docs/reference/update.mkinfit.html index 48bf1d7f..d5b4f6f3 100644 --- a/docs/reference/update.mkinfit.html +++ b/docs/reference/update.mkinfit.html @@ -20,7 +20,7 @@ override these starting values."><!-- mathjax --><script src="https://cdnjs.clou </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> </span> </div> @@ -47,19 +47,25 @@ override these starting values."><!-- mathjax --><script src="https://cdnjs.clou <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> </li> <li> <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> </li> <li> <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> </li> <li> - <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> </ul></li> <li> @@ -93,38 +99,43 @@ override these starting values.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="co"># S3 method for mkinfit</span> -<span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span>, evaluate <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mkinfit</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span>, evaluate <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> <h2>Arguments</h2> <dl><dt>object</dt> <dd><p>An mkinfit object to be updated</p></dd> + + <dt>...</dt> <dd><p>Arguments to <code><a href="mkinfit.html">mkinfit</a></code> that should replace the arguments from the original call. Arguments set to NULL will remove arguments given in the original call</p></dd> + + <dt>evaluate</dt> <dd><p>Should the call be evaluated or returned as a call</p></dd> + </dl></div> <div id="ref-examples"> <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="co"># \dontrun{</span></span> -<span class="r-in"><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="parms.html">parms</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="parms.html">parms</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> parent_0 k_parent sigma </span> -<span class="r-out co"><span class="r-pr">#></span> 99.44423886 0.09793574 3.39632469 </span> -<span class="r-in"><span class="fu"><a href="plot.mkinfit.html">plot_err</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span> +<span class="r-out co"><span class="r-pr">#></span> 99.44423885 0.09793574 3.39632469 </span> +<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_err</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="update.mkinfit-1.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="va">fit_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">fit</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="parms.html">parms</a></span><span class="op">(</span><span class="va">fit_2</span><span class="op">)</span></span> +<span class="r-in"><span><span class="va">fit_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">fit</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="parms.html">parms</a></span><span class="op">(</span><span class="va">fit_2</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> parent_0 k_parent sigma_low rsd_high </span> <span class="r-out co"><span class="r-pr">#></span> 1.008549e+02 1.005665e-01 3.752222e-03 6.763434e-02 </span> -<span class="r-in"><span class="fu"><a href="plot.mkinfit.html">plot_err</a></span><span class="op">(</span><span class="va">fit_2</span><span class="op">)</span></span> +<span class="r-in"><span><span class="fu"><a href="plot.mkinfit.html">plot_err</a></span><span class="op">(</span><span class="va">fit_2</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="update.mkinfit-2.png" alt="" width="700" height="433"></span> -<span class="r-in"><span class="co"># }</span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> </code></pre></div> </div> </div> @@ -139,7 +150,7 @@ remove arguments given in the original call</p></dd> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.3.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> </div> </footer></div> diff --git a/docs/sitemap.xml b/docs/sitemap.xml index b30d21a1..2571bb4b 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -34,6 +34,12 @@ <loc>https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/articles/web_only/multistart.html</loc> + </url> + <url> + <loc>https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/authors.html</loc> </url> <url> @@ -97,6 +103,9 @@ <loc>https://pkgdown.jrwb.de/mkin/reference/add_err.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/reference/anova.saem.mmkin.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/reference/aw.html</loc> </url> <url> @@ -112,6 +121,9 @@ <loc>https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/reference/ds_mixed.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/reference/endpoints.html</loc> </url> <url> @@ -142,12 +154,18 @@ <loc>https://pkgdown.jrwb.de/mkin/reference/intervals.saem.mmkin.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/reference/llhist.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/reference/loftest.html</loc> </url> <url> <loc>https://pkgdown.jrwb.de/mkin/reference/logLik.mkinfit.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/reference/logLik.saem.mmkin.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/reference/logistic.solution.html</loc> </url> <url> @@ -211,6 +229,9 @@ <loc>https://pkgdown.jrwb.de/mkin/reference/mmkin.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/reference/multistart.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/reference/nafta.html</loc> </url> <url> @@ -229,6 +250,9 @@ <loc>https://pkgdown.jrwb.de/mkin/reference/parms.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/reference/parplot.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/reference/plot.mixed.mmkin.html</loc> </url> <url> @@ -253,6 +277,9 @@ <loc>https://pkgdown.jrwb.de/mkin/reference/print.mmkin.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/reference/read_spreadsheet.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/reference/reexports.html</loc> </url> <url> @@ -265,9 +292,15 @@ <loc>https://pkgdown.jrwb.de/mkin/reference/schaefer07_complex_case.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/reference/set_nd_nq.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/reference/status.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/reference/summary.mkinfit.html</loc> </url> <url> @@ -289,6 +322,9 @@ <loc>https://pkgdown.jrwb.de/mkin/reference/test_data_from_UBA_2014.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/reference/tex_listing.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/reference/tffm0.html</loc> </url> <url> diff --git a/inst/dataset_generation/ds_mixed.R b/inst/dataset_generation/ds_mixed.R new file mode 100644 index 00000000..f2ae6e7e --- /dev/null +++ b/inst/dataset_generation/ds_mixed.R @@ -0,0 +1,105 @@ +# Synthetic data for hierarchical kinetic models +# Refactored version of the code previously in tests/testthat/setup_script.R +# The number of datasets was 3 for FOMC, and 10 for HS in that script, now it +# is always 15 for consistency + +library(mkin) # We use mkinmod and mkinpredict +sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) +n <- 15 +log_sd <- 0.3 +err_1 = list(const = 1, prop = 0.05) +tc <- function(value) sigma_twocomp(value, err_1$const, err_1$prop) +const <- function(value) 2 + +set.seed(123456) +SFO <- mkinmod(parent = mkinsub("SFO")) +sfo_pop <- list(parent_0 = 100, k_parent = 0.03) +sfo_parms <- as.matrix(data.frame( + k_parent = rlnorm(n, log(sfo_pop$k_parent), log_sd))) +set.seed(123456) +ds_sfo <- lapply(1:n, function(i) { + ds_mean <- mkinpredict(SFO, sfo_parms[i, ], + c(parent = sfo_pop$parent_0), sampling_times) + add_err(ds_mean, tc, n = 1)[[1]] +}) +attr(ds_sfo, "pop") <- sfo_pop +attr(ds_sfo, "parms") <- sfo_parms + +set.seed(123456) +FOMC <- mkinmod(parent = mkinsub("FOMC")) +fomc_pop <- list(parent_0 = 100, alpha = 2, beta = 8) +fomc_parms <- as.matrix(data.frame( + alpha = rlnorm(n, log(fomc_pop$alpha), 0.4), + beta = rlnorm(n, log(fomc_pop$beta), 0.2))) +set.seed(123456) +ds_fomc <- lapply(1:n, function(i) { + ds_mean <- mkinpredict(FOMC, fomc_parms[i, ], + c(parent = fomc_pop$parent_0), sampling_times) + add_err(ds_mean, tc, n = 1)[[1]] +}) +attr(ds_fomc, "pop") <- fomc_pop +attr(ds_fomc, "parms") <- fomc_parms + +set.seed(123456) +DFOP <- mkinmod(parent = mkinsub("DFOP")) +dfop_pop <- list(parent_0 = 100, k1 = 0.06, k2 = 0.015, g = 0.4) +dfop_parms <- as.matrix(data.frame( + k1 = rlnorm(n, log(dfop_pop$k1), log_sd), + k2 = rlnorm(n, log(dfop_pop$k2), log_sd), + g = plogis(rnorm(n, qlogis(dfop_pop$g), log_sd)))) +set.seed(123456) +ds_dfop <- lapply(1:n, function(i) { + ds_mean <- mkinpredict(DFOP, dfop_parms[i, ], + c(parent = dfop_pop$parent_0), sampling_times) + add_err(ds_mean, tc, n = 1)[[1]] +}) +attr(ds_dfop, "pop") <- dfop_pop +attr(ds_dfop, "parms") <- dfop_parms + +set.seed(123456) +HS <- mkinmod(parent = mkinsub("HS")) +hs_pop <- list(parent_0 = 100, k1 = 0.08, k2 = 0.01, tb = 15) +hs_parms <- as.matrix(data.frame( + k1 = rlnorm(n, log(hs_pop$k1), log_sd), + k2 = rlnorm(n, log(hs_pop$k2), log_sd), + tb = rlnorm(n, log(hs_pop$tb), 0.1))) +set.seed(123456) +ds_hs <- lapply(1:n, function(i) { + ds_mean <- mkinpredict(HS, hs_parms[i, ], + c(parent = hs_pop$parent_0), sampling_times) + add_err(ds_mean, const, n = 1)[[1]] +}) +attr(ds_hs, "pop") <- hs_pop +attr(ds_hs, "parms") <- hs_parms + +set.seed(123456) +DFOP_SFO <- mkinmod( + parent = mkinsub("DFOP", "m1"), + m1 = mkinsub("SFO"), + quiet = TRUE) +dfop_sfo_pop <- list(parent_0 = 100, + k_m1 = 0.007, f_parent_to_m1 = 0.5, + k1 = 0.1, k2 = 0.02, g = 0.5) +dfop_sfo_parms <- as.matrix(data.frame( + k1 = rlnorm(n, log(dfop_sfo_pop$k1), log_sd), + k2 = rlnorm(n, log(dfop_sfo_pop$k2), log_sd), + g = plogis(rnorm(n, qlogis(dfop_sfo_pop$g), log_sd)), + f_parent_to_m1 = plogis(rnorm(n, + qlogis(dfop_sfo_pop$f_parent_to_m1), log_sd)), + k_m1 = rlnorm(n, log(dfop_sfo_pop$k_m1), log_sd))) +ds_dfop_sfo_mean <- lapply(1:n, + function(i) { + mkinpredict(DFOP_SFO, dfop_sfo_parms[i, ], + c(parent = dfop_sfo_pop$parent_0, m1 = 0), sampling_times) + } +) +set.seed(123456) +ds_dfop_sfo <- lapply(ds_dfop_sfo_mean, function(ds) { + add_err(ds, + sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2), + n = 1, secondary = "m1")[[1]] +}) +attr(ds_dfop_sfo, "pop") <- dfop_sfo_pop +attr(ds_dfop_sfo, "parms") <- dfop_sfo_parms + +#save(ds_sfo, ds_fomc, ds_dfop, ds_hs, ds_dfop_sfo, file = "data/ds_mixed.rda", version = 2) diff --git a/inst/rmarkdown/templates/hierarchical_kinetics/skeleton/header.tex b/inst/rmarkdown/templates/hierarchical_kinetics/skeleton/header.tex new file mode 100644 index 00000000..a2b7ce83 --- /dev/null +++ b/inst/rmarkdown/templates/hierarchical_kinetics/skeleton/header.tex @@ -0,0 +1 @@ +\definecolor{shadecolor}{RGB}{248,248,248} diff --git a/inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd b/inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd new file mode 100644 index 00000000..38a6bd20 --- /dev/null +++ b/inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd @@ -0,0 +1,314 @@ +--- +title: "Hierarchical kinetic modelling of degradation data" +author: +date: +output: mkin::hierarchical_kinetics +geometry: margin=2cm +--- + +\clearpage + +# Setup + +```{r packages, cache = FALSE, message = FALSE} +library(mkin) +library(knitr) +library(saemix) +library(parallel) +library(readxl) +``` + +```{r n_cores, cache = FALSE} +n_cores <- detectCores() + +if (Sys.info()["sysname"] == "Windows") { + cl <- makePSOCKcluster(n_cores) +} else { + cl <- makeForkCluster(n_cores) +} +``` + +\clearpage + +# Introduction + +This report shows hierarchical kinetic modelling for ... +The data were obtained from ... + +```{r ds} +data_path <- system.file( + "testdata", "lambda-cyhalothrin_soil_efsa_2014.xlsx", + package = "mkin") +ds <- read_spreadsheet(data_path, valid_datasets = c(1:4, 7:13)) +covariates <- attr(ds, "covariates") +``` + +The covariate data are shown below. + +```{r results = "asis", dependson = "ds", echo = FALSE} +kable(covariates, caption = "Covariate data for all datasets") +``` + +\clearpage + +The datasets with the residue time series are shown in the tables below. Please +refer to the spreadsheet for details like data sources, treatment of values +below reporting limits and time step normalisation factors. + +```{r results = "asis", dependson = "ds", echo = FALSE} +for (ds_name in names(ds)) { + print( + kable(mkin_long_to_wide(ds[[ds_name]]), + caption = paste("Dataset", ds_name), + booktabs = TRUE, row.names = FALSE)) + cat("\n\\clearpage\n") +} +``` + +# Parent only evaluations + +The following code performs separate fits of the candidate degradation models +to all datasets using constant variance and the two-component error model. + +```{r parent-sep, dependson = "ds"} +parent_deg_mods <- c("SFO", "FOMC", "DFOP", "SFORB") +errmods <- c(const = "constant variance", tc = "two-component error") +parent_sep_const <- mmkin( + parent_deg_mods, ds, + error_model = "const", + cluster = cl, quiet = TRUE) +parent_sep_tc <- update(parent_sep_const, error_model = "tc") +``` + +To select the parent model, the corresponding hierarchical fits are performed below. + +```{r parent-mhmkin, dependson = "parent-sep"} +parent_mhmkin <- mhmkin(list(parent_sep_const, parent_sep_tc), cluster = cl) +status(parent_mhmkin) |> kable() +``` + +All fits terminate without errors (status OK). The check for ill-defined +parameters shows that not all random effect parameters can be robustly +quantified. + +```{r dependson = "parent_mhmkin"} +illparms(parent_mhmkin) |> kable() +``` + +Therefore, the fits are updated, excluding random effects that were +ill-defined according to the `illparms` function. The status of the fits +is checked. + +```{r parent-mhmkin-refined} +parent_mhmkin_refined <- update(parent_mhmkin, + no_random_effect = illparms(parent_mhmkin)) +status(parent_mhmkin_refined) |> kable() +``` + +Also, it is checked if the AIC values of the refined fits are actually smaller +than the AIC values of the original fits. + +```{r dependson = "parent-mhmkin-refined"} +(AIC(parent_mhmkin_refined) < AIC(parent_mhmkin)) |> kable() +``` + +From the refined fits, the most suitable model is selected using the AIC. + +```{r parent-best, dependson = "parent-mhmkin"} +aic_parent <- AIC(parent_mhmkin_refined) +min_aic <- which(aic_parent == min(aic_parent), arr.ind = TRUE) +best_degmod_parent <- rownames(aic_parent)[min_aic[1]] +best_errmod_parent <- colnames(aic_parent)[min_aic[2]] +anova(parent_mhmkin_refined) |> kable(digits = 1) +parent_best <- parent_mhmkin_refined[[best_degmod_parent, best_errmod_parent]] +``` + +Based on the AIC, the combination of the `r best_degmod_parent` degradation +model with the error model `r errmods[best_errmod_parent]` is identified to +be most suitable for the degradation of the parent. The check below +confirms that no ill-defined parameters remain for this combined model. + +```{r dependson = "parent-best"} +illparms(parent_best) +``` + +The corresponding fit is plotted below. + +```{r dependson = "parent-best"} +plot(parent_best) +``` +The fitted parameters, together with approximate confidence +intervals are listed below. + +```{r dependson = "parent-best"} +parms(parent_best, ci = TRUE) |> kable(digits = 3) +``` + +To investigate a potential covariate influence on degradation parameters, a +covariate model is added to the hierarchical model for each of the degradation +parameters with well-defined random effects. Also, a version with covariate +models for both of them is fitted. + +```{r parent-best-pH} +parent_best_pH_1 <- update(parent_best, covariates = covariates, + covariate_models = list(log_k_lambda_free ~ pH)) +parent_best_pH_2 <- update(parent_best, covariates = covariates, + covariate_models = list(log_k_lambda_bound_free ~ pH)) +parent_best_pH_3 <- update(parent_best, covariates = covariates, + covariate_models = list(log_k_lambda_free ~ pH, log_k_lambda_bound_free ~ pH)) +``` + +The resulting models are compared. + +```{r dependson = "parent-best-pH"} +anova(parent_best, parent_best_pH_1, parent_best_pH_2, parent_best_pH_3) |> + kable(digits = 1) +``` + +The model fit with the lowest AIC is the one with a pH correlation of the +desorption rate constant `k_lambda_bound_free`. Plot and parameter listing +of this fit are shown below. Also, it is confirmed that no ill-defined +variance parameters are found. + +```{r dependson = "parent-best-pH"} +plot(parent_best_pH_2) +``` + +```{r dependson = "parent-best-pH"} +illparms(parent_best_pH_2) +parms(parent_best_pH_2, ci = TRUE) |> kable(digits = 3) +``` + +\clearpage + +# Pathway fits + +As an example of a pathway fit, a model with SFORB for the parent compound and +parallel formation of two metabolites is set up. + +```{r path-1-degmod} +if (!dir.exists("dlls")) dir.create("dlls") + +m_sforb_sfo2 = mkinmod( + lambda = mkinsub("SFORB", to = c("c_V", "c_XV")), + c_V = mkinsub("SFO"), + c_XV = mkinsub("SFO"), + name = "sforb_sfo2", + dll_dir = "dlls", + overwrite = TRUE, quiet = TRUE +) +``` + +Separate evaluations of all datasets are performed with constant variance +and using two-component error. + +```{r path-1-sep, dependson = c("path-1-degmod", "ds")} +sforb_sep_const <- mmkin(list(sforb_path = m_sforb_sfo2), ds, + cluster = cl, quiet = TRUE) +sforb_sep_tc <- update(sforb_sep_const, error_model = "tc") +``` + +The separate fits with constant variance are plotted. + +```{r dependson = "path-1-sep", fig.height = 9} +plot(mixed(sforb_sep_const)) +``` + +The two corresponding hierarchical fits, with the random effects for the parent +degradation parameters excluded as discussed above, and including the covariate +model that was identified for the parent degradation, are attempted below. + +```{r path-1, dependson = "path-1-sep"} +path_1 <- mhmkin(list(sforb_sep_const, sforb_sep_tc), + no_random_effect = c("lambda_free_0", "log_k_lambda_free_bound"), + covariates = covariates, covariate_models = list(log_k_lambda_bound_free ~ pH), + cluster = cl) +``` + +```{r dependson = "path-1"} +status(path_1) |> kable() +``` + +The status information shows that both fits were successfully completed. + +```{r dependson = "path-1"} +anova(path_1) |> kable(digits = 1) +``` +Model comparison shows that the two-component error model provides a much +better fit. + +```{r dependson = "path-1"} +illparms(path_1[["sforb_path", "tc"]]) +``` + +Two ill-defined variance components are found. Therefore, the fit is +repeated with the corresponding random effects removed. + +```{r path-1-refined, dependson = "path-1"} +path_1_refined <- update(path_1[["sforb_path", "tc"]], + no_random_effect = c("lambda_free_0", "log_k_lambda_free_bound", + "log_k_c_XV", "f_lambda_ilr_2")) +``` + +The empty output of the illparms function indicates that there are no +ill-defined parameters remaining in the refined fit. + +```{r dependson = "path-1-refined"} +illparms(path_1_refined) +``` + +Below, the refined fit is plotted and the fitted parameters are shown together +with their 95% confidence intervals. + +```{r dependson = "path-1-refined", fig.height = 9} +plot(path_1_refined) +``` + +```{r dependson = "path-1-refined", fig.height = 9} +parms(path_1_refined, ci = TRUE) |> kable(digits = 3) +``` + +\clearpage + +# Appendix + +## Listings of initial parent fits + +```{r listings-parent, results = "asis", echo = FALSE, dependson = "parent_mhmkin"} +for (deg_mod in parent_deg_mods) { + for (err_mod in c("const", "tc")) { + caption <- paste("Hierarchical", deg_mod, "fit with", errmods[err_mod]) + tex_listing(parent_mhmkin[[deg_mod, err_mod]], caption) + } +} +``` + +## Listings of refined parent fits + +```{r listings-parent-refined, results = "asis", echo = FALSE, dependson = "parent_mhmkin_refined"} +for (deg_mod in parent_deg_mods) { + for (err_mod in c("const", "tc")) { + caption <- paste("Refined hierarchical", deg_mod, "fit with", errmods[err_mod]) + tex_listing(parent_mhmkin_refined[[deg_mod, err_mod]], caption) + } +} +``` + +## Listings of pathway fits + +```{r listings-path-1, results = "asis", echo = FALSE, dependson = "path-1-refined"} +tex_listing(path_1[["sforb_path", "const"]], + caption = "Hierarchical fit of SFORB-SFO2 with constant variance") +tex_listing(path_1[["sforb_path", "tc"]], + caption = "Hierarchical fit of SFORB-SFO2 with two-component error") +tex_listing(path_1_refined, + caption = "Refined hierarchical fit of SFORB-SFO2 with two-component error") +``` + +## Session info + +```{r echo = FALSE, cache = FALSE} +parallel::stopCluster(cl) +sessionInfo() +``` + diff --git a/inst/rmarkdown/templates/hierarchical_kinetics/template.yaml b/inst/rmarkdown/templates/hierarchical_kinetics/template.yaml new file mode 100644 index 00000000..d8ab6a4d --- /dev/null +++ b/inst/rmarkdown/templates/hierarchical_kinetics/template.yaml @@ -0,0 +1,3 @@ +name: Hierarchical kinetics +description: Hierarchical kinetic modelling of degradation data +create_dir: true diff --git a/inst/testdata/cyantraniliprole_soil_efsa_2014.xlsx b/inst/testdata/cyantraniliprole_soil_efsa_2014.xlsx Binary files differnew file mode 100644 index 00000000..3252fdf1 --- /dev/null +++ b/inst/testdata/cyantraniliprole_soil_efsa_2014.xlsx diff --git a/inst/testdata/lambda-cyhalothrin_soil_efsa_2014.xlsx b/inst/testdata/lambda-cyhalothrin_soil_efsa_2014.xlsx Binary files differnew file mode 100644 index 00000000..32fc049f --- /dev/null +++ b/inst/testdata/lambda-cyhalothrin_soil_efsa_2014.xlsx diff --git a/log/build.log b/log/build.log index 245bd205..dbe0cd5b 100644 --- a/log/build.log +++ b/log/build.log @@ -5,5 +5,5 @@ * creating vignettes ... OK * checking for LF line-endings in source and make files and shell scripts * checking for empty or unneeded directories -* building ‘mkin_1.3.0.tar.gz’ +* building ‘mkin_1.2.2.tar.gz’ diff --git a/log/check.log b/log/check.log index e7d4d327..a81475d9 100644 --- a/log/check.log +++ b/log/check.log @@ -1,17 +1,34 @@ * using log directory ‘/home/jranke/git/mkin/mkin.Rcheck’ -* using R version 4.2.2 (2022-10-31) +* using R version 4.2.2 Patched (2022-11-10 r83330) * using platform: x86_64-pc-linux-gnu (64-bit) * using session charset: UTF-8 * using options ‘--no-tests --as-cran’ * checking for file ‘mkin/DESCRIPTION’ ... OK * checking extension type ... Package -* this is package ‘mkin’ version ‘1.3.0’ +* this is package ‘mkin’ version ‘1.2.2’ * package encoding: UTF-8 -* checking CRAN incoming feasibility ... Note_to_CRAN_maintainers +* checking CRAN incoming feasibility ... NOTE Maintainer: ‘Johannes Ranke <johannes.ranke@jrwb.de>’ + +Size of tarball: 6636884 bytes * checking package namespace information ... OK * checking package dependencies ... OK -* checking if this is a source package ... OK +* checking if this is a source package ... NOTE +Found the following apparent object files/libraries: + vignettes/2022_wp_1/cyan_dlls/dfop_path_1.so + vignettes/2022_wp_1/cyan_dlls/dfop_path_2.so + vignettes/2022_wp_1/cyan_dlls/fomc_path_1.so + vignettes/2022_wp_1/cyan_dlls/fomc_path_2.so + vignettes/2022_wp_1/cyan_dlls/hs_path_1.so + vignettes/2022_wp_1/cyan_dlls/sfo_path_1.so + vignettes/2022_wp_1/cyan_dlls/sforb_path_1.so + vignettes/2022_wp_1/cyan_dlls/sforb_path_2.so + vignettes/2022_wp_1/dmta_dlls/m_dfop_path.so + vignettes/2022_wp_1/dmta_dlls/m_fomc_path.so + vignettes/2022_wp_1/dmta_dlls/m_hs_path.so + vignettes/2022_wp_1/dmta_dlls/m_sfo_path.so + vignettes/2022_wp_1/dmta_dlls/m_sforb_path.so +Object files/libraries should not be included in a source package. * checking if there is a namespace ... OK * checking for executable files ... OK * checking for hidden files and directories ... OK @@ -37,18 +54,11 @@ Maintainer: ‘Johannes Ranke <johannes.ranke@jrwb.de>’ * checking whether the namespace can be unloaded cleanly ... OK * checking loading without being on the library search path ... OK * checking use of S3 registration ... OK -* checking dependencies in R code ... NOTE -Package in Depends field not imported from: ‘deSolve’ - These packages need to be imported from (in the NAMESPACE file) - for when this namespace is loaded but not attached. +* checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK -* checking foreign function calls ... NOTE -Foreign function call to a different package: - .C("unlock_solver", ..., PACKAGE = "deSolve") -See chapter ‘System and foreign language interfaces’ in the ‘Writing R -Extensions’ manual. -* checking R code for possible problems ... [17s/17s] OK +* checking foreign function calls ... OK +* checking R code for possible problems ... OK * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd line widths ... OK @@ -64,7 +74,7 @@ Extensions’ manual. * checking data for ASCII and uncompressed saves ... OK * checking installed files from ‘inst/doc’ ... OK * checking files in ‘vignettes’ ... OK -* checking examples ... [20s/20s] OK +* checking examples ... [10s/10s] OK * checking for unstated dependencies in ‘tests’ ... OK * checking tests ... SKIPPED * checking for unstated dependencies in vignettes ... OK diff --git a/log/test.log b/log/test.log index 10c0aa76..dc1b6c74 100644 --- a/log/test.log +++ b/log/test.log @@ -1,57 +1,57 @@ ℹ Testing mkin ✔ | F W S OK | Context ✔ | 5 | AIC calculation -✔ | 5 | Analytical solutions for coupled models [2.9s] +✔ | 5 | Analytical solutions for coupled models [1.6s] ✔ | 5 | Calculation of Akaike weights ✔ | 3 | Export dataset for reading into CAKE -✔ | 12 | Confidence intervals and p-values [1.0s] -✔ | 1 12 | Dimethenamid data from 2018 [29.5s] +✔ | 12 | Confidence intervals and p-values [0.4s] +✔ | 1 12 | Dimethenamid data from 2018 [12.4s] ──────────────────────────────────────────────────────────────────────────────── -Skip ('test_dmta.R:99'): Different backends get consistent results for SFO-SFO3+, dimethenamid data +Skip ('test_dmta.R:98'): Different backends get consistent results for SFO-SFO3+, dimethenamid data Reason: Fitting this ODE model with saemix takes about 15 minutes on my system ──────────────────────────────────────────────────────────────────────────────── -✔ | 14 | Error model fitting [4.9s] +✔ | 14 | Error model fitting [2.3s] ✔ | 5 | Time step normalisation -✔ | 4 | Calculation of FOCUS chi2 error levels [0.6s] -✔ | 14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [0.7s] -✔ | 4 | Test fitting the decline of metabolites from their maximum [0.3s] -✔ | 1 | Fitting the logistic model [0.2s] -✔ | 10 | Batch fitting and diagnosing hierarchical kinetic models [24.7s] -✔ | 1 12 | Nonlinear mixed-effects models [0.3s] +✔ | 4 | Calculation of FOCUS chi2 error levels [0.3s] +✔ | 14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [0.4s] +✔ | 4 | Test fitting the decline of metabolites from their maximum [0.2s] +✔ | 1 | Fitting the logistic model [0.1s] +✔ | 10 | Batch fitting and diagnosing hierarchical kinetic models [19.1s] +✔ | 1 11 | Nonlinear mixed-effects models [5.9s] ──────────────────────────────────────────────────────────────────────────────── -Skip ('test_mixed.R:74'): saemix results are reproducible for biphasic fits +Skip ('test_mixed.R:78'): saemix results are reproducible for biphasic fits Reason: Fitting with saemix takes around 10 minutes when using deSolve ──────────────────────────────────────────────────────────────────────────────── ✔ | 3 | Test dataset classes mkinds and mkindsg -✔ | 10 | Special cases of mkinfit calls [0.5s] -✔ | 3 | mkinfit features [0.7s] -✔ | 8 | mkinmod model generation and printing [0.2s] -✔ | 3 | Model predictions with mkinpredict [0.4s] -✔ | 7 | Multistart method for saem.mmkin models [36.8s] -✔ | 16 | Evaluations according to 2015 NAFTA guidance [2.5s] -✔ | 9 | Nonlinear mixed-effects models with nlme [8.8s] -✔ | 16 | Plotting [10.0s] +✔ | 10 | Special cases of mkinfit calls [0.4s] +✔ | 3 | mkinfit features [0.5s] +✔ | 8 | mkinmod model generation and printing +✔ | 3 | Model predictions with mkinpredict [0.1s] +✔ | 12 | Multistart method for saem.mmkin models [21.6s] +✔ | 16 | Evaluations according to 2015 NAFTA guidance [1.5s] +✔ | 9 | Nonlinear mixed-effects models with nlme [3.7s] +✔ | 15 | Plotting [4.6s] ✔ | 4 | Residuals extracted from mkinfit models -✔ | 1 36 | saemix parent models [66.1s] +✔ | 1 36 | saemix parent models [30.9s] ──────────────────────────────────────────────────────────────────────────────── Skip ('test_saemix_parent.R:143'): We can also use mkin solution methods for saem Reason: This still takes almost 2.5 minutes although we do not solve ODEs ──────────────────────────────────────────────────────────────────────────────── -✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [1.1s] +✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [0.6s] ✔ | 11 | Processing of residue series -✔ | 10 | Fitting the SFORB model [3.4s] +✔ | 10 | Fitting the SFORB model [1.7s] ✔ | 1 | Summaries of old mkinfit objects -✔ | 5 | Summary [0.2s] -✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [1.6s] -✔ | 9 | Hypothesis tests [6.1s] -✔ | 4 | Calculation of maximum time weighted average concentrations (TWAs) [2.2s] +✔ | 5 | Summary +✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [0.9s] +✔ | 9 | Hypothesis tests [3.3s] +✔ | 4 | Calculation of maximum time weighted average concentrations (TWAs) [0.7s] ══ Results ═════════════════════════════════════════════════════════════════════ -Duration: 206.2 s +Duration: 113.6 s ── Skipped tests ────────────────────────────────────────────────────────────── • Fitting this ODE model with saemix takes about 15 minutes on my system (1) • Fitting with saemix takes around 10 minutes when using deSolve (1) • This still takes almost 2.5 minutes although we do not solve ODEs (1) -[ FAIL 0 | WARN 0 | SKIP 3 | PASS 267 ] +[ FAIL 0 | WARN 0 | SKIP 3 | PASS 270 ] diff --git a/log/test_dev.log b/log/test_dev.log index 24905a1a..370dc5af 100644 --- a/log/test_dev.log +++ b/log/test_dev.log @@ -1,54 +1,112 @@ -ℹ Loading mkin -Loading required package: parallel ℹ Testing mkin ✔ | F W S OK | Context ✔ | 5 | AIC calculation -✔ | 5 | Analytical solutions for coupled models [14.6s] +✔ | 5 | Analytical solutions for coupled models [3.0s] ✔ | 5 | Calculation of Akaike weights -✔ | 2 | Export dataset for reading into CAKE +✔ | 3 | Export dataset for reading into CAKE ✔ | 12 | Confidence intervals and p-values [1.0s] -⠋ | 1 | Dimethenamid data from 2018 -✔ | 1 27 | Dimethenamid data from 2018 [116.1s] +✔ | 1 12 | Dimethenamid data from 2018 [31.6s] ──────────────────────────────────────────────────────────────────────────────── -Skip (test_dmta.R:164:3): Different backends get consistent results for SFO-SFO3+, dimethenamid data +Skip ('test_dmta.R:98'): Different backends get consistent results for SFO-SFO3+, dimethenamid data Reason: Fitting this ODE model with saemix takes about 15 minutes on my system ──────────────────────────────────────────────────────────────────────────────── -✔ | 14 | Error model fitting [6.6s] +✔ | 14 | Error model fitting [5.2s] ✔ | 5 | Time step normalisation -✔ | 4 | Calculation of FOCUS chi2 error levels [0.8s] -✔ | 14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [3.5s] -✔ | 4 | Test fitting the decline of metabolites from their maximum [0.6s] -✔ | 1 | Fitting the logistic model [0.3s] -⠋ | 11 | Nonlinear mixed-effects models -✔ | 1 14 | Nonlinear mixed-effects models [1.3s] -──────────────────────────────────────────────────────────────────────────────── -Skip (test_mixed.R:68:3): saemix results are reproducible for biphasic fits +✔ | 4 | Calculation of FOCUS chi2 error levels [0.5s] +✔ | 14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [0.8s] +✔ | 4 | Test fitting the decline of metabolites from their maximum [0.3s] +✔ | 1 | Fitting the logistic model [0.2s] +✔ | 10 | Batch fitting and diagnosing hierarchical kinetic models [40.7s] +✖ | 1 1 10 | Nonlinear mixed-effects models [13.2s] +──────────────────────────────────────────────────────────────────────────────── +Failure ('test_mixed.R:21'): Print methods work +Results have changed from known value recorded in 'print_dfop_saem_1.txt'. + +old[13:23] vs new[13:23] + "" + "Fitted parameters:" + " estimate lower upper" +- "parent_0 99.92 98.77 101.06" ++ "parent_0 99.96 98.82 101.11" +- "log_k1 -2.72 -2.95 -2.50" ++ "log_k1 -2.71 -2.94 -2.49" +- "log_k2 -4.14 -4.27 -4.01" ++ "log_k2 -4.14 -4.26 -4.01" +- "g_qlogis -0.35 -0.53 -0.16" ++ "g_qlogis -0.36 -0.54 -0.17" +- "a.1 0.92 0.68 1.16" ++ "a.1 0.93 0.69 1.17" + "b.1 0.05 0.04 0.06" + "SD.log_k1 0.37 0.23 0.51" +and 1 more ... + +Skip ('test_mixed.R:78'): saemix results are reproducible for biphasic fits Reason: Fitting with saemix takes around 10 minutes when using deSolve ──────────────────────────────────────────────────────────────────────────────── ✔ | 3 | Test dataset classes mkinds and mkindsg ✔ | 10 | Special cases of mkinfit calls [0.6s] -✔ | 3 | mkinfit features [1.1s] +✔ | 3 | mkinfit features [0.7s] ✔ | 8 | mkinmod model generation and printing [0.2s] ✔ | 3 | Model predictions with mkinpredict [0.3s] -✔ | 16 | Evaluations according to 2015 NAFTA guidance [2.1s] -✔ | 9 | Nonlinear mixed-effects models with nlme [8.7s] -✔ | 16 | Plotting [1.4s] +✖ | 3 9 | Multistart method for saem.mmkin models [45.8s] +──────────────────────────────────────────────────────────────────────────────── +Failure ('test_multistart.R:44'): multistart works for saem.mmkin models +Snapshot of `testcase` to 'multistart/mixed-model-fit-for-saem-object-with-mkin-transformations.svg' has changed +Run `testthat::snapshot_review('multistart/')` to review changes +Backtrace: + 1. vdiffr::expect_doppelganger(...) + at test_multistart.R:44:2 + 3. testthat::expect_snapshot_file(...) + +Failure ('test_multistart.R:55'): multistart works for saem.mmkin models +Snapshot of `testcase` to 'multistart/llhist-for-dfop-sfo-fit.svg' has changed +Run `testthat::snapshot_review('multistart/')` to review changes +Backtrace: + 1. vdiffr::expect_doppelganger("llhist for dfop sfo fit", llhist_dfop_sfo) + at test_multistart.R:55:2 + 3. testthat::expect_snapshot_file(...) + +Failure ('test_multistart.R:56'): multistart works for saem.mmkin models +Snapshot of `testcase` to 'multistart/parplot-for-dfop-sfo-fit.svg' has changed +Run `testthat::snapshot_review('multistart/')` to review changes +Backtrace: + 1. vdiffr::expect_doppelganger("parplot for dfop sfo fit", parplot_dfop_sfo) + at test_multistart.R:56:2 + 3. testthat::expect_snapshot_file(...) +──────────────────────────────────────────────────────────────────────────────── +✔ | 16 | Evaluations according to 2015 NAFTA guidance [2.3s] +✔ | 9 | Nonlinear mixed-effects models with nlme [9.0s] +✖ | 1 14 | Plotting [10.2s] +──────────────────────────────────────────────────────────────────────────────── +Failure ('test_plot.R:55'): Plotting mkinfit, mmkin and mixed model objects is reproducible +Snapshot of `testcase` to 'plot/mixed-model-fit-for-nlme-object.svg' has changed +Run `testthat::snapshot_review('plot/')` to review changes +Backtrace: + 1. vdiffr::expect_doppelganger(...) + at test_plot.R:55:2 + 3. testthat::expect_snapshot_file(...) +──────────────────────────────────────────────────────────────────────────────── ✔ | 4 | Residuals extracted from mkinfit models -✔ | 23 | saemix parent models [28.4s] -✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [12.0s] -✔ | 7 | Fitting the SFORB model [16.9s] +✔ | 1 36 | saemix parent models [71.7s] +──────────────────────────────────────────────────────────────────────────────── +Skip ('test_saemix_parent.R:143'): We can also use mkin solution methods for saem +Reason: This still takes almost 2.5 minutes although we do not solve ODEs +──────────────────────────────────────────────────────────────────────────────── +✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [1.5s] +✔ | 11 | Processing of residue series +✔ | 10 | Fitting the SFORB model [3.5s] ✔ | 1 | Summaries of old mkinfit objects -✔ | 4 | Summary [0.1s] -✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [18.1s] -✔ | 9 | Hypothesis tests [78.9s] -✔ | 2 | tffm0 +✔ | 5 | Summary [0.2s] +✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.2s] +✔ | 9 | Hypothesis tests [7.7s] ✔ | 4 | Calculation of maximum time weighted average concentrations (TWAs) [2.0s] ══ Results ═════════════════════════════════════════════════════════════════════ -Duration: 315.9 s +Duration: 255.0 s ── Skipped tests ────────────────────────────────────────────────────────────── • Fitting this ODE model with saemix takes about 15 minutes on my system (1) • Fitting with saemix takes around 10 minutes when using deSolve (1) +• This still takes almost 2.5 minutes although we do not solve ODEs (1) -[ FAIL 0 | WARN 0 | SKIP 2 | PASS 240 ] +[ FAIL 5 | WARN 0 | SKIP 3 | PASS 265 ] diff --git a/man/ds_mixed.Rd b/man/ds_mixed.Rd new file mode 100644 index 00000000..227b8e7f --- /dev/null +++ b/man/ds_mixed.Rd @@ -0,0 +1,24 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/ds_mixed.R +\name{ds_mixed} +\alias{ds_mixed} +\alias{ds_sfo} +\alias{ds_fomc} +\alias{ds_dfop} +\alias{ds_hs} +\alias{ds_dfop_sfo} +\title{Synthetic data for hierarchical kinetic degradation models} +\description{ +The R code used to create this data object is installed with this package in +the 'dataset_generation' directory. +} +\examples{ +\dontrun{ + sfo_mmkin <- mmkin("SFO", ds_sfo, quiet = TRUE, error_model = "tc", cores = 15) + sfo_saem <- saem(sfo_mmkin, no_random_effect = "parent_0") + plot(sfo_saem) +} + +# This is the code used to generate the datasets +cat(readLines(system.file("dataset_generation/ds_mixed.R", package = "mkin")), sep = "\n") +} diff --git a/man/hierarchical_kinetics.Rd b/man/hierarchical_kinetics.Rd new file mode 100644 index 00000000..2a8e211c --- /dev/null +++ b/man/hierarchical_kinetics.Rd @@ -0,0 +1,29 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/hierarchical_kinetics.R +\name{hierarchical_kinetics} +\alias{hierarchical_kinetics} +\title{Hierarchical kinetics template} +\usage{ +hierarchical_kinetics(..., keep_tex = FALSE) +} +\arguments{ +\item{...}{Arguments to \code{rmarkdown::pdf_document}} + +\item{keep_tex}{Keep the intermediate tex file used in the conversion to PDF} +} +\value{ +R Markdown output format to pass to +\code{\link[rmarkdown:render]{render}} +} +\description{ +R markdown format for setting up hierarchical kinetics based on a template +provided with the mkin package. +} +\examples{ + +\dontrun{ +library(rmarkdown) +draft("example_analysis.rmd", template = "hierarchical_kinetics", package = "mkin") +} + +} diff --git a/man/illparms.Rd b/man/illparms.Rd index 14be9c35..75eb18f0 100644 --- a/man/illparms.Rd +++ b/man/illparms.Rd @@ -22,7 +22,14 @@ illparms(object, ...) \method{print}{illparms.mmkin}(x, ...) -\method{illparms}{saem.mmkin}(object, conf.level = 0.95, random = TRUE, errmod = TRUE, ...) +\method{illparms}{saem.mmkin}( + object, + conf.level = 0.95, + random = TRUE, + errmod = TRUE, + slopes = TRUE, + ... +) \method{print}{illparms.saem.mmkin}(x, ...) @@ -43,6 +50,10 @@ illparms(object, ...) \item{errmod}{For hierarchical fits, should error model parameters be tested?} + +\item{slopes}{For hierarchical \link{saem} fits using saemix as backend, +should slope parameters in the covariate model(starting with 'beta_') be +tested?} } \value{ For \link{mkinfit} or \link{saem} objects, a character vector of parameter diff --git a/man/mhmkin.Rd b/man/mhmkin.Rd index 597fa8ac..c77f4289 100644 --- a/man/mhmkin.Rd +++ b/man/mhmkin.Rd @@ -18,7 +18,6 @@ mhmkin(objects, ...) backend = "saemix", algorithm = "saem", no_random_effect = NULL, - auto_ranef_threshold = 3, ..., cores = if (Sys.info()["sysname"] == "Windows") 1 else parallel::detectCores(), cluster = NULL @@ -42,14 +41,14 @@ supported} \item{algorithm}{The algorithm to be used for fitting (currently not used)} -\item{no_random_effect}{Default is NULL and will be passed to \link{saem}. If -you specify "auto", random effects are only included if the number -of datasets in which the parameter passed the t-test is at least 'auto_ranef_threshold'. -Beware that while this may make for convenient model reduction or even -numerical stability of the algorithm, it will likely lead to -underparameterised models.} - -\item{auto_ranef_threshold}{See 'no_random_effect.} +\item{no_random_effect}{Default is NULL and will be passed to \link{saem}. If a +character vector is supplied, it will be passed to all calls to \link{saem}, +which will exclude random effects for all matching parameters. Alternatively, +a list of character vectors or an object of class \link{illparms.mhmkin} can be +specified. They have to have the same dimensions that the return object of +the current call will have, i.e. the number of rows must match the number +of degradation models in the mmkin object(s), and the number of columns must +match the number of error models used in the mmkin object(s).} \item{cores}{The number of cores to be used for multicore processing. This is only used when the \code{cluster} argument is \code{NULL}. On Windows @@ -76,7 +75,7 @@ be indexed using the degradation model names for the first index (row index) and the error model names for the second index (column index), with class attribute 'mhmkin'. -An object of class \code{\link{mhmkin}}. +An object inheriting from \code{\link{mhmkin}}. } \description{ The name of the methods expresses that (\strong{m}ultiple) \strong{h}ierarchichal @@ -84,6 +83,43 @@ The name of the methods expresses that (\strong{m}ultiple) \strong{h}ierarchicha fitted. Our kinetic models are nonlinear, so we can use various nonlinear mixed-effects model fitting functions. } +\examples{ +\dontrun{ +# We start with separate evaluations of all the first six datasets with two +# degradation models and two error models +f_sep_const <- mmkin(c("SFO", "FOMC"), ds_fomc[1:6], cores = 2, quiet = TRUE) +f_sep_tc <- update(f_sep_const, error_model = "tc") +# The mhmkin function sets up hierarchical degradation models aka +# nonlinear mixed-effects models for all four combinations, specifying +# uncorrelated random effects for all degradation parameters +f_saem_1 <- mhmkin(list(f_sep_const, f_sep_tc), cores = 2) +status(f_saem_1) +# The 'illparms' function shows that in all hierarchical fits, at least +# one random effect is ill-defined (the confidence interval for the +# random effect expressed as standard deviation includes zero) +illparms(f_saem_1) +# Therefore we repeat the fits, excluding the ill-defined random effects +f_saem_2 <- update(f_saem_1, no_random_effect = illparms(f_saem_1)) +status(f_saem_2) +illparms(f_saem_2) +# Model comparisons show that FOMC with two-component error is preferable, +# and confirms our reduction of the default parameter model +anova(f_saem_1) +anova(f_saem_2) +# The convergence plot for the selected model looks fine +saemix::plot(f_saem_2[["FOMC", "tc"]]$so, plot.type = "convergence") +# The plot of predictions versus data shows that we have a pretty data-rich +# situation with homogeneous distribution of residuals, because we used the +# same degradation model, error model and parameter distribution model that +# was used in the data generation. +plot(f_saem_2[["FOMC", "tc"]]) +# We can specify the same parameter model reductions manually +no_ranef <- list("parent_0", "log_beta", "parent_0", c("parent_0", "log_beta")) +dim(no_ranef) <- c(2, 2) +f_saem_2m <- update(f_saem_1, no_random_effect = no_ranef) +anova(f_saem_2m) +} +} \seealso{ \code{\link{[.mhmkin}} for subsetting \link{mhmkin} objects } diff --git a/man/mkinmod.Rd b/man/mkinmod.Rd index 612c3c2b..77a4f520 100644 --- a/man/mkinmod.Rd +++ b/man/mkinmod.Rd @@ -33,7 +33,7 @@ the source compartment. Additionally, \code{\link[=mkinsub]{mkinsub()}} has an argument \code{to}, specifying names of variables to which a transfer is to be assumed in the model. If the argument \code{use_of_ff} is set to "min" -(default) and the model for the compartment is "SFO" or "SFORB", an +and the model for the compartment is "SFO" or "SFORB", an additional \code{\link[=mkinsub]{mkinsub()}} argument can be \code{sink = FALSE}, effectively fixing the flux to sink to zero. In print.mkinmod, this argument is currently not used.} diff --git a/man/parplot.Rd b/man/parplot.Rd index 37c5841d..67bf0cc1 100644 --- a/man/parplot.Rd +++ b/man/parplot.Rd @@ -10,6 +10,7 @@ parplot(object, ...) \method{parplot}{multistart.saem.mmkin}( object, llmin = -Inf, + llquant = NA, scale = c("best", "median"), lpos = "bottomleft", main = "", @@ -23,7 +24,11 @@ parplot(object, ...) \item{llmin}{The minimum likelihood of objects to be shown} -\item{scale}{By default, scale parameters using the best available fit. +\item{llquant}{Fractional value for selecting only the fits with higher +likelihoods. Overrides 'llmin'.} + +\item{scale}{By default, scale parameters using the best +available fit. If 'median', parameters are scaled using the median parameters from all fits.} \item{lpos}{Positioning of the legend.} @@ -35,6 +40,11 @@ Produces a boxplot with all parameters from the multiple runs, scaled either by the parameters of the run with the highest likelihood, or by their medians as proposed in the paper by Duchesne et al. (2021). } +\details{ +Starting values of degradation model parameters and error model parameters +are shown as green circles. The results obtained in the original run +are shown as red circles. +} \references{ Duchesne R, Guillemin A, Gandrillon O, Crauste F. Practical identifiability in the frame of nonlinear mixed effects models: the example diff --git a/man/read_spreadsheet.Rd b/man/read_spreadsheet.Rd index 147d09bf..41c32108 100644 --- a/man/read_spreadsheet.Rd +++ b/man/read_spreadsheet.Rd @@ -7,7 +7,7 @@ read_spreadsheet( path, valid_datasets = "all", - parent_only = TRUE, + parent_only = FALSE, normalize = TRUE ) } diff --git a/man/saem.Rd b/man/saem.Rd index 3a5abada..89647ff4 100644 --- a/man/saem.Rd +++ b/man/saem.Rd @@ -24,7 +24,7 @@ saem(object, ...) covariates = NULL, covariate_models = NULL, no_random_effect = NULL, - error.init = c(3, 0.1), + error.init = c(1, 1), nbiter.saemix = c(300, 100), control = list(displayProgress = FALSE, print = FALSE, nbiter.saemix = nbiter.saemix, save = FALSE, save.graphs = FALSE), diff --git a/man/summary.saem.mmkin.Rd b/man/summary.saem.mmkin.Rd index fb099899..0845d4d2 100644 --- a/man/summary.saem.mmkin.Rd +++ b/man/summary.saem.mmkin.Rd @@ -92,10 +92,21 @@ f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo, f_saem_dfop_sfo <- saem(f_mmkin_dfop_sfo) print(f_saem_dfop_sfo) illparms(f_saem_dfop_sfo) -f_saem_dfop_sfo_2 <- update(f_saem_dfop_sfo, covariance.model = diag(c(0, 0, 1, 1, 1, 0))) +f_saem_dfop_sfo_2 <- update(f_saem_dfop_sfo, + no_random_effect = c("parent_0", "log_k_m1")) illparms(f_saem_dfop_sfo_2) intervals(f_saem_dfop_sfo_2) summary(f_saem_dfop_sfo_2, data = TRUE) +# Add a correlation between random effects of g and k2 +cov_model_3 <- f_saem_dfop_sfo_2$so@model@covariance.model +cov_model_3["log_k2", "g_qlogis"] <- 1 +cov_model_3["g_qlogis", "log_k2"] <- 1 +f_saem_dfop_sfo_3 <- update(f_saem_dfop_sfo, + covariance.model = cov_model_3) +intervals(f_saem_dfop_sfo_3) +# The correlation does not improve the fit judged by AIC and BIC, although +# the likelihood is higher with the additional parameter +anova(f_saem_dfop_sfo, f_saem_dfop_sfo_2, f_saem_dfop_sfo_3) } } diff --git a/man/summary_listing.Rd b/man/summary_listing.Rd new file mode 100644 index 00000000..995ebd8d --- /dev/null +++ b/man/summary_listing.Rd @@ -0,0 +1,27 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/summary_listing.R +\name{summary_listing} +\alias{summary_listing} +\alias{tex_listing} +\alias{html_listing} +\title{Display the output of a summary function according to the output format} +\usage{ +summary_listing(object, caption = NULL, label = NULL, clearpage = TRUE) + +tex_listing(object, caption = NULL, label = NULL, clearpage = TRUE) + +html_listing(object, caption = NULL) +} +\arguments{ +\item{object}{The object for which the summary is to be listed} + +\item{caption}{An optional caption} + +\item{label}{An optional label, ignored in html output} + +\item{clearpage}{Should a new page be started after the listing? Ignored in html output} +} +\description{ +This function is intended for use in a R markdown code chunk with the chunk +option \code{results = "asis"}. +} diff --git a/man/tex_listing.Rd b/man/tex_listing.Rd deleted file mode 100644 index 2f11d211..00000000 --- a/man/tex_listing.Rd +++ /dev/null @@ -1,21 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/tex_listing.R -\name{tex_listing} -\alias{tex_listing} -\title{Wrap the output of a summary function in tex listing environment} -\usage{ -tex_listing(object, caption = NULL, label = NULL, clearpage = TRUE) -} -\arguments{ -\item{object}{The object for which the summary is to be listed} - -\item{caption}{An optional caption} - -\item{label}{An optional label} - -\item{clearpage}{Should a new page be started after the listing?} -} -\description{ -This function can be used in a R markdown code chunk with the chunk -option \code{results = "asis"}. -} diff --git 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12.00px; font-family: sans;' textLength='80.04px' lengthAdjust='spacingAndGlyphs'>Original results</text> <text x='126.22' y='470.11' style='font-size: 12.00px; font-family: sans;' textLength='75.36px' lengthAdjust='spacingAndGlyphs'>Multistart runs</text> </g> </svg> diff --git a/tests/testthat/anova_sfo_saem.txt b/tests/testthat/anova_sfo_saem.txt index 9e4bf71f..0ccd6d5c 100644 --- a/tests/testthat/anova_sfo_saem.txt +++ b/tests/testthat/anova_sfo_saem.txt @@ -1,7 +1,7 @@ -Data: 262 observations of 1 variable(s) grouped in 15 datasets +Data: 263 observations of 1 variable(s) grouped in 15 datasets npar AIC BIC Lik Chisq Df Pr(>Chisq) -sfo_saem_1_reduced 5 1310 1313 -650 -sfo_saem_1_reduced_mkin 5 1310 1313 -650 0 0 -sfo_saem_1 6 1312 1316 -650 0 1 1 -sfo_saem_1_mkin 6 1312 1316 -650 0 0 +sfo_saem_1_reduced 5 1302 1306 -646 +sfo_saem_1_reduced_mkin 5 1302 1306 -646 0 0 +sfo_saem_1 6 1304 1308 -646 0 1 1 +sfo_saem_1_mkin 6 1303 1308 -646 1 0 diff --git a/tests/testthat/illparms_hfits_synth.txt b/tests/testthat/illparms_hfits_synth.txt index affd1318..7a69645b 100644 --- a/tests/testthat/illparms_hfits_synth.txt +++ b/tests/testthat/illparms_hfits_synth.txt @@ -1,8 +1,4 @@ error -degradation const - SFO - FOMC sd(log_alpha), sd(log_beta) - error -degradation tc - SFO sd(parent_0) - FOMC sd(parent_0), sd(log_alpha), sd(log_beta) +degradation const tc + SFO sd(parent_0) sd(parent_0) + FOMC sd(log_beta) sd(parent_0), sd(log_beta) diff --git a/tests/testthat/illparms_hfits_synth_no_ranef_auto.txt b/tests/testthat/illparms_hfits_synth_no_ranef_auto.txt deleted file mode 100644 index a64ed222..00000000 --- a/tests/testthat/illparms_hfits_synth_no_ranef_auto.txt +++ /dev/null @@ -1,4 +0,0 @@ - error -degradation const tc - SFO sd(parent_0) - FOMC b.1 diff --git a/tests/testthat/print_dfop_saemix_1.txt b/tests/testthat/print_dfop_saem_1.txt index f6fda37c..bdc40065 100644 --- a/tests/testthat/print_dfop_saemix_1.txt +++ b/tests/testthat/print_dfop_saem_1.txt @@ -9,16 +9,15 @@ Data: Likelihood computed by importance sampling AIC BIC logLik - 1409 1415 -695 + 1409 1415 -696 Fitted parameters: - estimate lower upper -parent_0 100.09 98.94 101.25 -log_k1 -2.68 -2.91 -2.45 -log_k2 -4.12 -4.24 -4.00 -g_qlogis -0.41 -0.63 -0.20 -a.1 0.91 0.67 1.15 -b.1 0.05 0.04 0.06 -SD.log_k1 0.36 0.21 0.50 -SD.log_k2 0.22 0.13 0.30 -SD.g_qlogis 0.15 -0.09 0.40 + estimate lower upper +parent_0 99.92 98.77 101.06 +log_k1 -2.72 -2.95 -2.50 +log_k2 -4.14 -4.27 -4.01 +g_qlogis -0.35 -0.53 -0.16 +a.1 0.92 0.68 1.16 +b.1 0.05 0.04 0.06 +SD.log_k1 0.37 0.23 0.51 +SD.log_k2 0.23 0.14 0.31 diff --git a/tests/testthat/print_fits_synth_const.txt b/tests/testthat/print_fits_synth_const.txt index 2ea1f133..5d076d3d 100644 --- a/tests/testthat/print_fits_synth_const.txt +++ b/tests/testthat/print_fits_synth_const.txt @@ -4,8 +4,6 @@ Status of individual fits: dataset model 1 2 3 4 5 6 SFO OK OK OK OK OK OK - FOMC C C OK OK OK OK + FOMC OK OK OK OK OK OK -C: Optimisation did not converge: -false convergence (8) OK: No warnings diff --git a/tests/testthat/print_hfits_synth_no_ranef_auto.txt b/tests/testthat/print_hfits_synth_no_ranef_auto.txt deleted file mode 100644 index 9af1cbcd..00000000 --- a/tests/testthat/print_hfits_synth_no_ranef_auto.txt +++ /dev/null @@ -1,9 +0,0 @@ -<mhmkin> object -Status of individual fits: - - error -degradation const tc - SFO OK OK - FOMC OK OK - -OK: Fit terminated successfully diff --git a/tests/testthat/print_mmkin_sfo_1_mixed.txt b/tests/testthat/print_mmkin_sfo_1_mixed.txt index 33e5bf5c..c12cfe2b 100644 --- a/tests/testthat/print_mmkin_sfo_1_mixed.txt +++ b/tests/testthat/print_mmkin_sfo_1_mixed.txt @@ -3,7 +3,7 @@ Structural model: d_parent/dt = - k_parent * parent Data: -262 observations of 1 variable(s) grouped in 15 datasets +263 observations of 1 variable(s) grouped in 15 datasets <mmkin> object Status of individual fits: @@ -16,4 +16,4 @@ OK: No warnings Mean fitted parameters: parent_0 log_k_parent - 99.9 -3.3 + 100.0 -3.4 diff --git a/tests/testthat/print_multistart_biphasic.txt b/tests/testthat/print_multistart_dfop_sfo.txt index b4344f22..b4344f22 100644 --- a/tests/testthat/print_multistart_biphasic.txt +++ b/tests/testthat/print_multistart_dfop_sfo.txt diff --git a/tests/testthat/print_sfo_saem_1_reduced.txt b/tests/testthat/print_sfo_saem_1_reduced.txt index bac8848e..1c7fb588 100644 --- a/tests/testthat/print_sfo_saem_1_reduced.txt +++ b/tests/testthat/print_sfo_saem_1_reduced.txt @@ -3,16 +3,16 @@ Structural model: d_parent/dt = - k_parent * parent Data: -262 observations of 1 variable(s) grouped in 15 datasets +263 observations of 1 variable(s) grouped in 15 datasets Likelihood computed by importance sampling AIC BIC logLik - 1310 1313 -650 + 1302 1306 -646 Fitted parameters: estimate lower upper -parent_0 1e+02 99.08 1e+02 -k_parent 4e-02 0.03 4e-02 -a.1 9e-01 0.75 1e+00 +parent_0 1e+02 99.03 1e+02 +k_parent 3e-02 0.03 4e-02 +a.1 9e-01 0.71 1e+00 b.1 5e-02 0.04 5e-02 -SD.k_parent 3e-01 0.20 4e-01 +SD.k_parent 2e-01 0.14 3e-01 diff --git a/tests/testthat/setup_script.R b/tests/testthat/setup_script.R index 777c998a..4e2f76ab 100644 --- a/tests/testthat/setup_script.R +++ b/tests/testthat/setup_script.R @@ -2,17 +2,23 @@ require(mkin) require(testthat) # Per default (on my box where I set NOT_CRAN in .Rprofile) use all cores minus one +# Otherwise (CRAN check systems) use the allowed maximum of two cores if (identical(Sys.getenv("NOT_CRAN"), "true")) { n_cores <- parallel::detectCores() - 1 } else { - n_cores <- 1 + n_cores <- 2 } # Use the two available cores on travis if (Sys.getenv("TRAVIS") != "") n_cores = 2 -# On Windows we would need to make a cluster first -if (Sys.info()["sysname"] == "Windows") n_cores = 1 +# On Windows we need to make a cluster, or use one core +if (Sys.info()["sysname"] == "Windows") { + cl <- parallel::makePSOCKcluster(n_cores) + n_cores = 1 +} else { + cl <- parallel::makeForkCluster(n_cores) +} # Very simple example fits f_1_mkin_trans <- mkinfit("SFO", FOCUS_2006_A, quiet = TRUE) @@ -24,7 +30,7 @@ models <- c("SFO", "FOMC", "DFOP", "HS") fits <- suppressWarnings( # FOCUS A FOMC was, it seems, in testthat output mmkin(models, list(FOCUS_A = FOCUS_2006_A, FOCUS_C = FOCUS_2006_C, FOCUS_D = FOCUS_2006_D), - quiet = TRUE, cores = n_cores)) + quiet = TRUE, cluster = cl)) # One metabolite SFO_SFO <- mkinmod(parent = mkinsub("SFO", to = "m1"), @@ -81,112 +87,26 @@ fit_obs_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "obs", quiet = TR fit_tc_1 <- mkinfit(m_synth_SFO_lin, SFO_lin_a, error_model = "tc", quiet = TRUE, error_model_algorithm = "threestep") -# Mixed models data and fits -sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) -n <- n_biphasic <- 15 -log_sd <- 0.3 -err_1 = list(const = 1, prop = 0.05) -tc <- function(value) sigma_twocomp(value, err_1$const, err_1$prop) -const <- function(value) 2 - -set.seed(123456) -SFO <- mkinmod(parent = mkinsub("SFO")) -k_parent = rlnorm(n, log(0.03), log_sd) -set.seed(123456) -ds_sfo <- lapply(1:n, function(i) { - ds_mean <- mkinpredict(SFO, c(k_parent = k_parent[i]), - c(parent = 100), sampling_times) - add_err(ds_mean, tc, n = 1)[[1]] -}) - -set.seed(123456) -FOMC <- mkinmod(parent = mkinsub("FOMC")) -fomc_pop <- list(parent_0 = 100, alpha = 2, beta = 8) -fomc_parms <- as.matrix(data.frame( - alpha = rlnorm(n, log(fomc_pop$alpha), 0.4), - beta = rlnorm(n, log(fomc_pop$beta), 0.2))) -set.seed(123456) -ds_fomc <- lapply(1:3, function(i) { - ds_mean <- mkinpredict(FOMC, fomc_parms[i, ], - c(parent = 100), sampling_times) - add_err(ds_mean, tc, n = 1)[[1]] -}) - -set.seed(123456) -DFOP <- mkinmod(parent = mkinsub("DFOP")) -dfop_pop <- list(parent_0 = 100, k1 = 0.06, k2 = 0.015, g = 0.4) -dfop_parms <- as.matrix(data.frame( - k1 = rlnorm(n, log(dfop_pop$k1), log_sd), - k2 = rlnorm(n, log(dfop_pop$k2), log_sd), - g = plogis(rnorm(n, qlogis(dfop_pop$g), log_sd)))) -set.seed(123456) -ds_dfop <- lapply(1:n, function(i) { - ds_mean <- mkinpredict(DFOP, dfop_parms[i, ], - c(parent = dfop_pop$parent_0), sampling_times) - add_err(ds_mean, tc, n = 1)[[1]] -}) - -set.seed(123456) -HS <- mkinmod(parent = mkinsub("HS")) -hs_pop <- list(parent_0 = 100, k1 = 0.08, k2 = 0.01, tb = 15) -hs_parms <- as.matrix(data.frame( - k1 = rlnorm(n, log(hs_pop$k1), log_sd), - k2 = rlnorm(n, log(hs_pop$k2), log_sd), - tb = rlnorm(n, log(hs_pop$tb), 0.1))) -set.seed(123456) -ds_hs <- lapply(1:10, function(i) { - ds_mean <- mkinpredict(HS, hs_parms[i, ], - c(parent = hs_pop$parent_0), sampling_times) - add_err(ds_mean, const, n = 1)[[1]] -}) - -set.seed(123456) -DFOP_SFO <- mkinmod( - parent = mkinsub("DFOP", "m1"), - m1 = mkinsub("SFO"), - quiet = TRUE) -dfop_sfo_pop <- list(parent_0 = 100, - k_m1 = 0.007, f_parent_to_m1 = 0.5, - k1 = 0.1, k2 = 0.02, g = 0.5) -syn_biphasic_parms <- as.matrix(data.frame( - k1 = rlnorm(n_biphasic, log(dfop_sfo_pop$k1), log_sd), - k2 = rlnorm(n_biphasic, log(dfop_sfo_pop$k2), log_sd), - g = plogis(rnorm(n_biphasic, qlogis(dfop_sfo_pop$g), log_sd)), - f_parent_to_m1 = plogis(rnorm(n_biphasic, - qlogis(dfop_sfo_pop$f_parent_to_m1), log_sd)), - k_m1 = rlnorm(n_biphasic, log(dfop_sfo_pop$k_m1), log_sd))) -ds_biphasic_mean <- lapply(1:n_biphasic, - function(i) { - mkinpredict(DFOP_SFO, syn_biphasic_parms[i, ], - c(parent = 100, m1 = 0), sampling_times) - } -) -set.seed(123456) -ds_biphasic <- lapply(ds_biphasic_mean, function(ds) { - add_err(ds, - sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2), - n = 1, secondary = "m1")[[1]] -}) - # Mixed model fits -mmkin_sfo_1 <- mmkin("SFO", ds_sfo, quiet = TRUE, error_model = "tc", cores = n_cores) -mmkin_dfop_1 <- mmkin("DFOP", ds_dfop, quiet = TRUE, cores = n_cores, +mmkin_sfo_1 <- mmkin("SFO", ds_sfo, quiet = TRUE, error_model = "tc", cluster = cl) +mmkin_dfop_1 <- mmkin("DFOP", ds_dfop, quiet = TRUE, cluster = cl, error_model = "tc") -mmkin_biphasic <- mmkin(list("DFOP-SFO" = DFOP_SFO), ds_biphasic, quiet = TRUE, cores = n_cores, +DFOP_SFO <- mkinmod(parent = mkinsub("DFOP", "m1"), + m1 = mkinsub("SFO"), quiet = TRUE) +mmkin_dfop_sfo <- mmkin(list("DFOP-SFO" = DFOP_SFO), ds_dfop_sfo, quiet = TRUE, + cluster = cl, control = list(eval.max = 500, iter.max = 400), error_model = "tc") # nlme dfop_nlme_1 <- suppressWarnings(nlme(mmkin_dfop_1)) -nlme_biphasic <- suppressWarnings(nlme(mmkin_biphasic)) +nlme_dfop_sfo <- suppressWarnings(nlme(mmkin_dfop_sfo)) # saemix sfo_saem_1 <- saem(mmkin_sfo_1, quiet = TRUE, transformations = "saemix") sfo_saem_1_reduced <- update(sfo_saem_1, no_random_effect = "parent_0") -dfop_saemix_1 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin", - no_random_effect = "parent_0") - -saem_biphasic_m <- saem(mmkin_biphasic, transformations = "mkin", quiet = TRUE) -saem_biphasic_s <- saem(mmkin_biphasic, transformations = "saemix", quiet = TRUE) +dfop_saem_1 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin", + no_random_effect = c("parent_0", "g_qlogis")) +parallel::stopCluster(cl) diff --git a/tests/testthat/summary_hfit_sfo_tc.txt b/tests/testthat/summary_hfit_sfo_tc.txt index 41743091..0618c715 100644 --- a/tests/testthat/summary_hfit_sfo_tc.txt +++ b/tests/testthat/summary_hfit_sfo_tc.txt @@ -8,7 +8,7 @@ Equations: d_parent/dt = - k_parent * parent Data: -106 observations of 1 variable(s) grouped in 6 datasets +95 observations of 1 variable(s) grouped in 6 datasets Model predictions using solution type analytical @@ -17,26 +17,35 @@ Using 300, 100 iterations and 9 chains Variance model: Two-component variance function -Mean of starting values for individual parameters: +Starting values for degradation parameters: parent_0 log_k_parent - 101 -3 + 94 -2 Fixed degradation parameter values: None +Starting values for random effects (square root of initial entries in omega): + parent_0 log_k_parent +parent_0 4 0.0 +log_k_parent 0 0.7 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + Results: Likelihood computed by importance sampling AIC BIC logLik - 533 531 -261 + 542 541 -266 Optimised parameters: - est. lower upper -parent_0 101.02 99.58 102.46 -log_k_parent -3.32 -3.53 -3.11 -a.1 0.91 0.64 1.17 -b.1 0.05 0.04 0.06 -SD.log_k_parent 0.27 0.11 0.42 + est. lower upper +parent_0 92.52 89.11 95.9 +log_k_parent -1.66 -2.07 -1.3 +a.1 2.03 1.60 2.5 +b.1 0.09 0.07 0.1 +SD.log_k_parent 0.51 0.22 0.8 Correlation: pr_0 @@ -44,18 +53,18 @@ log_k_parent 0.1 Random effects: est. lower upper -SD.log_k_parent 0.3 0.1 0.4 +SD.log_k_parent 0.5 0.2 0.8 Variance model: est. lower upper -a.1 0.91 0.64 1.17 -b.1 0.05 0.04 0.06 +a.1 2.03 1.60 2.5 +b.1 0.09 0.07 0.1 Backtransformed parameters: - est. lower upper -parent_0 1e+02 1e+02 1e+02 -k_parent 4e-02 3e-02 4e-02 + est. lower upper +parent_0 92.5 89.1 95.9 +k_parent 0.2 0.1 0.3 Estimated disappearance times: DT50 DT90 -parent 19 64 +parent 4 12 diff --git a/tests/testthat/summary_saem_biphasic_s.txt b/tests/testthat/summary_saem_dfop_sfo_s.txt index 7c337843..6468ff17 100644 --- a/tests/testthat/summary_saem_biphasic_s.txt +++ b/tests/testthat/summary_saem_dfop_sfo_s.txt @@ -22,7 +22,7 @@ Using 300, 100 iterations and 4 chains Variance model: Two-component variance function -Mean of starting values for individual parameters: +Starting values for degradation parameters: parent_0 k_m1 f_parent_to_m1 k1 k2 1e+02 7e-03 5e-01 1e-01 2e-02 g @@ -31,6 +31,19 @@ Mean of starting values for individual parameters: Fixed degradation parameter values: None +Starting values for random effects (square root of initial entries in omega): + parent_0 k_m1 f_parent_to_m1 k1 k2 g +parent_0 101 0 0 0 0 0 +k_m1 0 1 0 0 0 0 +f_parent_to_m1 0 0 1 0 0 0 +k1 0 0 0 1 0 0 +k2 0 0 0 0 1 0 +g 0 0 0 0 0 1 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + Results: Likelihood computed by importance sampling diff --git a/tests/testthat/test_AIC.R b/tests/testthat/test_AIC.R index 57b9a673..7e97904d 100644 --- a/tests/testthat/test_AIC.R +++ b/tests/testthat/test_AIC.R @@ -8,6 +8,6 @@ test_that("The AIC is reproducible", { expect_error(AIC(fits["SFO", ]), "column object") expect_error(BIC(fits["SFO", ]), "column object") expect_equivalent(BIC(fits[, "FOCUS_C"]), - data.frame(df = c(3, 4, 5, 5), AIC = c(59.9, 45.5, 30.0, 40.2)), + data.frame(df = c(3, 4, 5, 5), BIC = c(59.9, 45.5, 30.0, 40.2)), scale = 1, tolerance = 0.1) }) diff --git a/tests/testthat/test_dmta.R b/tests/testthat/test_dmta.R index 5cfc61d2..825c6e80 100644 --- a/tests/testthat/test_dmta.R +++ b/tests/testthat/test_dmta.R @@ -11,13 +11,13 @@ names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) dmta_ds[["Elliot 1"]] <- dmta_ds[["Elliot 2"]] <- NULL -# mkin -dmta_dfop <- mmkin("DFOP", dmta_ds, quiet = TRUE, cores = n_cores) -dmta_dfop_tc <- mmkin("DFOP", dmta_ds, error_model = "tc", quiet = TRUE, cores = n_cores) - test_that("Different backends get consistent results for DFOP tc, dimethenamid data", { skip_on_cran() # Time constraints + # mkin + dmta_dfop <- mmkin("DFOP", dmta_ds, quiet = TRUE, cores = n_cores) + dmta_dfop_tc <- mmkin("DFOP", dmta_ds, error_model = "tc", quiet = TRUE, cores = n_cores) + # nlme expect_warning( nlme_dfop_tc <- nlme(dmta_dfop_tc), diff --git a/tests/testthat/test_mhmkin.R b/tests/testthat/test_mhmkin.R index 93333ac1..da063326 100644 --- a/tests/testthat/test_mhmkin.R +++ b/tests/testthat/test_mhmkin.R @@ -3,8 +3,11 @@ context("Batch fitting and diagnosing hierarchical kinetic models") test_that("Multiple hierarchical kinetic models can be fitted and diagnosed", { skip_on_cran() - fits_synth_const <- suppressWarnings( - mmkin(c("SFO", "FOMC"), ds_sfo[1:6], cores = n_cores, quiet = TRUE)) + fits_synth_const <- mmkin(c("SFO", "FOMC"), ds_fomc[1:6], cores = n_cores, quiet = TRUE) + + expect_known_output( + print(fits_synth_const), + "print_fits_synth_const.txt") fits_synth_tc <- suppressWarnings( update(fits_synth_const, error_model = "tc")) @@ -19,8 +22,8 @@ test_that("Multiple hierarchical kinetic models can be fitted and diagnosed", { print(illparms(hfits)), "illparms_hfits_synth.txt") - expect_equal(which.min(AIC(hfits)), 3) - expect_equal(which.min(BIC(hfits)), 3) + expect_equal(which.min(AIC(hfits)), 4) + expect_equal(which.min(BIC(hfits)), 4) hfit_sfo_tc <- update(hfits[["SFO", "tc"]], covariance.model = diag(c(0, 1))) @@ -38,22 +41,19 @@ test_that("Multiple hierarchical kinetic models can be fitted and diagnosed", { expect_known_output(print(test_summary, digits = 1), "summary_hfit_sfo_tc.txt") - # It depends on the platform exactly which of the datasets fail to converge - # with FOMC, because they were generated to be SFO - skip_on_travis() - - expect_known_output( - print(fits_synth_const), - "print_fits_synth_const.txt") - - hfits_no_ranef_auto <- update(hfits, no_random_effect = "auto", auto_ranef_threshold = 2) - - expect_known_output( - print(hfits_no_ranef_auto), - "print_hfits_synth_no_ranef_auto.txt") - - expect_known_output( - print(illparms(hfits_no_ranef_auto)), - "illparms_hfits_synth_no_ranef_auto.txt") - + hfits_sfo_reduced <- update(hfits, + no_random_effect = illparms(hfits)) + expect_equal( + as.character(illparms(hfits_sfo_reduced)), + rep("", 4)) + + # We can also manually set up an object specifying random effects to be + # excluded. Entries in the inital list have to be by column + no_ranef <- list("parent_0", "log_beta", "parent_0", c("parent_0", "log_beta")) + dim(no_ranef) <- c(2, 2) + + hfits_sfo_reduced_2 <- update(hfits, + no_random_effect = no_ranef) + expect_equivalent(round(anova(hfits_sfo_reduced), 0), + round(anova(hfits_sfo_reduced_2), 0)) }) diff --git a/tests/testthat/test_mixed.R b/tests/testthat/test_mixed.R index 646b6110..39a332f5 100644 --- a/tests/testthat/test_mixed.R +++ b/tests/testthat/test_mixed.R @@ -1,17 +1,24 @@ context("Nonlinear mixed-effects models") + # Round error model parameters as they are not rounded in print methods dfop_nlme_1$modelStruct$varStruct$const <- signif(dfop_nlme_1$modelStruct$varStruct$const, 3) dfop_nlme_1$modelStruct$varStruct$prop <- signif(dfop_nlme_1$modelStruct$varStruct$prop, 4) +dfop_sfo_pop <- attr(ds_dfop_sfo, "pop") + test_that("Print methods work", { expect_known_output(print(fits[, 2:3], digits = 2), "print_mmkin_parent.txt") expect_known_output(print(mixed(mmkin_sfo_1), digits = 2), "print_mmkin_sfo_1_mixed.txt") expect_known_output(print(dfop_nlme_1, digits = 1), "print_dfop_nlme_1.txt") + expect_known_output(print(sfo_saem_1_reduced, digits = 1), "print_sfo_saem_1_reduced.txt") - expect_known_output(print(dfop_saemix_1, digits = 1), "print_dfop_saemix_1.txt") + skip_on_cran() # The following test is platform dependent and fails on + # win-builder with current (18 Nov 2022) R-devel, on the Linux R-devel CRAN check systems + # and also using R-devel locally + expect_known_output(print(dfop_saem_1, digits = 1), "print_dfop_saem_1.txt") }) test_that("nlme results are reproducible to some degree", { @@ -31,17 +38,19 @@ test_that("nlme results are reproducible to some degree", { # k1 and k2 just fail the first test (lower bound of the ci), so we need to exclude it dfop_no_k1_k2 <- c("parent_0", "k_m1", "f_parent_to_m1", "g") dfop_sfo_pop_no_k1_k2 <- as.numeric(dfop_sfo_pop[dfop_no_k1_k2]) - dfop_sfo_pop <- as.numeric(dfop_sfo_pop) # to remove names - ci_dfop_sfo_n <- summary(nlme_biphasic)$confint_back + ci_dfop_sfo_n <- summary(nlme_dfop_sfo)$confint_back expect_true(all(ci_dfop_sfo_n[dfop_no_k1_k2, "lower"] < dfop_sfo_pop_no_k1_k2)) - expect_true(all(ci_dfop_sfo_n[, "upper"] > dfop_sfo_pop)) + expect_true(all(ci_dfop_sfo_n[, "upper"] > as.numeric(dfop_sfo_pop))) }) test_that("saemix results are reproducible for biphasic fits", { - test_summary <- summary(saem_biphasic_s) + skip_on_cran() + saem_dfop_sfo_s <- saem(mmkin_dfop_sfo, transformations = "saemix", quiet = TRUE) + + test_summary <- summary(saem_dfop_sfo_s) test_summary$saemixversion <- "Dummy 0.0 for testing" test_summary$mkinversion <- "Dummy 0.0 for testing" test_summary$Rversion <- "Dummy R version for testing" @@ -49,33 +58,28 @@ test_that("saemix results are reproducible for biphasic fits", { test_summary$date.summary <- "Dummy date for testing" test_summary$time <- c(elapsed = "test time 0") - expect_known_output(print(test_summary, digits = 1), "summary_saem_biphasic_s.txt") + expect_known_output(print(test_summary, digits = 1), "summary_saem_dfop_sfo_s.txt") dfop_sfo_pop <- as.numeric(dfop_sfo_pop) no_k1 <- c(1, 2, 3, 5, 6) no_k2 <- c(1, 2, 3, 4, 6) no_k1_k2 <- c(1, 2, 3, 6) - ci_dfop_sfo_s_s <- summary(saem_biphasic_s)$confint_back + ci_dfop_sfo_s_s <- summary(saem_dfop_sfo_s)$confint_back expect_true(all(ci_dfop_sfo_s_s[, "lower"] < dfop_sfo_pop)) expect_true(all(ci_dfop_sfo_s_s[, "upper"] > dfop_sfo_pop)) - # k2 is not fitted well - ci_dfop_sfo_s_m <- summary(saem_biphasic_m)$confint_back - expect_true(all(ci_dfop_sfo_s_m[no_k2, "lower"] < dfop_sfo_pop[no_k2])) - expect_true(all(ci_dfop_sfo_s_m[no_k1, "upper"] > dfop_sfo_pop[no_k1])) - # I tried to only do few iterations in routine tests as this is so slow # but then deSolve fails at some point (presumably at the switch between # the two types of iterations) - #saem_biphasic_2 <- saem(mmkin_biphasic, solution_type = "deSolve", + #saem_dfop_sfo_2 <- saem(mmkin_biphasic, solution_type = "deSolve", # control = list(nbiter.saemix = c(10, 5), nbiter.burn = 5), quiet = TRUE) skip("Fitting with saemix takes around 10 minutes when using deSolve") - saem_biphasic_2 <- saem(mmkin_biphasic, solution_type = "deSolve", quiet = TRUE) + saem_dfop_sfo_2 <- saem(mmkin_dfop_sfo, solution_type = "deSolve", quiet = TRUE) # As with the analytical solution, k1 and k2 are not fitted well - ci_dfop_sfo_s_d <- summary(saem_biphasic_2)$confint_back + ci_dfop_sfo_s_d <- summary(saem_dfop_sfo_2)$confint_back expect_true(all(ci_dfop_sfo_s_d[no_k2, "lower"] < dfop_sfo_pop[no_k2])) expect_true(all(ci_dfop_sfo_s_d[no_k1, "upper"] > dfop_sfo_pop[no_k1])) }) diff --git a/tests/testthat/test_multistart.R b/tests/testthat/test_multistart.R index c1a10d10..dda0ea23 100644 --- a/tests/testthat/test_multistart.R +++ b/tests/testthat/test_multistart.R @@ -1,41 +1,57 @@ context("Multistart method for saem.mmkin models") test_that("multistart works for saem.mmkin models", { + skip_on_cran() # Save CRAN time set.seed(123456) - saem_sfo_s_multi <- multistart(sfo_saem_1_reduced, n = 8, cores = n_cores, - no_random_effect = "parent_0") + saem_sfo_s_multi <- multistart(sfo_saem_1_reduced, n = 8, cores = n_cores) + anova_sfo <- anova(sfo_saem_1, sfo_saem_1_reduced, best(saem_sfo_s_multi), test = TRUE ) - expect_true(anova_sfo[3, "Pr(>Chisq)"] > 0.5) + expect_equal(round(anova_sfo, 1)["sfo_saem_1_reduced", "AIC"], 1302.2) + expect_equal(round(anova_sfo, 1)["best(saem_sfo_s_multi)", "AIC"], 1302.2) + expect_true(anova_sfo[3, "Pr(>Chisq)"] > 0.2) # Local: 1, win-builder: 0.4 + + saem_dfop_sfo_m <- saem(mmkin_dfop_sfo, transformations = "mkin", quiet = TRUE) + dfop_sfo_pop <- attr(ds_dfop_sfo, "pop") + + # k2 is not fitted well (compare saem_dfop_sfo_s in test_mixed.R) + ci_dfop_sfo_s_m <- summary(saem_dfop_sfo_m)$confint_back + no_k1 <- c(1, 2, 3, 5, 6) + no_k2 <- c(1, 2, 3, 4, 6) + expect_true(all(ci_dfop_sfo_s_m[no_k2, "lower"] < dfop_sfo_pop[no_k2])) + expect_true(all(ci_dfop_sfo_s_m[no_k1, "upper"] > dfop_sfo_pop[no_k1])) + - skip_on_cran() # Save CRAN time set.seed(123456) - saem_biphasic_m_multi <- multistart(saem_biphasic_m, n = 8, + saem_dfop_sfo_m_multi <- multistart(saem_dfop_sfo_m, n = 8, cores = n_cores) - expect_known_output(print(saem_biphasic_m_multi), - file = "print_multistart_biphasic.txt") + expect_known_output(print(saem_dfop_sfo_m_multi), + file = "print_multistart_dfop_sfo.txt") - anova_biphasic <- anova(saem_biphasic_m, - best(saem_biphasic_m_multi)) + anova_dfop_sfo <- anova(saem_dfop_sfo_m, + best(saem_dfop_sfo_m_multi)) # With the new starting parameters we do not improve # with multistart any more - expect_equal(anova_biphasic[2, "AIC"], anova_biphasic[1, "AIC"], + expect_equal(anova_dfop_sfo[2, "AIC"], anova_dfop_sfo[1, "AIC"], tolerance = 1e-4) skip_on_travis() # Plots are platform dependent + plot_dfop_sfo_saem_m <- function() plot(saem_dfop_sfo_m) + vdiffr::expect_doppelganger("mixed model fit for saem object with mkin transformations", plot_dfop_sfo_saem_m) + llhist_sfo <- function() llhist(saem_sfo_s_multi) parplot_sfo <- function() parplot(saem_sfo_s_multi, ylim = c(0.5, 2)) vdiffr::expect_doppelganger("llhist for sfo fit", llhist_sfo) vdiffr::expect_doppelganger("parplot for sfo fit", parplot_sfo) - llhist_biphasic <- function() llhist(saem_biphasic_m_multi) - parplot_biphasic <- function() parplot(saem_biphasic_m_multi, - ylim = c(0.5, 2)) + llhist_dfop_sfo <- function() llhist(saem_dfop_sfo_m_multi) + parplot_dfop_sfo <- function() parplot(saem_dfop_sfo_m_multi, + ylim = c(0.5, 2), llquant = 0.5) - vdiffr::expect_doppelganger("llhist for biphasic saemix fit", llhist_biphasic) - vdiffr::expect_doppelganger("parplot for biphasic saemix fit", parplot_biphasic) + vdiffr::expect_doppelganger("llhist for dfop sfo fit", llhist_dfop_sfo) + vdiffr::expect_doppelganger("parplot for dfop sfo fit", parplot_dfop_sfo) }) diff --git a/tests/testthat/test_nafta.R b/tests/testthat/test_nafta.R index 8eb052c5..b89ea342 100644 --- a/tests/testthat/test_nafta.R +++ b/tests/testthat/test_nafta.R @@ -4,6 +4,7 @@ test_that("Data for more than one compound are rejected", expect_error(nafta(FOCUS_2006_D, cores = 1))) test_that("Test data from Appendix B are correctly evaluated", { + skip_on_cran() expect_message(res <- nafta(NAFTA_SOP_Appendix_B, "aerobic aquatic", cores = 1)) # From Figure D.1 @@ -25,6 +26,7 @@ test_that("Test data from Appendix B are correctly evaluated", { }) test_that("Test data from Appendix D are correctly evaluated", { + skip_on_cran() # We are not interested in the warnings about non-normal residuals here suppressWarnings( res <- nafta(NAFTA_SOP_Appendix_D, "MRID 555555", diff --git a/tests/testthat/test_plot.R b/tests/testthat/test_plot.R index 13058c00..f5da5982 100644 --- a/tests/testthat/test_plot.R +++ b/tests/testthat/test_plot.R @@ -41,27 +41,23 @@ test_that("Plotting mkinfit, mmkin and mixed model objects is reproducible", { f_uba_mmkin <- mmkin(list("DFOP-SFO" = dfop_sfo_uba), ds_uba, quiet = TRUE, cores = n_cores) f_uba_dfop_sfo_mixed <- mixed(f_uba_mmkin["DFOP-SFO", ]) - f_uba_dfop_sfo_saem <- saem(f_uba_mmkin["DFOP-SFO", ], quiet = TRUE, transformations = "saemix") - plot_biphasic_mmkin <- function() plot(f_uba_dfop_sfo_mixed, pop_curve = TRUE) - vdiffr::expect_doppelganger("mixed model fit for mmkin object", plot_biphasic_mmkin) + plot_dfop_sfo_mmkin <- function() plot(f_uba_dfop_sfo_mixed, pop_curve = TRUE) + vdiffr::expect_doppelganger("mixed model fit for mmkin object", plot_dfop_sfo_mmkin) - plot_biphasic_saem_s <- function() plot(f_uba_dfop_sfo_saem) - vdiffr::expect_doppelganger("mixed model fit for saem object with saemix transformations", plot_biphasic_saem_s) + plot_dfop_sfo_saem_s <- function() plot(f_uba_dfop_sfo_saem) + vdiffr::expect_doppelganger("mixed model fit for saem object with saemix transformations", plot_dfop_sfo_saem_s) skip_on_travis() - plot_biphasic_nlme <- function() plot(dfop_nlme_1) - vdiffr::expect_doppelganger("mixed model fit for nlme object", plot_biphasic_nlme) + plot_dfop_sfo_nlme <- function() plot(dfop_nlme_1) + vdiffr::expect_doppelganger("mixed model fit for nlme object", plot_dfop_sfo_nlme) - #plot_biphasic_mmkin <- function() plot(mixed(mmkin_biphasic)) + #plot_dfop_sfo_mmkin <- function() plot(mixed(mmkin_dfop_sfo)) # Biphasic fits with lots of data and fits have lots of potential for differences - plot_biphasic_nlme <- function() plot(nlme_biphasic) - #plot_biphasic_saem_s <- function() plot(saem_biphasic_s) - plot_biphasic_saem_m <- function() plot(saem_biphasic_m) - - vdiffr::expect_doppelganger("mixed model fit for saem object with mkin transformations", plot_biphasic_saem_m) + plot_dfop_sfo_nlme <- function() plot(nlme_dfop_sfo) + #plot_dfop_sfo_saem_s <- function() plot(saem_dfop_sfo_s) # different results when working with eigenvalues plot_errmod_fit_D_obs_eigen <- function() plot_err(fit_D_obs_eigen, sep_obs = FALSE) diff --git a/tests/testthat/test_saemix_parent.R b/tests/testthat/test_saemix_parent.R index 20889c6c..7fbecd0c 100644 --- a/tests/testthat/test_saemix_parent.R +++ b/tests/testthat/test_saemix_parent.R @@ -15,7 +15,7 @@ test_that("Parent fits using saemix are correctly implemented", { expect_silent(print(illparms(sfo_saem_1_reduced))) # We cannot currently do the fit with completely fixed initial values - mmkin_sfo_2 <- update(mmkin_sfo_1, fixed_initials = c(parent = 100)) + mmkin_sfo_2 <- update(mmkin_sfo_1, fixed_initials = c(parent = 100), cluster = NULL, cores = n_cores) sfo_saem_3 <- expect_error(saem(mmkin_sfo_2, quiet = TRUE), "at least two parameters") # We get an error if we do not supply a suitable model specification @@ -38,11 +38,11 @@ test_that("Parent fits using saemix are correctly implemented", { s_sfo_nlme_1 <- summary(sfo_nlme_1) # Compare with input - expect_equal(round(s_sfo_saem_1$confint_ranef["SD.k_parent", "est."], 1), 0.3) - expect_equal(round(s_sfo_saem_1_mkin$confint_ranef["SD.log_k_parent", "est."], 1), 0.3) + expect_equal(round(s_sfo_saem_1$confint_ranef["SD.k_parent", "est."], 1), 0.3, tol = 0.1) + expect_equal(round(s_sfo_saem_1_mkin$confint_ranef["SD.log_k_parent", "est."], 1), 0.3, tol = 0.1) # k_parent is a bit different from input 0.03 here - expect_equal(round(s_sfo_saem_1$confint_back["k_parent", "est."], 3), 0.035) - expect_equal(round(s_sfo_saem_1_mkin$confint_back["k_parent", "est."], 3), 0.035) + expect_equal(round(s_sfo_saem_1$confint_back["k_parent", "est."], 3), 0.033) + expect_equal(round(s_sfo_saem_1_mkin$confint_back["k_parent", "est."], 3), 0.033) # But the result is pretty unanimous between methods expect_equal(round(s_sfo_saem_1_reduced$confint_back["k_parent", "est."], 3), @@ -74,7 +74,7 @@ test_that("Parent fits using saemix are correctly implemented", { mmkin_fomc_1 <- mmkin("FOMC", ds_fomc, quiet = TRUE, error_model = "tc", cores = n_cores) fomc_saem_1 <- saem(mmkin_fomc_1, quiet = TRUE, transformations = "saemix", no_random_effect = "parent_0") - fomc_pop <- as.numeric(fomc_pop) + fomc_pop <- as.numeric(attr(ds_fomc, "pop")) ci_fomc_s1 <- summary(fomc_saem_1)$confint_back expect_true(all(ci_fomc_s1[, "lower"] < fomc_pop)) expect_true(all(ci_fomc_s1[, "upper"] > fomc_pop)) @@ -87,14 +87,14 @@ test_that("Parent fits using saemix are correctly implemented", { expect_equal(endpoints(fomc_saem_1), endpoints(fomc_saem_2), tol = 0.01) # DFOP - dfop_saemix_2 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "saemix", + dfop_saem_2 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "saemix", no_random_effect = "parent_0") - s_dfop_s1 <- summary(dfop_saemix_1) # mkin transformations - s_dfop_s2 <- summary(dfop_saemix_2) # saemix transformations + s_dfop_s1 <- summary(dfop_saem_1) # mkin transformations + s_dfop_s2 <- summary(dfop_saem_2) # saemix transformations s_dfop_n <- summary(dfop_nlme_1) - dfop_pop <- as.numeric(dfop_pop) + dfop_pop <- as.numeric(attr(ds_dfop, "pop")) expect_true(all(s_dfop_s1$confint_back[, "lower"] < dfop_pop)) expect_true(all(s_dfop_s1$confint_back[, "upper"] > dfop_pop)) @@ -111,18 +111,18 @@ test_that("Parent fits using saemix are correctly implemented", { # SFORB mmkin_sforb_1 <- mmkin("SFORB", ds_dfop, quiet = TRUE, cores = n_cores) - sforb_saemix_1 <- saem(mmkin_sforb_1, quiet = TRUE, + sforb_saem_1 <- saem(mmkin_sforb_1, quiet = TRUE, no_random_effect = c("parent_free_0"), transformations = "mkin") - sforb_saemix_2 <- saem(mmkin_sforb_1, quiet = TRUE, + sforb_saem_2 <- saem(mmkin_sforb_1, quiet = TRUE, no_random_effect = c("parent_free_0"), transformations = "saemix") expect_equal( - log(endpoints(dfop_saemix_1)$distimes[1:2]), - log(endpoints(sforb_saemix_1)$distimes[1:2]), tolerance = 0.01) + log(endpoints(dfop_saem_1)$distimes[1:2]), + log(endpoints(sforb_saem_1)$distimes[1:2]), tolerance = 0.01) expect_equal( - log(endpoints(sforb_saemix_1)$distimes[1:2]), - log(endpoints(sforb_saemix_2)$distimes[1:2]), tolerance = 0.01) + log(endpoints(sforb_saem_1)$distimes[1:2]), + log(endpoints(sforb_saem_2)$distimes[1:2]), tolerance = 0.01) mmkin_hs_1 <- mmkin("HS", ds_hs, quiet = TRUE, error_model = "const", cores = n_cores) hs_saem_1 <- saem(mmkin_hs_1, quiet = TRUE, no_random_effect = "parent_0") @@ -131,7 +131,7 @@ test_that("Parent fits using saemix are correctly implemented", { expect_equal(endpoints(hs_saem_1), endpoints(hs_saem_2), tol = 0.01) ci_hs_s1 <- summary(hs_saem_1)$confint_back - hs_pop <- as.numeric(hs_pop) + hs_pop <- as.numeric(attr(ds_hs, "pop")) #expect_true(all(ci_hs_s1[, "lower"] < hs_pop)) # k1 is overestimated expect_true(all(ci_hs_s1[, "upper"] > hs_pop)) }) @@ -141,10 +141,10 @@ test_that("We can also use mkin solution methods for saem", { "saemix transformations is only supported if an analytical solution is implemented" ) skip("This still takes almost 2.5 minutes although we do not solve ODEs") - dfop_saemix_3 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin", - solution_type = "analytical", no_random_effect = "parent_0") - distimes_dfop <- endpoints(dfop_saemix_1)$distimes - distimes_dfop_analytical <- endpoints(dfop_saemix_3)$distimes + dfop_saem_3 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin", + solution_type = "analytical", no_random_effect = c("parent_0", "g_qlogis")) + distimes_dfop <- endpoints(dfop_saem_1)$distimes + distimes_dfop_analytical <- endpoints(dfop_saem_3)$distimes rel_diff <- abs(distimes_dfop_analytical - distimes_dfop) / distimes_dfop expect_true(all(rel_diff < 0.01)) }) diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html index 9f768b06..c729e3c2 100644 --- a/vignettes/FOCUS_D.html +++ b/vignettes/FOCUS_D.html @@ -31,7 +31,7 @@ document.addEventListener('DOMContentLoaded', function(e) { !function(e,t){"use strict";"object"==typeof module&&"object"==typeof 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) 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ke2C8vfjgV/Y9kkRQOJIn/xM9INZSpiBnqX0Q9GlQPpPKAyO5y+W5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W/N6l54qOynCqD3DpWQ+mpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE+7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv/8lbTIkkYpqKM9VOhp65ktYk+Q46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI+ejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5+7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu+vtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY/iOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v+6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ/qDQK+bfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam+WpHG+0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3+J1eCBay8IYZ0wQRKGAqvCuZ/UgbQPyllosq+XtfKIZOzmeJqRazpmmoP/76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t/I4Jktu0XSgifO2ozFOiwd/0SssJDn0dn4xqk4GDTTKX73/wQyBLdqgJ+Wx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy/qqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh/wkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml/R4yrzow1Q2A5G+kzo/RhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj/UMMc34IBqTKLDTp76WzL/dMjCxK7MjhiGjeYAC/kj/jY/Rde7hpSM1xChrog6yZ7OWTuD56xBJnGFE+pT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G+9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36/dBySis4m9/DR8izaLJW6bWCkVgm5T+ius3ZXq4xI+GnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te/r4dPYMCl5qtiHNTn+TPbh1jCBHH+dMJNhwNgs3nT+OhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF+uRIWyXjQMAs2chdpaKPNaB+kSezYt0+CA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6/TzoA1/ZBG9bIUVHLAbi/kdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh+epgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF+zkJHIA7PwCDk1gGVmGUZSSoPhNf+Tklauz98QofOlCIQ/tCD4dosHYPqtPCXB3agggQQIqQJsSkB+qn0rkQ1toJjON/OtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU+TQ6NIw3ej+AtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb++W6Uk4q6F7/rg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK+EfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l+wM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l+DMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg+EWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb+Mw76Qy29iQ5up/X7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa/xnsMYcIO/vEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz/Chp/Vlpj2P7jJQmQRwGnltkTV5dbF9fE3/fxoSqTROgq9wFUlbuYzYcasE0ouzBo+dDCDzxKAfhbAZYxQiHrLzV2iVexnDX/QnT1fsT/xuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR+CtGdkPwYN2o7DOw/VGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO/f9Qua+pDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD/prQ84B1pVGkIpVUAHCG+iz3Bn3qm2AVrYcYWhock4jso5+J7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk+UILT7+VoX5mdxxA5fS42gISQVi/HTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8/6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk/wdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt+Su9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ/BfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk/gc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B//lHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO+CvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd/p1gi/07h8qfwHrByuSxglc9cI4QIg2oqvC/qm0i7tjPLTgDhoWTAKDO2ONW5oe+/eKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2/j4ODUwRkqrtBBCrDsDpt8jhZdXoy/1BCqw3sSGhgGGy0a5Jw6BP/TExoCmNFYjZl248A0osgPyGEmRA+fAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd/ocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE/EZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ/Ugd/JZQK8lvAm43uDRAbyW8gZ+ZGq0EVerVGUKUSm/Idn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ+QATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm+oOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724+zUQ7+vkCpZB+pGA562hYQiDxHVWOq0oDQl/QsoiY+cuI7iWq/ZIBtHcXJ7kks+h2fCNUPA82BzjnqktNts+RLdk1VSu+tqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy/44XYXdI5noQoRcvjZ1RMPACRqYg2V1+OwOepcOknRLLFdYgTkT5UApt/JhLM3jeFYprZV+Zow2g8fP+U68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr/A1SY9dXFz4RjzoU9ExfJCmx/I9FKEGT3n2cmzl2X42L3Jh+AbQq6sA+Ss1kitoa4TAYgKHaoybHUDJ51oETdeI/9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB+Un44zExFE4vLytcu5NwpWrUxO/0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL+BDqMyaN3RJPh/anbx+Iv+qgQdAa3M9Z5JmvYlh4qop+Ho1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs+gs37sFvi0PPVvA5dnCBgILTwoKd/+DoL9F6inlM7H4rOTzD79KJgKlZO/Zgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno+gBoKVXgIL/VI8dB1O5o/R3Suez/gD7M781ShjKpIIORM/nxG+jjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4/QftDbEn+AucIr1oxrLabRj9q4ae0+fXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd+eNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz/6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT+mAUmiYbV3YQVqFVp9dorv+TsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp+xJyYLv1OsxQCZwTB4a8BZ/5EdxTBJthApqyfd9u3ifr/WILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj/qn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9+W8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4/YtowhEmTs0vrvlD/CrisnoBNDAcUi/teY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O/4DcgV/dZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk+tgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb/n7qwhvGnrHuf5bX6Vh/n3xffU3PeHdR+FA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E+YbfL0adwNtHP7dT7t7b46DVZIkzaRJOM+S6KcrzYVg+T3wSRFRQashjfU18NutrKa/7PXbtuJvpIjbgPeqd+pjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir/8tNXJ/OsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG+FZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx/GdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD/Qi/EmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4/asthNMK5UQKCOhU97oaOYNGsTah+jfCKsZnTRn5TbhFX8ghg8CBYt/BjeYYYUrtUZ5jVij/op7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM+3SW6Opll/wgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy+QbSKVJcqkia+GvDefFwMOmgnD7h81TUtMn+mRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d/QK7Cr4uoCeOQ7/8JfKT77KiDzLImESHw/0wf73QeHu74hxv7uihi4fTX+XEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo/oVH5ww5OzLFsiTPDns7fS6EURSSWd/92BxMYQ8sBaH+j+wthQPdVgDGpTfi+JQIWMD8xKqULliRH01rTeyF8x8q/GBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE+/7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV/yTDRRP8Y2ww5RO6d2D94o+6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt+kqdae76ViWe3STan69yaF9+fESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw/A1zEdjWquIsQXXGIVEH0thC5M+W9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF+RVmI8L4HUYk4x+67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8/p5qIQrEo/H+1l/0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud+tJUfdaZ4CWNijzZtlRa8+CkmO/EwHYfPZFU/hzjFWH7vnzHRMo+aF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce/+/9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs/GWJS6SwEN/ULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e+G2zor8l+YaMxSEomDdLHGcD6YVQPegTaA74H8+V4WvJkFUrjMLGLlvSZQWvi8/QA7yzQ8GPno//5SJHRP/OqKObPCo81s/+6WgLqykYpGAgQZhVDEBPXWgU/WzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M+GWn6ASobIWC+LbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg/kpf3+CnAXKiMgIE8Jk/Mhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo+RJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B+SkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE+VMd3b1fhCynD0pQNhCG6/WCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp/YbHjo++7/Wj8S4YNa+ZdqAw1hDrKWFXv9+zaXpf8ZTDSbiqsxnwN/CzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m/NCW/HILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO+5EJ7Z6bCiRoPedRZ/P0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn/LvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl+11PoFYnNv2HwAODeNRkHj+8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij+bsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur/eWHjiB7SOVdqMAVmpBvfRiebsFjger7DC+8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l+kXRZ0KLZaGsFSIdQz/HXDxf3/TE30+DgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH+RptvRMVRaahu4cySjS3P5wxAUCPkmn+rhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik/zbrTQk5KmgxCg/f45L0jywebOWUYFJQAJia7XzCV0x89rpp/f3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl+5tfUWZNwBpEPXghzbBggYCw/dhy0ntds2yeHCDKkF/YxQjNIL/F/37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD+qNOhwMlfARQUdJ2tUX+MNJqOwIciWalZsmEjt07tfa8ma4cji9sqz+Q9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe+jGDiNJQLWnfx+drTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf/bkvo8PLVBsZl152y5S8+HRDfZIMCbYZ1WDp4yrdchOJw8k6R+/2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB+M4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5/iYp3ZdrCf7fL+en+sIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv+NRiJc8JAKqqgCA/PNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN/hCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj+bYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5/zBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8/i+jHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2+JrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk+Mz7wwstg6RFZb+TZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm/7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk+9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx+whVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC+XYuqMBMUun5YezKbRKmEPwuK+CLzijPEQgfhahQswBBLfg/GBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX+RCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk/4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug/AbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM+Zu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX/pcsfwXbLze2+D+u33OGBoJyAAL3jn3RuEcdp5If8O+a4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT++tm+haBCikRUUMrMhYKZJKYoVuv/bsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV/DMUxd9uFZmBfKXMCn/SqkWJyKPnT6lq+4zBZni6fYRByJn6OK+OgPBGRAJluwGSk4wxjOOzyce/PKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO/cEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm+bddRQu5F9s0XftGEJ9JSuSk+ZachCbdU45fEqbugzTIUokwoAKvpUQF/CvLbWW5BNQFqFkJg2f30E/48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J+1pT0tLkvFVZaNzfhs/Kd3+A9YsImlO4XK4vpCo/elHQi/9gkFg07xxnuXLt21unCIpDV+bbRxb7FC6nWYTsMFF8+1LUg4JFjVt3vqbuhHmDKbgQ4e+RGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB/KVijd1ARWkFEf3yiUw1v/WaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G+ZManTqDLPjyrOse7WiiwOJCG+J0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj/6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j/N46f+S2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb/0jQbaHJ2YRK8A+ls6WMhWmpCXYG5jqapGs5/eOJErxi2/2KWVHiPellTgh/fNl/2KYPKb7DUcAg+mCOPQFCiU9Mq/WLcU1xxC8aLePFZZlE+PCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh/nFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW+oKFLvpyvTBMM69tN1Ydwv1LIEhHsC+ueVG+w+kyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw+H/AuOx+aH+tBL88H57D0MsqyiZxhOEQkF/8DR1d2hSPMj/sNOa5rxcUnBgH8ictv2J+cb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd/PjMMtQfyDNZsOPd6XcAsnBE/mRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl/XPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG/VanIvcwycVA7+BE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP/MVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX/5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c/F1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J/5wkjpkre727p5PTRX5FGrSBIfJqhJE/IS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug+oRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U/5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7/BQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN+nA7pvF78/RII5ZHA09OAiE/66MF6HQ+qVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe+hXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz+JV/4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB+K3wBP/ineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q/9uocGsx41O4IZhViw/2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY/cQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE/om7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi/caHSM3FPRGRf7dB7YC+cD2ho6oL2zGDCkjlf/DFoQVl8GS/56wur3rdV6ggtzZW60MRB3g+U1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg/gBQIZMG/YcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK/G7F3mUc3GOAKqh60zM0v34v+ELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND+/GTk6M56Ig4yMsU6LUW1EWE+fIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP/IoRIZ/F6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg/RrPD/d3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl+Mu4xf0ezqeXD2PtPDnwMPo86sbwDV+9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD/OwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d/UfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH+14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC+OA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6+vAUUBoGhY3CT2tgwehdPqU/4Q7ZLYvhRl/ogOvR9O2+wkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn/dFSWBWzQ/VYk+Gezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n/yJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET/Hh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j+DXfpi12m1RbzYLg9J2wFergEwOxFyD0/JstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT/cUP6pE/mujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB+HEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu+vqQ02+KpJBjaLt9ye1Ab+BbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC+UwkXOoAjneU/xHplMQo2cXUlrVNqJYczgYlaOEczVCs/OCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38+xsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9+b8fH6+b8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj/0Q7PFUcC8hDrxESWdfgFRm+7vvWbkEppHB4T/1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y+g3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR/Rrs/JLKXgEx+qkmeDlFOD1/yTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW+xz+5FElFxWB28VjYIGZ0Yd+5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ+lT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ+2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1+JpI9psyNYIFuJogZa0/1AhOWdlHQxdAgbwacsHqPZo8u/ngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2+RdM+MAaYaZ0Y/ADkbNCZuAyAVQa2OcXOeICmDn9Q/eFkDeFQg5MgHEDXq/tVjj+jtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2/Bc0UxvseQCO2pQ2i+Krfhu/WeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt/U0Wf+phiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7+ExseJauyqo30hs+1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j/e1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la/QCTiVelFnU6O/GCvykqS/wZJDhKN9gBtSOp/1SP5VRgJcoVj+kmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn+8p6+vBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H+gDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D/GvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P+jAgN5TB3haSifDcApp6yymEi6Ij/GsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x/pChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11/yxyw0UnGig3MFdZklN5FI/qiT65T+jOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5+bqWiAYiAv6Jsf79/VUs4cIl+n6+WOjcgB/2l5TreoAV2717JzZbQIR0W1cl/dEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW/PhoawJDrGAP0JYWHgAVUByo/bGdiv2T2EMg8gsS14/rAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq+SCcTSx5NDtbFwNlh8VhjGGDu7JG5/TAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9+ogD8Kk/W7QoRN1UWPqM4+xdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c+4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS+o3F8YVVeikw13w+OEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX++7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9/O53DYi/5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD+P8sNh6e+XYHJXT/lkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp+pT0KYV5dGtknU64JhabdqcVQbG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) 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content: ""; +.tabset-dropdown > .nav-tabs > li.active:before, .tabset-dropdown > .nav-tabs.nav-tabs-open:before { + content: "\e259"; font-family: 'Glyphicons Halflings'; display: inline-block; padding: 10px; @@ -308,16 +308,9 @@ pre code { } .tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before { - content: ""; - border: none; -} - -.tabset-dropdown > .nav-tabs.nav-tabs-open:before { - content: ""; + content: "\e258"; font-family: 'Glyphicons Halflings'; - display: inline-block; - padding: 10px; - border-right: 1px solid #ddd; + border: none; } .tabset-dropdown > .nav-tabs > li.active { @@ -362,14 +355,20 @@ pre code { -<h1 class="title toc-ignore">Example evaluation of FOCUS Example Dataset D</h1> +<h1 class="title toc-ignore">Example evaluation of FOCUS Example Dataset +D</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 31 January 2019 (rebuilt 2022-07-08)</h4> +<h4 class="date">Last change 31 January 2019 (rebuilt 2023-01-05)</h4> </div> -<p>This is just a very simple vignette showing how to fit a degradation model for a parent compound with one transformation product using <code>mkin</code>. After loading the library we look at the data. We have observed concentrations in the column named <code>value</code> at the times specified in column <code>time</code> for the two observed variables named <code>parent</code> and <code>m1</code>.</p> +<p>This is just a very simple vignette showing how to fit a degradation +model for a parent compound with one transformation product using +<code>mkin</code>. After loading the library we look at the data. We +have observed concentrations in the column named <code>value</code> at +the times specified in column <code>time</code> for the two observed +variables named <code>parent</code> and <code>m1</code>.</p> <pre class="r"><code>library(mkin, quietly = TRUE) print(FOCUS_2006_D)</code></pre> <pre><code>## name time value @@ -417,8 +416,14 @@ print(FOCUS_2006_D)</code></pre> ## 42 m1 100 33.13 ## 43 m1 120 25.15 ## 44 m1 120 33.31</code></pre> -<p>Next we specify the degradation model: The parent compound degrades with simple first-order kinetics (SFO) to one metabolite named m1, which also degrades with SFO kinetics.</p> -<p>The call to mkinmod returns a degradation model. The differential equations represented in R code can be found in the character vector <code>$diffs</code> of the <code>mkinmod</code> object. If a C compiler (gcc) is installed and functional, the differential equation model will be compiled from auto-generated C code.</p> +<p>Next we specify the degradation model: The parent compound degrades +with simple first-order kinetics (SFO) to one metabolite named m1, which +also degrades with SFO kinetics.</p> +<p>The call to mkinmod returns a degradation model. The differential +equations represented in R code can be found in the character vector +<code>$diffs</code> of the <code>mkinmod</code> object. If a C compiler +(gcc) is installed and functional, the differential equation model will +be compiled from auto-generated C code.</p> <pre class="r"><code>SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))</code></pre> <pre><code>## Temporary DLL for differentials generated and loaded</code></pre> <pre class="r"><code>print(SFO_SFO$diffs)</code></pre> @@ -426,22 +431,28 @@ print(FOCUS_2006_D)</code></pre> ## "d_parent = - k_parent * parent" ## m1 ## "d_m1 = + f_parent_to_m1 * k_parent * parent - k_m1 * m1"</code></pre> -<p>We do the fitting without progress report (<code>quiet = TRUE</code>).</p> +<p>We do the fitting without progress report +(<code>quiet = TRUE</code>).</p> <pre class="r"><code>fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)</code></pre> <pre><code>## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE): Observations with value ## of zero were removed from the data</code></pre> -<p>A plot of the fit including a residual plot for both observed variables is obtained using the <code>plot_sep</code> method for <code>mkinfit</code> objects, which shows separate graphs for all compounds and their residuals.</p> +<p>A plot of the fit including a residual plot for both observed +variables is obtained using the <code>plot_sep</code> method for +<code>mkinfit</code> objects, which shows separate graphs for all +compounds and their residuals.</p> <pre class="r"><code>plot_sep(fit, lpos = c("topright", "bottomright"))</code></pre> <p><img 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width="768" /></p> -<p>Confidence intervals for the parameter estimates are obtained using the <code>mkinparplot</code> function.</p> +<p>Confidence intervals for the parameter estimates are obtained using +the <code>mkinparplot</code> function.</p> <pre class="r"><code>mkinparplot(fit)</code></pre> <p><img src="data:image/png;base64,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width="768" /></p> -<p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p> +<p>A comprehensive report of the results is obtained using the +<code>summary</code> method for <code>mkinfit</code> objects.</p> <pre class="r"><code>summary(fit)</code></pre> -<pre><code>## mkin version used for fitting: 1.1.0 -## R version used for fitting: 4.2.1 -## Date of fit: Fri Jul 8 15:44:37 2022 -## Date of summary: Fri Jul 8 15:44:38 2022 +<pre><code>## mkin version used for fitting: 1.2.2 +## R version used for fitting: 4.2.2 +## Date of fit: Thu Jan 5 14:50:13 2023 +## Date of summary: Thu Jan 5 14:50:14 2023 ## ## Equations: ## d_parent/dt = - k_parent * parent @@ -449,7 +460,7 @@ print(FOCUS_2006_D)</code></pre> ## ## Model predictions using solution type analytical ## -## Fitted using 401 model solutions performed in 0.13 s +## Fitted using 401 model solutions performed in 0.049 s ## ## Error model: Constant variance ## @@ -492,11 +503,11 @@ print(FOCUS_2006_D)</code></pre> ## ## Parameter correlation: ## parent_0 log_k_parent log_k_m1 f_parent_qlogis sigma -## parent_0 1.000e+00 5.174e-01 -1.688e-01 -5.471e-01 -1.174e-06 -## log_k_parent 5.174e-01 1.000e+00 -3.263e-01 -5.426e-01 -8.492e-07 -## log_k_m1 -1.688e-01 -3.263e-01 1.000e+00 7.478e-01 8.220e-07 -## f_parent_qlogis -5.471e-01 -5.426e-01 7.478e-01 1.000e+00 1.307e-06 -## sigma -1.174e-06 -8.492e-07 8.220e-07 1.307e-06 1.000e+00 +## parent_0 1.000e+00 5.174e-01 -1.688e-01 -5.471e-01 -1.172e-06 +## log_k_parent 5.174e-01 1.000e+00 -3.263e-01 -5.426e-01 -8.483e-07 +## log_k_m1 -1.688e-01 -3.263e-01 1.000e+00 7.478e-01 8.205e-07 +## f_parent_qlogis -5.471e-01 -5.426e-01 7.478e-01 1.000e+00 1.305e-06 +## sigma -1.172e-06 -8.483e-07 8.205e-07 1.305e-06 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index 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) 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) 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@@ -1382,16 +1405,9 @@ pre code { } .tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before { - content: ""; - border: none; -} - -.tabset-dropdown > .nav-tabs.nav-tabs-open:before { - content: ""; + content: "\e258"; font-family: 'Glyphicons Halflings'; - display: inline-block; - padding: 10px; - border-right: 1px solid #ddd; + border: none; } .tabset-dropdown > .nav-tabs > li.active { @@ -1515,16 +1531,18 @@ div.tocify { -<h1 class="title toc-ignore">Example evaluation of FOCUS Laboratory Data L1 to L3</h1> +<h1 class="title toc-ignore">Example evaluation of FOCUS Laboratory Data +L1 to L3</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 18 May 2022 (rebuilt 2022-09-14)</h4> +<h4 class="date">Last change 18 May 2022 (rebuilt 2023-01-05)</h4> </div> <div id="laboratory-data-l1" class="section level1"> <h1>Laboratory Data L1</h1> -<p>The following code defines example dataset L1 from the FOCUS kinetics report, p. 284:</p> +<p>The following code defines example dataset L1 from the FOCUS kinetics +report, p. 284:</p> <pre class="r"><code>library("mkin", quietly = TRUE) FOCUS_2006_L1 = data.frame( t = rep(c(0, 1, 2, 3, 5, 7, 14, 21, 30), each = 2), @@ -1532,21 +1550,28 @@ FOCUS_2006_L1 = data.frame( 72.0, 71.9, 50.3, 59.4, 47.0, 45.1, 27.7, 27.3, 10.0, 10.4, 2.9, 4.0)) FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1)</code></pre> -<p>Here we use the assumptions of simple first order (SFO), the case of declining rate constant over time (FOMC) and the case of two different phases of the kinetics (DFOP). For a more detailed discussion of the models, please see the FOCUS kinetics report.</p> -<p>Since mkin version 0.9-32 (July 2014), we can use shorthand notation like <code>"SFO"</code> for parent only degradation models. The following two lines fit the model and produce the summary report of the model fit. This covers the numerical analysis given in the FOCUS report.</p> +<p>Here we use the assumptions of simple first order (SFO), the case of +declining rate constant over time (FOMC) and the case of two different +phases of the kinetics (DFOP). For a more detailed discussion of the +models, please see the FOCUS kinetics report.</p> +<p>Since mkin version 0.9-32 (July 2014), we can use shorthand notation +like <code>"SFO"</code> for parent only degradation models. The +following two lines fit the model and produce the summary report of the +model fit. This covers the numerical analysis given in the FOCUS +report.</p> <pre class="r"><code>m.L1.SFO <- mkinfit("SFO", FOCUS_2006_L1_mkin, quiet = TRUE) summary(m.L1.SFO)</code></pre> -<pre><code>## mkin version used for fitting: 1.1.2 -## R version used for fitting: 4.2.1 -## Date of fit: Wed Sep 14 22:28:35 2022 -## Date of summary: Wed Sep 14 22:28:35 2022 +<pre><code>## mkin version used for fitting: 1.2.2 +## R version used for fitting: 4.2.2 +## Date of fit: Thu Jan 5 14:50:14 2023 +## Date of summary: Thu Jan 5 14:50:14 2023 ## ## Equations: ## d_parent/dt = - k_parent * parent ## ## Model predictions using solution type analytical ## -## Fitted using 133 model solutions performed in 0.032 s +## Fitted using 133 model solutions performed in 0.011 s ## ## Error model: Constant variance ## @@ -1620,13 +1645,15 @@ summary(m.L1.SFO)</code></pre> ## 21 parent 10.4 12.416 -2.0163 ## 30 parent 2.9 5.251 -2.3513 ## 30 parent 4.0 5.251 -1.2513</code></pre> -<p>A plot of the fit is obtained with the plot function for mkinfit objects.</p> +<p>A plot of the fit is obtained with the plot function for mkinfit +objects.</p> <pre class="r"><code>plot(m.L1.SFO, show_errmin = TRUE, main = "FOCUS L1 - SFO")</code></pre> <p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAkAAAAHgCAMAAAB6sCJ3AAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////isF19AAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO2dB1gURxuA51QUKQoCh4CoIIq9gR1jL1hiRDTqD8YkaqKxoLFHjUaTqDFRMAUSiYqKDbtiwRoN0cSoQROJYk1iA8GCdG7+3St4nHewe7Pl9vZ7n4e9LbPzfcD73LbZGYQBgAAkdgKAtAGBACJAIIAIEAggAgQCiACBACJAIIAIEAggAgQCiACBACJAIIAIEAggAgQCiACBACJAIIAIEAggAgQCiACBACJAIIAIEAggAgQCiACBACJAIIAIEAggAgQCiACBACJAIIAIEAggAgQCiACBACJAIIAI+Qj0PtJxklo6OKqFffOwLSrNNlVMkIutz5g/6PnpCCXQnwkIRVAfmQvaKR2aDv+1pJ6lCEXr1/vtmL+NxtPfsazYEkeeAr0Yr50Nfk5vutdbs1RxJTYU6KartuQSXT0GAl20VUvxCqV2LCO21JGVQBOi1dzDoxByGDI31AGht+hNnRGqMXJ+aEWE9hgK1AWh0HXrJ1VFFc5p69EX6PqBqQ7IuECldiwjttSRlUD7tbNJCDWgjzt/10coCeOtCDW+Qy3uQ6ilgUB5Nqg7vbAZoU+1O+sLNKDksGRI6R1Nx5Y8shQoEKHj6pkjCAViHITQYfXimy1bF5QW6BFCbYuohbyNG3/R7qwvUNSYMe2NC1R6R9OxJY+sBJq0hubPxwpUT7uyLlJkYBfkqleu9CHMFyG/GdvT9E54Dc6BVpk4hJXa0XRsySMrgTR8fZE6gdWupM6eL2RQXxZ65UoLlOyl3scp9LhuO0OBSu1oMjaHv59IyFGgCwj1067sh9Cv1NGmo145g8v47PgxzW3o3VZrt5ch0L6JFN9oF/R3NBmbh99TYGQlkPY8JOPlYaQeQg+xM/LULBXk5akMBaLJ//ld6tKpWLNQhkDzaEf66m3U7Wg6tuSRo0A4QPdPP4lQK4w7IKS5Rm+FbF7gxQjF0guxCC3EV5cu1Zw8U8ebfzU7MxOo9I6mY0seWQp0GKGGN6jP6w0ROojxeoQ6ZFGL+xWoE8Y7EBpIFxqI0Hb8N0Jd6IspVVtkX6TZmdk5UOkdTceWPLIUCIchVG3ExyMcEQrD6n8yqjt1xf8qogrUv/SJN+XT/HnU11KtLPpLCXX4ekdMd4SGafelBApTX1GteUEvmjqJLrWj6diSR54CPeulPant9YxevNNZs2TzJb10ykWz5HqKWrjtoS3Z9Il236UljyXUxzRTApXasYzYUkeeAmFV/Mhm9s1Gxmvv7xSvfqOuY+t3r2uWsmZ1cHHpMCtLvZD9RVCtKr491hbqdmUoUKkdy4otceQjEMALIBBABAgEEAECAUSAQAARIBBABAgEEAECAUSAQBQ3Bo2KLr8UYAwQiGLGBdxZ7BykCghEkaP6Z3A5RU6+wWRVCYXppRYzb7FPSiKAQDTbBt0vpwQTgWbW8vhQNxuG8ff+7mE5t9v7R2I8IZWLLC0SEIhi/zSDB5sF2p+XSwwEOhyYk+V/SLPJNQyfr3v/ab+F7x0qav307wkcJ2xByFygafXP4e0N3+4fqmmbE+XjN634zMjh8+gfvMTXZ4Z6CattUW/sswvjwFPqWUOBfj6I8ZCd9FxWm6gwvPMLjGPDZn51q3HOqP+E/r2EQ+YC4R19cuqUNAw82eJR9vAlZ6qnYvrnkP/TvKA4eo7e9IZm49pwfMP3hHpWLVBwHTUpmsr6DcijP0fs3aIW8nGbXU/f63nktznC/16CIXeBVPXfGliyML9u165N+p/pgjH9M/MjjL8ZTc9hWiDNxifuBZ8t0sy+elS7t70FLePGsVgt0Ea/jerVoVl7+0zP4/93EQW5C4SXVb7+cv5zjHOfnhlACUT9zKAOXdHh9BymBdJsxIMOt7ypmVUL1NtLDd2vx85kjL98n5p5w6Oum32/4vDXNWfmh5dntL4z/0vhfzVBkL1Aw11efjdcbPgor1+iTqCDjZ7nd15bIpBmI97ctZu2nOE30OpuT5/2iFSl0k0QqW+gnT00Z+aq13Pv9FBtmyvULyQwchfojMfAjS+Xout5TcI6gfBiP98Pi0sE0mzE2XbrtOUMBSqeUqfWB4V56DZWCzS1soODw1vU7BqMP27U+YFQv5HAyFyg4oC1JwIKyy8HmELmAsUGqnBYrZtipyFhZC4QQAoIBBABAgFEgEAAESAQQAQIBBABAgFEgEAAESAQQAQIBBABAgFEcCzQtADAKghi2nqAY4Harz0PWAP1/xBJoF/KLwNIgOYgEEACCAQQAQIBRIBAABEgEEAECAQQAQIBRIBAABEWItCx12u+9jW8diVBLEOgJfXjB4/q2w0Mkh4WIdAt5SN82+V+7zXcxgAEwCIEih1FTaZM2TyU2xiAAFiEQFETqUm6a2ywqdJA2aSME4DrRkNbhEDHWtPTj5vN5DaGfFjTKYZ3ArYZDW0RAhW3m5WHcXwFaxhbVhTWvMt/jKEWLBB+MFTZrX6LiIGmSgNlI3uBMP73eGpRXt0z3AaRDSBQ4aFV2zJwXEfrGF9WcGQvUErToCkh7huLAxK4jSIX5C5Qbt1N1PQvj/PHffO5DSMT5C7Qnh7qjy8m4D5fcxtGJshdoMjJ6o+DfXGK+1Nu48gDuQu0UfMMIzYM49EfcRtHHshdoHS3K9T0RYvd1OW8y11uA8kCuQuEtyjnJqzyU491NOcdbgPJAtkLhG/PHTLplHrumed5jLOSrxVxG8+6AYH0iO6W/YFzB7+Gx7kNaNXwKNDF37QzkhGoqFnH8CcYH3a/xG1Ea4ZHgcJ0A31IRiAcV+k5/bFqFLcRrZlXBDrSycEzvOzLEdWrJwn5BfpLmgbGEhRoZ81J/Wq3nvdzS24jWjOGAsX47n3+3xIP00N7pKGDtRUNl1Fzz6b42fouVmFcc+t059t4XYBdkx+p1bW+6YmqD32M2yOEnqh3kY5AB30q/nDn93dqt+M2ojVjIFCuyzX647O3TO6QhqqO3T1d8THGIc4r9k5B8ZRAgYO250bZLEiMUHxLCeT8v5PLK32AHw/p+0DzhFs6At1RDI6gPvxf4zaiNUMLpFpf0nhwTh31xxLXl+0JT5feIQ0NoaYzHJ/hIdHUjP9MSqDmKpztsphaGqukBGpJaTOwkyQPYYmNvB2Tfh3lA99AjKEFKoooab38urv6Y6T9ywbN8aV3SEN0u4fL6Bw1fX5xjc10SqBZGJ9DyRkZGRvQXVyLHv93entJCrRz8M9NqgYuTIZzIMYYHMKeVE9Xr311CHsdaYhuu5dFaXSmucIz2JMW6CuMtyINKbjWF1iyAl33yCtsug+vfIvbiNaM4Un03KDr9MXIb8ZLY9030F/o50ybCQ+p/wYt0CqMj6NH2gK1VmDJCoTfHJGZVG+3ewq3Ea0ZQ4GKV7rUrRFQRgPhNDSMms61yzqC0qiTbg+tQI+qxFKr5/eQuEDZU5za2rmf4jagVfPqjUTVzcdl7ZCGbCccmFNhLr5tM/T0nnb2/TLUAuFZlZckzlBE6gn0dpPzmhtGEhII46fnTrje4zagVcP6TnQa2tXfqcFn1JXWFn/7tvvWOy/QCKRa0dSuMX1dViLQCR9HTRMtwQXKT88sY2u53bvMGs02oIwxQ6DzbGMIK9DduT4KhKr4zTZ1M7RcgZ55nmMVUdZYnUAX7LzHR26Ii5ro62zikWj5HUzFBUCLDqZYnUBdg3M0M4UjehovUb5Aqi4xbELKGtYCFd5m/fqLoAJVK3m/67ST8RIMuri7rExnE1POWF2DssAJurmFJh5IMOkjcfI4NjHljNUJlKAIjk2+mno2LqSiiXdNmQj01Ossm6AyxuoEwvu7qZ+pKLonmijAqJfWdQHFrKLKFusTCOPMK0lJKYZ3Qy8N1WG/iUEdqiD6PPqPofVaTSvzvqrssUaBaIqvF5Re8XibDtvvmFTwh/sjvNMjOu1CRO1/zYgvG6xPoP2hA9biGBdku8REhy0OPzCq58PRRZ7qZ8oLBPgTSRerE2gbatPPZoLD4oPzbGKNl2Ao0Iu6a5qoZ27UYRNfblidQK3GY/wd+pyam9PKeAmGAuGdtdsk93WvN+6yG5v4cmPNIP4HR+0ppEB2BzBOVzd6O2hvvARTgXBfG89ND9LmO3diE19u/CTA8MyBxh9+8CNQ/WUYn0XrqLnIhsZLMBbohqLNA4x/cerFJj4gGPwI9LltxCKvNp5HMva4mOiuhbFAF939nNs2rhPtzSY+IBj8CFQ4z9Pt/YIxCKH+L4yXYCzQ2TbN1p77s/CBkk18QDB4vQ90Nc5kmx7GAj2tftgzE+P4PmbEB/hHrCatjAXCs7uOGouTPH/iNj7AEZYvUNFSJxs3/8Pchge4wvIFwrgg2jeX2+gAZ0hBIIxDFnAbHeAMaQj0n9tlbsMDXCENgfAPbaGFvWUiEYFUvVdwGx/gCIkIhG8pr3GbAMANUhEIr+oCQ0FZIpIRqDiIURtGQGAkIxD+y+0OtykAXCAdgfBnvemDWEqgQ/XXTHc/CgiMhAQqaheD8fZKjZcsqGtzuvzigCBISCB81TUNO4ZTM6pu0DrIUpCSQHh595sKda8NdxTwxqGFICmBijtPr6iZQc+5TQcwF0kJhP92Uaibdm+pxG02gNlISyD8laPrFYx/th/MbTaA2UhMoOLO7pVqOFXqlMdtNoDZSEwg/E/NNdM/gt4TLQepCYTXNYHWiZaE5ATCQ2dxmQdAiPQESvc8yWUiABnSEwjv9X3GYSIAGRIUCL/3FmdpAKRIUaAXDePLLwQIgxQFwr8rb3OWCECGJAXCyzrDSxoWgjQFKu7xGVeJAGRIUyD8rzvcjbYMJCoQ3lfvCTeJAGRIVSD8wVBO8gAIkaxAeS3XcJIIQIZkBcLX3JimDvCIdAXCsU1yOEgEIEPCAuGR75HXARAiZYGeN4ojrwQgQ8oC4ctuf3FQC0CCpAXCPzQ10Q01IBTSFgiPglGgREbiAsFpkNhIXCB8RZnCTUWAeUhdILy5PjwUExP+BMpPzyxjK2cC4YkDoe87EeFJoLtzfRQIVfGbbaorKO4EKghaxlVVAHv4EeiCnff4yA1xURN9nS8ZL8GdQPi+1xHO6gLYwo9AXYO1j6kKR/Q0XoJDgfAxz7vcVQawgx+BqiXo5k47GS/BpUB4eSt4rCoW/AgUOEE3t7Cd8RKcCoSHh3NZG8ACfgRKUATHJl9NPRsXUjHBeAluBcoJWK3+3PXB6FXZXFYMlAdPV2H7uyEaRfdEEwW4FQjf9jhJadS3zeq14d5wZ1FIeLsPlHklKSnlscHKkzWctSg4vvY+Qp1ILxxKd725oSW3NQNlwuud6KxrBp2pqjJ12HP7DYTxitYvmvyunqsLw7IICE8CbeiQgh8MQMhxlYkCHB/CKMYMUT5UzwRBL+QCwo9AkajzA9zb67uDMyqZeFrOvUD5nb1O0J+Fyn+4rhowDT8C+czB+AGiR+qe3cp4Ce4Fwvdr+GViXDwnmPOaAdPwI5DLDowvI/qCOtHReAkeBMLn7VzHTG3R9SH3NQMm4UegN/rl40LHo9TcHEFuJGrZ6rniK+j/Tlj4EeiqsunyE0s8fzwxx2az8RK8CIQXBUAbaYHh6Srs2vga6juJTbaaKMCPQKrwUBiFRVh4uw9UcPOXA8mmB4bjRyCc32UeL/UCppB8k1YDHvlCK3tBsTaB8J/ucBotJFYnED4F72kIiVkC3b1MHJc/gfBmn/u81Q0YYoZAh2oihHtEkcXlUSD8SQC0CRIM9gJtqDRuA8LzFTFEcfkUCL87EHoBFgr2AjWeijOohRlNieLyKlBBH+g5SCjYC2SXqBYo0Z4oLq8C4RcdFvJZPfAS9gK1/lgt0CfNieLyKxBO9/+a1/oBHewFWmuzOBk9iq38FVFcngXCNzx38BsA0GDGVViUC0KoymyyN9L5FgifV57hOQJAY859oOxzW4+nE8blXSB8xB26ARYA67sTXcIuj795jwGwF2iADqK4AgiE19W7x38QucNeoNE0bygrTCSKK4RA+NOmhi+mAVxj7iEsu3cborh8CVT6FvS0jvBQg2fMPgc6iR6RxOVFoLQhTlXb7NNboXqvey4PcYCXmC3QuqpE1/F8CJTqvjIz/1D97/RWFY/sk8d9IOAl7AXaqGahR1eiuHwINFx9b/O6i74yRaEhhdxHAkpgL5CtGrv2V4ni8iGQt2Ys59a/6a/M6/UOdMLJI9Z0H8hT805z27Ol1mZ3fh8M4g9rEmiQus7/nAyuvLJfmwQG8QZLgVz1IIrLh0C/u28pximBnxquf9o2gvtggAaWAkVHR69y8Ir46iN/7/1EcXm5jP+1o6PS6/tXv26etJnGQzSAhv0h7P0u9FVOUV+yRn883UjMNt6zwuOWc3gJB5ghkJem28zdnkRxBXmU8ZKswA8FjScfzBBI0+lYVG2iuAILhLNaB9St3PR7eHOea9gLNLY6/bBgf3WLPISZ5EUj5Tv5ZzuNEzaqDGAv0POuqEbTGqg72WNKoQVaOSwzMEL1otYVYcNaP+bcBzq+NOKLnwjjCi3QkG34ScexxeO+K78owAZrupFYFoN2UZdoPUaMJ3yhFjCEpUBrkvAaHURxhRbok/H439/u93aELoA5hqVADsOxkw6iuEILlOFe1yOwmo/ydWgfxC1yOYT959bKvq5D644juj8TNrC1Y6ZAqQcJWxsLLdBH03DOrSJVixNTAknfSAL0YS/Q3V7T8Y6KyNXEWJYMEVqggXvUH5Mj8ceN/hU2tHXDXqA3PHfjln1u9rL813r0GRqv/ngnBuPP66UJG9uqYS9Qje/xPZSE492I4gotUHQ/+iF9Zs3r1DTG84Kwwa0Z9gI5bcE/2ubifQ5EcYUWKL9DyNl7e5tqHsrvdjssbHQrhr1AvTsebTYIZ/cPJIortEA4b1mgR/fd2oWTSlMdoAMsYS/QJXdU7QJuUHlfWaXLRXCBSnPJC/oP4gYzLuNzzlOX8NsIhwUUWSB8q+FkaNrBBfx185ufnlnGVrEFwpmdQ+GmNAfw1M3v3bk+CoSq+M02NVqG6ALhvDe7ZYmdgxXATze/F+y8x0duiIua6Ots4n6j+ALh4slNb4udg/Thp5vfrsE5mpnCET2Nl7AAgTCOqfWr2ClIHn66+a2WoJs7beKhvUUIhA8pTYyHBzCFn25+Ayfo5hYKOeQley54rxA7BYnDTze/CYrg2OSrqWfjQiomGC9hIQLhf1q+my92DpKGp25+93dTj3ip6J5oooClCISzQzrBMM8E8NbNb+aVpKQUw0ZDhTd02FuKQFj1sS+8qmE+bAV6fnjrHeqP/t+fJ/sz2Gm3QTd4p+v5aqmwnE2W/LJRuVfsFKQLS4H+8qYOTOP/qE0fnpjsdNTUFos5hNFcqDMLnmuYCUuBBrpsSNns5tVhy5FzZb1YuDNMA+oRFma8hEUJhB91GfhU7BwkCkuB3JZRk2WovDu4B+xQQHsK1Kh9e+MlLEsgnPdOC9NDlANlwFIgRLej2VH+GxqpAW3pcQYkcgijWe1+UOwUJAlbgejbOrsZvOJTMMvhO0kJhM94wYmQGfAlEMYnvYPvS0kg/Khbf3g8zxr+BMJZb7pKSiBcMLE+2btKcoStQDa2trY2SN1VdPn7bI4wOeCSJQpEnd25rxI7BanBUqB5ehDFtUyBcGqz8Bdi5yAt5PJuPFOy/9c8VewcJAUIZMj3bnFipyAlQKBX+KtZOIwyxhgQ6FWyRzdOETsHyQACGWODWxQMr8EMEMgotzr1ggF7GQECGadgthd0wMAEEMgUx2tPzhE7BwkAApnkSXij38XOwfIBgcpgk3JpUfml5A0IVBZ3unck7IXE6gGBykQV47oUWgmVBQhUDn936HFL7BwsGRCoPIqWun4DdxVNAgKVT1qXTibbNckeEIgBRV+6roTLMeOAQIy43rUdPF81CgjEDNV691l5YidhiYBATLkX4n9C7BwsEBCIOfvqDH1UfimZAQKx4HmE+1q4oi8NCMSK39t2ZtJHtowAgdihWu8xGTry0AMEYkv6O7XiTRzHHuzbLrs+PkAg9iQHvGb0Feglyt79PMbIbAAFEMgMimNqTnh1zNjvGtR3q+0eOF6EhEQEBDKLzInKyAKDdXXcj2J8ubmtvF4qA4HMJLV/g22l1yjUXWLfqMj0L2odgEBms9+/b6lLegU9HitWVfxJnHREAgQyn4Io97H3Xy7aRNDT+Mo3xMpHFEAgErJmuCx6rlvoWXP43iNTXDzkda8aBCLj1kiPb7Vn0ylur/fuNthjp7gJCQ0IRMqFXvW3ab50rg9TVu+ZLHI6QgMCkZMU0OaI2DmIBgjEBUnNO50UOweRAIE4oWhtnQHnxU5CFEAgjiiI8eopx1fpQSDOyF3lESqvu9A0IBCHvPjSI+Si2EkIDAjEKTkrPQfJ61wIBOKYnNW1+8rpaRgIxDn5sfVfS5TN8wz+BMpPzyxjqxULRF3Ux7dosalQ7CyEgSeB7s71USBUxW+2qTbCVi0QxqrELj6rZdGyjB+BLth5j4/cEBc10dfZxABKVi4QxS8hbvMeiJ0E//AjUNdgbQenhSN6Gi9h/QJhfH2889g/xU6Cb/gRqFqCbu60k/ESchAI4/RPPPocsu7zaX4ECpygm1vYzngJeQiEcd66lo2+s+aTIX4ESlAExyZfTT0bF1IxwXgJuQhEcSLE5UPrbebK01XY/m6IRtE90UQBGQmE8e2ZrgMPWmlnr7zdB8q8kpSUYvj23f0YHVW+ZVmftMmJDfBb8eq7iFYAXwIV3dS84ptb6kr2r3E6bGQ3uu25UU7hZ8ROgnv4EahwflVUdSbdL+U6E/vJ6hCm5fGX/k1XW9vQ9PwItLzShwlTK72NQaDSqI6PcAo/ZVXX9fwI1GAONdmI9oBAr5Cxson/UisazI4fgewP0NMwn1wQyAg/j3EesCNf7Cw4gh+B2kyjpw+VH4BARsle10U52ToanvEjUBSafCQP4wMVR80CgYxzc5Ffk2X/iJ0FOTxdxi+uhtKoj32eCAQyher0OJfuP0q9w0W+7gPl31D36154LNr4dhCIJm9niFPoDkl3igdNWkUmc02PGqMPSbf5IggkPvdWdXB775hEhwMCgSyC218EKt8/KkWHQCBL4cayNm5jD0nu9hAIZEHcWtGpRtiOF2KnwQoQyLK4922vaq/HSmhQIBDI4siKH+bUaelfYqfBEBDIEsk7NKF2valHpXBCBAJZKheXtHcaEmvxz+1BIAvm0fo3a7Sa85NF32UEgSybojMfBTiHxNwWOw+TgECWz8ON4e4NJux6InYeRgGBJIHq4he9HTvMO2F5I4+DQJIh99jcdo69P//Fsk6JQCBJ8WRPRIvq/ZefsxyJQCDJkbFjcvNqfT87Yxl3iUAgSZE2rm3PZbkYP949tY1jl/kHxW/PCAJJiQPK0DdHdG+i6Tvw+eF5XR1bfLDplqgpgUASotDdKyTyU//6U0vWFJz9aoinR8iKMzli5QQCSYjzVX+kpvkd3Euvvr1pUhu7tpM2XBMjJxBIQqy3VX/srPzqppwzXw6rUyN4wd77r27jFRBIQmy3UR+pvq9iYvuDvfP7ungPXnIoXbicQCAJcc0u/DnGlz0allXoxraZPZxqv/HJfmEe5INAUqJdO2W/TjXrm3jX7iWq61tn9XKp2Xf2tr/57hgNBJISt1q2HzncaxJDKe7uXTTY16Hde9+e5vE5LAgkKYp2ffwluzHJnp7+elx7h7oD58SnFPCREQgkB4rTdn4ytFHVJsM+2ZHK8WM0EEg+5F+KnzPIr2qzYQu3/sFZuxAQSG7kXoyfF9qkql//6d+fekheHQgkTwqv7Vn2bkcXp7Zhi7f8/oygIhBI1mQk/zgntIVdzc5vf7r1d7Ou1UAgAON/Tvwwa0hLR9d2I+b9ePIuq1tHIBBQwsOfNywaFeRZpUGf95dtPcfsBAkEAgzJvXrgm+mhga52jfuN/3zTmX/L/EYCgQBTPL+y7+uZwzt6Vq4TFDbnm72XMowVAoGA8si/eSru0/EDmteo2iAwaLPBWA0gEMCY2108O/ZwX1dqHQgEMKbLnEKMr9Y5pr8OBAKYctm3GOdhvC5EfyUIBDBl5+D1Das6jTjSVH8lCAQw5bh34NmirM9cSo2jDAIBTLlT4Qg1VTUI0l8JAgFM2R+gXHh0S5fA1vorQSCAKXv7347oMWz9b630V4JAAFP+c1E/r180Xn8lCAQwZuprV3H+1zVv6a8DgQDGFEV5uNr3Sy21jj+B8tMzy9gKAkmTdMNXO3gS6O5cHwVCVfxm3zRRAASyEvgR6IKd9/jIDXFRE32dLxkvAQJZCfwI1DVY219N4YiexkuAQFYCPwJVS9DNnXbSX//HUB02K9nUB1gs/AgUOEE3t7DUg5PH23TUPs6mPsBi4UegBEVwbPLV1LNxIRUTjJdo/wub+gCLhaersP3dEI2ie6KJAiCQlcDbfaDMK0lJKY9NbgaBrASx7kSDQFYCCAQQAQIBRIBAABEgEEAECAQQIZpAs2NKM2hkGAeEBnNRS1jPEVzUMqwPF7WE9RzORS1v9uailrDeyw3+b14iCfTDOANs6zXiAK9qXNTSqIovF7V4O3BRS6OqdbmopbY9F7U0su9t8H+bzHTEKY4FeoX6nAwVsnUYF7Uw/14uk70DuaiFo6P90R5c1IK7mf0MEwRiDQikDwjEGhBIHxCINSCQPiAQa0AgfUAg1oBA+oBArAGB9AGBWAMC6cO3QI1MvUHGip0juagFt/6Ti1oODuaiFhz0Gxe1nOrDRS2412lz9+RbIKN9yLKmkJuh1bhJpiir/DIMeKziohaV6dahbDA/Gb4FAqwcEAggAgQCiACBACJAIIAIEAggAgQCiACBACJAIIAIEAggAgQCiACBACL4FSihTfVuF4lr2anulGgMWSVJe7jISFMLYUKr2zn4f1FImoyuFuTvJasAAAP7SURBVLJknkf42Adsx+Ynw6tA+xXjt/e1v0tazQplNMVJojqK207nICNtLWQJLUbT9s+uNJ8wmZJayJIJc1yZ+DZKMj8ZXgXq1hfjHO+5pNVMIG409c83r6HpxBmV1EKUUH61ydT0w6pFRMm8rIUomSeKOIxV/qPN/8vwKVAmiqWm7/uQ1tN3HGkNiUFBttOJM9LVQpbQDUQPz5WAbhIlU1ILWTLXuqZR09eGmf+X4VOgKyiZmkYq8gnradCntX2LGMJK/KZzkZG6FrKE8tLyqOnUqrlEyZTUQv7XUSVW3WD+X4ZPgY6iq9R0A0onq6a4skvk7jFoBVkt6n89cUbqWjhIaGOlGRz8eehaiJOJtEURBH8ZPgVKQvQIMHGorNFZGJC/5QY1HVWtmKgW9b+eOCN1LcQJPQpHowuJk9HUQpzMzV0zbFaYnwyfAqWgs9Q0qgonle1EaUT7q//1xBlpDmGECR1Q+uwmT0ZbC2kyNFPrmZ8MnwI9pk/x8aR6hNU8PE+3+N6LHhDVov7XE2ekroUwoQMVJ+Ri4mR0tZAls70fvfca9MLsZHi9jO8egnGh7yzCWpLQJmr6Xm2yWjTfHaQZaQ+EJAkVeoZr50iSKamFLJlE9Cs1fbeW+cnwKlBixUVnRjqTvhlW1E65+MDkCtvJatEIRJqRuhayhI6hmetocomSKamFLJmCjr7rDk2vEG3+X4bfRxnb21bvQf4oIyeioWPHg4SVaM9eCDPS1EKUUDTS8IAomZe1kP11no3xdwikv8PMTQYepgJEgEAAESAQQAQIBBABAgFEgEAAESAQQAQIBBABAgFEgEAAESAQQAQIBBABAgFEgEAAESAQQAQIBBABAgFEgEAAESAQQAQIBBABAgFEgEAAESAQQAQIBBABAgFEgEAAESBQeYRqXyJGS7DDGrGTsTxAoPL4JSEhwbsTNbmKhyeJnYzlAQIxoelwsTOwWEAgJmgFcqIOYbVWdrDz/fbeAKc6dJ8W6wLsmvwobm4iAwIxQV8gmwXHBihqrUxsa/scR9ksSIxQfCtydqICAjFBX6ChGKeiKRjvR5ezXRZTa8cqxU1OXEAgJugLtAzjIhRPW3TpHErOyMjYgIgHc5AwIBAT9AVaQQuUoBZoq/YCP0Xk9MQEBGKCCYGOo0ciJyY+IBATTAj0qAo9wMR84rFgpAwIxAQTAuFZlZckzlBEipydqIBATDAlkGpFU7vG0SInJy4gEEAECAQQAQIBRIBAABEgEEAECAQQAQIBRIBAABEgEEAECAQQAQIBRIBAABEgEEAECAQQAQIBRIBAABEgEEAECAQQAQIBRIBAABEgEEAECAQQAQIBRIBAABEgEEDE/wFycCHypSvetAAAAABJRU5ErkJggg==" /><!-- --></p> <p>The residual plot can be easily obtained by</p> <pre class="r"><code>mkinresplot(m.L1.SFO, ylab = "Observed", xlab = "Time")</code></pre> <p><img src="data:image/png;base64,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" /><!-- --></p> -<p>For comparison, the FOMC model is fitted as well, and the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is checked.</p> +<p>For comparison, the FOMC model is fitted as well, and the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is +checked.</p> <pre class="r"><code>m.L1.FOMC <- mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet=TRUE)</code></pre> <pre><code>## Warning in mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation did not converge: ## false convergence (8)</code></pre> @@ -1637,17 +1664,17 @@ summary(m.L1.SFO)</code></pre> <pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> <pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is ## doubtful</code></pre> -<pre><code>## mkin version used for fitting: 1.1.2 -## R version used for fitting: 4.2.1 -## Date of fit: Wed Sep 14 22:28:35 2022 -## Date of summary: Wed Sep 14 22:28:35 2022 +<pre><code>## mkin version used for fitting: 1.2.2 +## R version used for fitting: 4.2.2 +## Date of fit: Thu Jan 5 14:50:15 2023 +## Date of summary: Thu Jan 5 14:50:15 2023 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 369 model solutions performed in 0.081 s +## Fitted using 369 model solutions performed in 0.025 s ## ## Error model: Constant variance ## @@ -1710,13 +1737,40 @@ summary(m.L1.SFO)</code></pre> ## Estimated disappearance times: ## DT50 DT90 DT50back ## parent 7.25 24.08 7.25</code></pre> -<p>We get a warning that the default optimisation algorithm <code>Port</code> did not converge, which is an indication that the model is overparameterised, <em>i.e.</em> contains too many parameters that are ill-defined as a consequence.</p> -<p>And in fact, due to the higher number of parameters, and the lower number of degrees of freedom of the fit, the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is actually higher for the FOMC model (3.6%) than for the SFO model (3.4%). Additionally, the parameters <code>log_alpha</code> and <code>log_beta</code> internally fitted in the model have excessive confidence intervals, that span more than 25 orders of magnitude (!) when backtransformed to the scale of <code>alpha</code> and <code>beta</code>. Also, the t-test for significant difference from zero does not indicate such a significant difference, with p-values greater than 0.1, and finally, the parameter correlation of <code>log_alpha</code> and <code>log_beta</code> is 1.000, clearly indicating that the model is overparameterised.</p> -<p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error levels reported in Appendix 3 and Appendix 7 to the FOCUS kinetics report are rounded to integer percentages and partly deviate by one percentage point from the results calculated by mkin. The reason for this is not known. However, mkin gives the same <span class="math inline"><em>χ</em><sup>2</sup></span> error levels as the kinfit package and the calculation routines of the kinfit package have been extensively compared to the results obtained by the KinGUI software, as documented in the kinfit package vignette. KinGUI was the first widely used standard package in this field. Also, the calculation of <span class="math inline"><em>χ</em><sup>2</sup></span> error levels was compared with KinGUII, CAKE and DegKin manager in a project sponsored by the German Umweltbundesamt <span class="citation">(Ranke 2014)</span>.</p> +<p>We get a warning that the default optimisation algorithm +<code>Port</code> did not converge, which is an indication that the +model is overparameterised, <em>i.e.</em> contains too many parameters +that are ill-defined as a consequence.</p> +<p>And in fact, due to the higher number of parameters, and the lower +number of degrees of freedom of the fit, the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is +actually higher for the FOMC model (3.6%) than for the SFO model (3.4%). +Additionally, the parameters <code>log_alpha</code> and +<code>log_beta</code> internally fitted in the model have excessive +confidence intervals, that span more than 25 orders of magnitude (!) +when backtransformed to the scale of <code>alpha</code> and +<code>beta</code>. Also, the t-test for significant difference from zero +does not indicate such a significant difference, with p-values greater +than 0.1, and finally, the parameter correlation of +<code>log_alpha</code> and <code>log_beta</code> is 1.000, clearly +indicating that the model is overparameterised.</p> +<p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error +levels reported in Appendix 3 and Appendix 7 to the FOCUS kinetics +report are rounded to integer percentages and partly deviate by one +percentage point from the results calculated by mkin. The reason for +this is not known. However, mkin gives the same <span class="math inline"><em>χ</em><sup>2</sup></span> error levels as the +kinfit package and the calculation routines of the kinfit package have +been extensively compared to the results obtained by the KinGUI +software, as documented in the kinfit package vignette. KinGUI was the +first widely used standard package in this field. Also, the calculation +of <span class="math inline"><em>χ</em><sup>2</sup></span> error levels +was compared with KinGUII, CAKE and DegKin manager in a project +sponsored by the German Umweltbundesamt <span class="citation">(Ranke +2014)</span>.</p> </div> <div id="laboratory-data-l2" class="section level1"> <h1>Laboratory Data L2</h1> -<p>The following code defines example dataset L2 from the FOCUS kinetics report, p. 287:</p> +<p>The following code defines example dataset L2 from the FOCUS kinetics +report, p. 287:</p> <pre class="r"><code>FOCUS_2006_L2 = data.frame( t = rep(c(0, 1, 3, 7, 14, 28), each = 2), parent = c(96.1, 91.8, 41.4, 38.7, @@ -1725,34 +1779,48 @@ summary(m.L1.SFO)</code></pre> FOCUS_2006_L2_mkin <- mkin_wide_to_long(FOCUS_2006_L2)</code></pre> <div id="sfo-fit-for-l2" class="section level2"> <h2>SFO fit for L2</h2> -<p>Again, the SFO model is fitted and the result is plotted. The residual plot can be obtained simply by adding the argument <code>show_residuals</code> to the plot command.</p> +<p>Again, the SFO model is fitted and the result is plotted. The +residual plot can be obtained simply by adding the argument +<code>show_residuals</code> to the plot command.</p> <pre class="r"><code>m.L2.SFO <- mkinfit("SFO", FOCUS_2006_L2_mkin, quiet=TRUE) plot(m.L2.SFO, show_residuals = TRUE, show_errmin = TRUE, main = "FOCUS L2 - SFO")</code></pre> <p><img src="data:image/png;base64,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" /><!-- --></p> -<p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 14% suggests that the model does not fit very well. This is also obvious from the plots of the fit, in which we have included the residual plot.</p> -<p>In the FOCUS kinetics report, it is stated that there is no apparent systematic error observed from the residual plot up to the measured DT90 (approximately at day 5), and there is an underestimation beyond that point.</p> -<p>We may add that it is difficult to judge the random nature of the residuals just from the three samplings at days 0, 1 and 3. Also, it is not clear <em>a priori</em> why a consistent underestimation after the approximate DT90 should be irrelevant. However, this can be rationalised by the fact that the FOCUS fate models generally only implement SFO kinetics.</p> +<p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error +level of 14% suggests that the model does not fit very well. This is +also obvious from the plots of the fit, in which we have included the +residual plot.</p> +<p>In the FOCUS kinetics report, it is stated that there is no apparent +systematic error observed from the residual plot up to the measured DT90 +(approximately at day 5), and there is an underestimation beyond that +point.</p> +<p>We may add that it is difficult to judge the random nature of the +residuals just from the three samplings at days 0, 1 and 3. Also, it is +not clear <em>a priori</em> why a consistent underestimation after the +approximate DT90 should be irrelevant. However, this can be rationalised +by the fact that the FOCUS fate models generally only implement SFO +kinetics.</p> </div> <div id="fomc-fit-for-l2" class="section level2"> <h2>FOMC fit for L2</h2> -<p>For comparison, the FOMC model is fitted as well, and the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is checked.</p> +<p>For comparison, the FOMC model is fitted as well, and the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is +checked.</p> <pre class="r"><code>m.L2.FOMC <- mkinfit("FOMC", FOCUS_2006_L2_mkin, quiet = TRUE) plot(m.L2.FOMC, show_residuals = TRUE, main = "FOCUS L2 - FOMC")</code></pre> <p><img 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" /><!-- --></p> <pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 1.1.2 -## R version used for fitting: 4.2.1 -## Date of fit: Wed Sep 14 22:28:35 2022 -## Date of summary: Wed Sep 14 22:28:35 2022 +<pre><code>## mkin version used for fitting: 1.2.2 +## R version used for fitting: 4.2.2 +## Date of fit: Thu Jan 5 14:50:15 2023 +## Date of summary: Thu Jan 5 14:50:15 2023 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 239 model solutions performed in 0.049 s +## Fitted using 239 model solutions performed in 0.015 s ## ## Error model: Constant variance ## @@ -1810,7 +1878,10 @@ plot(m.L2.FOMC, show_residuals = TRUE, ## Estimated disappearance times: ## DT50 DT90 DT50back ## parent 0.8092 5.356 1.612</code></pre> -<p>The error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes is much lower in this case. Therefore, the FOMC model provides a better description of the data, as less experimental error has to be assumed in order to explain the data.</p> +<p>The error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes is much +lower in this case. Therefore, the FOMC model provides a better +description of the data, as less experimental error has to be assumed in +order to explain the data.</p> </div> <div id="dfop-fit-for-l2" class="section level2"> <h2>DFOP fit for L2</h2> @@ -1820,10 +1891,10 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, main = "FOCUS L2 - DFOP")</code></pre> <p><img src="data:image/png;base64,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" /><!-- --></p> <pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 1.1.2 -## R version used for fitting: 4.2.1 -## Date of fit: Wed Sep 14 22:28:36 2022 -## Date of summary: Wed Sep 14 22:28:36 2022 +<pre><code>## mkin version used for fitting: 1.2.2 +## R version used for fitting: 4.2.2 +## Date of fit: Thu Jan 5 14:50:15 2023 +## Date of summary: Thu Jan 5 14:50:15 2023 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -1832,7 +1903,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, ## ## Model predictions using solution type analytical ## -## Fitted using 581 model solutions performed in 0.135 s +## Fitted using 581 model solutions performed in 0.04 s ## ## Error model: Constant variance ## @@ -1895,35 +1966,48 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, ## Estimated disappearance times: ## DT50 DT90 DT50back DT50_k1 DT50_k2 ## parent 0.5335 5.311 1.599 0.03084 2.058</code></pre> -<p>Here, the DFOP model is clearly the best-fit model for dataset L2 based on the chi^2 error level criterion.</p> +<p>Here, the DFOP model is clearly the best-fit model for dataset L2 +based on the chi^2 error level criterion.</p> </div> </div> <div id="laboratory-data-l3" class="section level1"> <h1>Laboratory Data L3</h1> -<p>The following code defines example dataset L3 from the FOCUS kinetics report, p. 290.</p> +<p>The following code defines example dataset L3 from the FOCUS kinetics +report, p. 290.</p> <pre class="r"><code>FOCUS_2006_L3 = data.frame( t = c(0, 3, 7, 14, 30, 60, 91, 120), parent = c(97.8, 60, 51, 43, 35, 22, 15, 12)) FOCUS_2006_L3_mkin <- mkin_wide_to_long(FOCUS_2006_L3)</code></pre> <div id="fit-multiple-models" class="section level2"> <h2>Fit multiple models</h2> -<p>As of mkin version 0.9-39 (June 2015), we can fit several models to one or more datasets in one call to the function <code>mmkin</code>. The datasets have to be passed in a list, in this case a named list holding only the L3 dataset prepared above.</p> +<p>As of mkin version 0.9-39 (June 2015), we can fit several models to +one or more datasets in one call to the function <code>mmkin</code>. The +datasets have to be passed in a list, in this case a named list holding +only the L3 dataset prepared above.</p> <pre class="r"><code># Only use one core here, not to offend the CRAN checks mm.L3 <- mmkin(c("SFO", "FOMC", "DFOP"), cores = 1, list("FOCUS L3" = FOCUS_2006_L3_mkin), quiet = TRUE) plot(mm.L3)</code></pre> <p><img src="data:image/png;base64,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" /><!-- --></p> -<p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 21% as well as the plot suggest that the SFO model does not fit very well. The FOMC model performs better, with an error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes of 7%. Fitting the four parameter DFOP model further reduces the <span class="math inline"><em>χ</em><sup>2</sup></span> error level considerably.</p> +<p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error +level of 21% as well as the plot suggest that the SFO model does not fit +very well. The FOMC model performs better, with an error level at which +the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes +of 7%. Fitting the four parameter DFOP model further reduces the <span class="math inline"><em>χ</em><sup>2</sup></span> error level +considerably.</p> </div> <div id="accessing-mmkin-objects" class="section level2"> <h2>Accessing mmkin objects</h2> -<p>The objects returned by mmkin are arranged like a matrix, with models as a row index and datasets as a column index.</p> -<p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.</p> +<p>The objects returned by mmkin are arranged like a matrix, with models +as a row index and datasets as a column index.</p> +<p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, +using square brackets for indexing which will result in the use of the +summary and plot functions working on mkinfit objects.</p> <pre class="r"><code>summary(mm.L3[["DFOP", 1]])</code></pre> -<pre><code>## mkin version used for fitting: 1.1.2 -## R version used for fitting: 4.2.1 -## Date of fit: Wed Sep 14 22:28:36 2022 -## Date of summary: Wed Sep 14 22:28:36 2022 +<pre><code>## mkin version used for fitting: 1.2.2 +## R version used for fitting: 4.2.2 +## Date of fit: Thu Jan 5 14:50:15 2023 +## Date of summary: Thu Jan 5 14:50:15 2023 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -1932,7 +2016,7 @@ plot(mm.L3)</code></pre> ## ## Model predictions using solution type analytical ## -## Fitted using 376 model solutions performed in 0.081 s +## Fitted using 376 model solutions performed in 0.024 s ## ## Error model: Constant variance ## @@ -2008,37 +2092,51 @@ plot(mm.L3)</code></pre> ## 120 parent 12.0 10.19 1.81395</code></pre> <pre class="r"><code>plot(mm.L3[["DFOP", 1]], show_errmin = TRUE)</code></pre> <p><img src="data:image/png;base64,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" /><!-- --></p> -<p>Here, a look to the model plot, the confidence intervals of the parameters and the correlation matrix suggest that the parameter estimates are reliable, and the DFOP model can be used as the best-fit model based on the <span class="math inline"><em>χ</em><sup>2</sup></span> error level criterion for laboratory data L3.</p> -<p>This is also an example where the standard t-test for the parameter <code>g_ilr</code> is misleading, as it tests for a significant difference from zero. In this case, zero appears to be the correct value for this parameter, and the confidence interval for the backtransformed parameter <code>g</code> is quite narrow.</p> +<p>Here, a look to the model plot, the confidence intervals of the +parameters and the correlation matrix suggest that the parameter +estimates are reliable, and the DFOP model can be used as the best-fit +model based on the <span class="math inline"><em>χ</em><sup>2</sup></span> error level criterion +for laboratory data L3.</p> +<p>This is also an example where the standard t-test for the parameter +<code>g_ilr</code> is misleading, as it tests for a significant +difference from zero. In this case, zero appears to be the correct value +for this parameter, and the confidence interval for the backtransformed +parameter <code>g</code> is quite narrow.</p> </div> </div> <div id="laboratory-data-l4" class="section level1"> <h1>Laboratory Data L4</h1> -<p>The following code defines example dataset L4 from the FOCUS kinetics report, p. 293:</p> +<p>The following code defines example dataset L4 from the FOCUS kinetics +report, p. 293:</p> <pre class="r"><code>FOCUS_2006_L4 = data.frame( t = c(0, 3, 7, 14, 30, 60, 91, 120), parent = c(96.6, 96.3, 94.3, 88.8, 74.9, 59.9, 53.5, 49.0)) FOCUS_2006_L4_mkin <- mkin_wide_to_long(FOCUS_2006_L4)</code></pre> -<p>Fits of the SFO and FOMC models, plots and summaries are produced below:</p> +<p>Fits of the SFO and FOMC models, plots and summaries are produced +below:</p> <pre class="r"><code># Only use one core here, not to offend the CRAN checks mm.L4 <- mmkin(c("SFO", "FOMC"), cores = 1, list("FOCUS L4" = FOCUS_2006_L4_mkin), quiet = TRUE) plot(mm.L4)</code></pre> <p><img src="data:image/png;base64,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" /><!-- --></p> -<p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p> +<p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error +level of 3.3% as well as the plot suggest that the SFO model fits very +well. The error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes is +slightly lower for the FOMC model. However, the difference appears +negligible.</p> <pre class="r"><code>summary(mm.L4[["SFO", 1]], data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 1.1.2 -## R version used for fitting: 4.2.1 -## Date of fit: Wed Sep 14 22:28:36 2022 -## Date of summary: Wed Sep 14 22:28:37 2022 +<pre><code>## mkin version used for fitting: 1.2.2 +## R version used for fitting: 4.2.2 +## Date of fit: Thu Jan 5 14:50:16 2023 +## Date of summary: Thu Jan 5 14:50:16 2023 ## ## Equations: ## d_parent/dt = - k_parent * parent ## ## Model predictions using solution type analytical ## -## Fitted using 142 model solutions performed in 0.034 s +## Fitted using 142 model solutions performed in 0.01 s ## ## Error model: Constant variance ## @@ -2092,17 +2190,17 @@ plot(mm.L4)</code></pre> ## DT50 DT90 ## parent 106 352</code></pre> <pre class="r"><code>summary(mm.L4[["FOMC", 1]], data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 1.1.2 -## R version used for fitting: 4.2.1 -## Date of fit: Wed Sep 14 22:28:37 2022 -## Date of summary: Wed Sep 14 22:28:37 2022 +<pre><code>## mkin version used for fitting: 1.2.2 +## R version used for fitting: 4.2.2 +## Date of fit: Thu Jan 5 14:50:16 2023 +## Date of summary: Thu Jan 5 14:50:16 2023 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 224 model solutions performed in 0.045 s +## Fitted using 224 model solutions performed in 0.013 s ## ## Error model: Constant variance ## @@ -2163,9 +2261,11 @@ plot(mm.L4)</code></pre> </div> <div id="references" class="section level1 unnumbered"> <h1 class="unnumbered">References</h1> -<div id="refs" class="references hanging-indent"> -<div id="ref-ranke2014"> -<p>Ranke, Johannes. 2014. “Prüfung und Validierung von Modellierungssoftware als Alternative zu ModelMaker 4.0.” Umweltbundesamt Projektnummer 27452.</p> +<div id="refs" class="references csl-bib-body hanging-indent"> +<div id="ref-ranke2014" class="csl-entry"> +Ranke, Johannes. 2014. <span>“<span class="nocase">Prüfung und +Validierung von Modellierungssoftware als Alternative zu ModelMaker +4.0</span>.”</span> Umweltbundesamt Projektnummer 27452. </div> </div> </div> diff --git a/vignettes/dmta_parent_2022_prebuilt.rnw b/vignettes/dmta_parent_2022_prebuilt.rnw new file mode 100644 index 00000000..cbfa6897 --- /dev/null +++ b/vignettes/dmta_parent_2022_prebuilt.rnw @@ -0,0 +1,7 @@ +\documentclass{article} +\usepackage{pdfpages} +%\VignetteIndexEntry{Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P} + +\begin{document} +\includepdf[pages=-, fitpaper=true]{2022_wp_1/2022_dmta_parent.pdf} +\end{document} diff --git a/vignettes/dmta_pathway_2022_prebuilt.rnw b/vignettes/dmta_pathway_2022_prebuilt.rnw new file mode 100644 index 00000000..0a3aaaa8 --- /dev/null +++ b/vignettes/dmta_pathway_2022_prebuilt.rnw @@ -0,0 +1,7 @@ +\documentclass{article} +\usepackage{pdfpages} +%\VignetteIndexEntry{Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P} + +\begin{document} +\includepdf[pages=-, fitpaper=true]{2022_wp_1/2022_dmta_pathway.pdf} +\end{document} diff --git a/vignettes/mkin.html b/vignettes/mkin.html index 38c44a0f..ec3bf5da 100644 --- a/vignettes/mkin.html +++ b/vignettes/mkin.html @@ -31,7 +31,7 @@ document.addEventListener('DOMContentLoaded', function(e) { !function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return 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) 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) 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li.active { @@ -1599,15 +1614,25 @@ div.tocify { <h1 class="title toc-ignore">Introduction to mkin</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 15 February 2021 (rebuilt 2022-11-15)</h4> +<h4 class="date">Last change 15 February 2021 (rebuilt 2023-02-13)</h4> </div> -<p><a href="https://www.jrwb.de">Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany</a><br /> Privatdozent at the University of Freiburg</p> +<p><a href="https://www.jrwb.de">Wissenschaftlicher Berater, Kronacher +Str. 12, 79639 Grenzach-Wyhlen, Germany</a><br /> Privatdozent at the +University of Freiburg</p> <div id="abstract" class="section level1"> <h1>Abstract</h1> -<p>In the regulatory evaluation of chemical substances like plant protection products (pesticides), biocides and other chemicals, degradation data play an important role. For the evaluation of pesticide degradation experiments, detailed guidance has been developed, based on nonlinear optimisation. The <code>R</code> add-on package <code>mkin</code> implements fitting some of the models recommended in this guidance from within R and calculates some statistical measures for data series within one or more compartments, for parent and metabolites.</p> +<p>In the regulatory evaluation of chemical substances like plant +protection products (pesticides), biocides and other chemicals, +degradation data play an important role. For the evaluation of pesticide +degradation experiments, detailed guidance has been developed, based on +nonlinear optimisation. The <code>R</code> add-on package +<code>mkin</code> implements fitting some of the models recommended in +this guidance from within R and calculates some statistical measures for +data series within one or more compartments, for parent and +metabolites.</p> <pre class="r"><code>library("mkin", quietly = TRUE) # Define the kinetic model m_SFO_SFO_SFO <- mkinmod(parent = mkinsub("SFO", "M1"), @@ -1636,92 +1661,245 @@ f_SFO_SFO_SFO <- mkinfit(m_SFO_SFO_SFO, d_SFO_SFO_SFO_err[[1]], quiet = TRUE) # Plot the results separately for parent and metabolites plot_sep(f_SFO_SFO_SFO, lpos = c("topright", "bottomright", "bottomright"))</code></pre> -<p><img 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" 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" /><!-- --></p> </div> <div id="background" class="section level1"> <h1>Background</h1> -<p>The <code>mkin</code> package <span class="citation">(Ranke 2021)</span> implements the approach to degradation kinetics recommended in the kinetics report provided by the FOrum for Co-ordination of pesticide fate models and their USe <span class="citation">(FOCUS Work Group on Degradation Kinetics 2006, 2014)</span>. It covers data series describing the decline of one compound, data series with transformation products (commonly termed metabolites) and data series for more than one compartment. It is possible to include back reactions. Therefore, equilibrium reactions and equilibrium partitioning can be specified, although this often leads to an overparameterisation of the model.</p> -<p>When the first <code>mkin</code> code was published in 2010, the most commonly used tools for fitting more complex kinetic degradation models to experimental data were KinGUI <span class="citation">(Schäfer et al. 2007)</span>, a MATLAB based tool with a graphical user interface that was specifically tailored to the task and included some output as proposed by the FOCUS Kinetics Workgroup, and ModelMaker, a general purpose compartment based tool providing infrastructure for fitting dynamic simulation models based on differential equations to data.</p> -<p>The ‘mkin’ code was first uploaded to the BerliOS development platform. When this was taken down, the version control history was imported into the R-Forge site (see <em>e.g.</em> <a href="https://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770">the initial commit on 11 May 2010</a>), where the code is still being updated.</p> -<p>At that time, the R package <code>FME</code> (Flexible Modelling Environment) <span class="citation">(Soetaert and Petzoldt 2010)</span> was already available, and provided a good basis for developing a package specifically tailored to the task. The remaining challenge was to make it as easy as possible for the users (including the author of this vignette) to specify the system of differential equations and to include the output requested by the FOCUS guidance, such as the <span class="math inline">\(\chi^2\)</span> error level as defined in this guidance.</p> -<p>Also, <code>mkin</code> introduced using analytical solutions for parent only kinetics for improved optimization speed. Later, Eigenvalue based solutions were introduced to <code>mkin</code> for the case of linear differential equations (<em>i.e.</em> where the FOMC or DFOP models were not used for the parent compound), greatly improving the optimization speed for these cases. This, has become somehow obsolete, as the use of compiled code described below gives even faster execution times.</p> -<p>The possibility to specify back-reactions and a biphasic model (SFORB) for metabolites were present in <code>mkin</code> from the very beginning.</p> +<p>The <code>mkin</code> package <span class="citation">(J. Ranke +2021)</span> implements the approach to degradation kinetics recommended +in the kinetics report provided by the FOrum for Co-ordination of +pesticide fate models and their USe <span class="citation">(FOCUS Work +Group on Degradation Kinetics 2006, 2014)</span>. It covers data series +describing the decline of one compound, data series with transformation +products (commonly termed metabolites) and data series for more than one +compartment. It is possible to include back reactions. Therefore, +equilibrium reactions and equilibrium partitioning can be specified, +although this often leads to an overparameterisation of the model.</p> +<p>When the first <code>mkin</code> code was published in 2010, the most +commonly used tools for fitting more complex kinetic degradation models +to experimental data were KinGUI <span class="citation">(Schäfer et al. +2007)</span>, a MATLAB based tool with a graphical user interface that +was specifically tailored to the task and included some output as +proposed by the FOCUS Kinetics Workgroup, and ModelMaker, a general +purpose compartment based tool providing infrastructure for fitting +dynamic simulation models based on differential equations to data.</p> +<p>The ‘mkin’ code was first uploaded to the BerliOS development +platform. When this was taken down, the version control history was +imported into the R-Forge site (see <em>e.g.</em> <a href="https://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770">the +initial commit on 11 May 2010</a>), where the code is still being +updated.</p> +<p>At that time, the R package <code>FME</code> (Flexible Modelling +Environment) <span class="citation">(Soetaert and Petzoldt 2010)</span> +was already available, and provided a good basis for developing a +package specifically tailored to the task. The remaining challenge was +to make it as easy as possible for the users (including the author of +this vignette) to specify the system of differential equations and to +include the output requested by the FOCUS guidance, such as the <span class="math inline">\(\chi^2\)</span> error level as defined in this +guidance.</p> +<p>Also, <code>mkin</code> introduced using analytical solutions for +parent only kinetics for improved optimization speed. Later, Eigenvalue +based solutions were introduced to <code>mkin</code> for the case of +linear differential equations (<em>i.e.</em> where the FOMC or DFOP +models were not used for the parent compound), greatly improving the +optimization speed for these cases. This, has become somehow obsolete, +as the use of compiled code described below gives even faster execution +times.</p> +<p>The possibility to specify back-reactions and a biphasic model +(SFORB) for metabolites were present in <code>mkin</code> from the very +beginning.</p> <div id="derived-software-tools" class="section level2"> <h2>Derived software tools</h2> -<p>Soon after the publication of <code>mkin</code>, two derived tools were published, namely KinGUII (developed at Bayer Crop Science) and CAKE (commissioned to Tessella by Syngenta), which added a graphical user interface (GUI), and added fitting by iteratively reweighted least squares (IRLS) and characterisation of likely parameter distributions by Markov Chain Monte Carlo (MCMC) sampling.</p> -<p>CAKE focuses on a smooth use experience, sacrificing some flexibility in the model definition, originally allowing only two primary metabolites in parallel. The current version 3.4 of CAKE released in May 2020 uses a scheme for up to six metabolites in a flexible arrangement and supports biphasic modelling of metabolites, but does not support back-reactions (non-instantaneous equilibria).</p> -<p>KinGUI offers an even more flexible widget for specifying complex kinetic models. Back-reactions (non-instantaneous equilibria) were supported early on, but until 2014, only simple first-order models could be specified for transformation products. Starting with KinGUII version 2.1, biphasic modelling of metabolites was also available in KinGUII.</p> -<p>A further graphical user interface (GUI) that has recently been brought to a decent degree of maturity is the browser based GUI named <code>gmkin</code>. Please see its <a href="https://pkgdown.jrwb.de/gmkin/">documentation page</a> and <a href="https://pkgdown.jrwb.de/gmkin/articles/gmkin_manual.html">manual</a> for further information.</p> -<p>A comparison of scope, usability and numerical results obtained with these tools has been recently been published by <span class="citation">Ranke, Wöltjen, and Meinecke (2018)</span>.</p> +<p>Soon after the publication of <code>mkin</code>, two derived tools +were published, namely KinGUII (developed at Bayer Crop Science) and +CAKE (commissioned to Tessella by Syngenta), which added a graphical +user interface (GUI), and added fitting by iteratively reweighted least +squares (IRLS) and characterisation of likely parameter distributions by +Markov Chain Monte Carlo (MCMC) sampling.</p> +<p>CAKE focuses on a smooth use experience, sacrificing some flexibility +in the model definition, originally allowing only two primary +metabolites in parallel. The current version 3.4 of CAKE released in May +2020 uses a scheme for up to six metabolites in a flexible arrangement +and supports biphasic modelling of metabolites, but does not support +back-reactions (non-instantaneous equilibria).</p> +<p>KinGUI offers an even more flexible widget for specifying complex +kinetic models. Back-reactions (non-instantaneous equilibria) were +supported early on, but until 2014, only simple first-order models could +be specified for transformation products. Starting with KinGUII version +2.1, biphasic modelling of metabolites was also available in +KinGUII.</p> +<p>A further graphical user interface (GUI) that has recently been +brought to a decent degree of maturity is the browser based GUI named +<code>gmkin</code>. Please see its <a href="https://pkgdown.jrwb.de/gmkin/">documentation page</a> and <a href="https://pkgdown.jrwb.de/gmkin/articles/gmkin_manual.html">manual</a> +for further information.</p> +<p>A comparison of scope, usability and numerical results obtained with +these tools has been recently been published by <span class="citation">Johannes Ranke, Wöltjen, and Meinecke +(2018)</span>.</p> </div> </div> <div id="unique-features" class="section level1"> <h1>Unique features</h1> -<p>Currently, the main unique features available in <code>mkin</code> are</p> +<p>Currently, the main unique features available in <code>mkin</code> +are</p> <ul> -<li>the <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html">speed increase</a> by using compiled code when a compiler is present,</li> -<li>parallel model fitting on multicore machines using the <a href="https://pkgdown.jrwb.de/mkin/reference/mmkin.html"><code>mmkin</code> function</a>,</li> -<li>the estimation of parameter confidence intervals based on transformed parameters (see below) and</li> -<li>the possibility to use the <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">two-component error model</a></li> +<li>the <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html">speed +increase</a> by using compiled code when a compiler is present,</li> +<li>parallel model fitting on multicore machines using the <a href="https://pkgdown.jrwb.de/mkin/reference/mmkin.html"><code>mmkin</code> +function</a>,</li> +<li>the estimation of parameter confidence intervals based on +transformed parameters (see below) and</li> +<li>the possibility to use the <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">two-component +error model</a></li> </ul> -<p>The iteratively reweighted least squares fitting of different variances for each variable as introduced by <span class="citation">Gao et al. (2011)</span> has been available in mkin since <a href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-22-2013-10-26">version 0.9-22</a>. With <a href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-49-5-2019-07-04">release 0.9.49.5</a>, the IRLS algorithm has been complemented by direct or step-wise maximisation of the likelihood function, which makes it possible not only to fit the variance by variable error model but also a <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">two-component error model</a> inspired by error models developed in analytical chemistry <span class="citation">(Ranke and Meinecke 2019)</span>.</p> +<p>The iteratively reweighted least squares fitting of different +variances for each variable as introduced by <span class="citation">Gao +et al. (2011)</span> has been available in mkin since <a href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-22-2013-10-26">version +0.9-22</a>. With <a href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-49-5-2019-07-04">release +0.9.49.5</a>, the IRLS algorithm has been complemented by direct or +step-wise maximisation of the likelihood function, which makes it +possible not only to fit the variance by variable error model but also a +<a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">two-component +error model</a> inspired by error models developed in analytical +chemistry <span class="citation">(Johannes Ranke and Meinecke +2019)</span>.</p> </div> <div id="internal-parameter-transformations" class="section level1"> <h1>Internal parameter transformations</h1> -<p>For rate constants, the log transformation is used, as proposed by Bates and Watts <span class="citation">(1988, 77, 149)</span>. Approximate intervals are constructed for the transformed rate constants <span class="citation">(compare Bates and Watts 1988, 135)</span>, <em>i.e.</em> for their logarithms. Confidence intervals for the rate constants are then obtained using the appropriate backtransformation using the exponential function.</p> -<p>In the first version of <code>mkin</code> allowing for specifying models using formation fractions, a home-made reparameterisation was used in order to ensure that the sum of formation fractions would not exceed unity.</p> -<p>This method is still used in the current version of KinGUII (v2.1 from April 2014), with a modification that allows for fixing the pathway to sink to zero. CAKE uses penalties in the objective function in order to enforce this constraint.</p> -<p>In 2012, an alternative reparameterisation of the formation fractions was proposed together with René Lehmann <span class="citation">(Ranke and Lehmann 2012)</span>, based on isometric logratio transformation (ILR). The aim was to improve the validity of the linear approximation of the objective function during the parameter estimation procedure as well as in the subsequent calculation of parameter confidence intervals. In the current version of mkin, a logit transformation is used for parameters that are bound between 0 and 1, such as the g parameter of the DFOP model.</p> +<p>For rate constants, the log transformation is used, as proposed by +Bates and Watts <span class="citation">(1988, 77, 149)</span>. +Approximate intervals are constructed for the transformed rate constants +<span class="citation">(compare Bates and Watts 1988, 135)</span>, +<em>i.e.</em> for their logarithms. Confidence intervals for the rate +constants are then obtained using the appropriate backtransformation +using the exponential function.</p> +<p>In the first version of <code>mkin</code> allowing for specifying +models using formation fractions, a home-made reparameterisation was +used in order to ensure that the sum of formation fractions would not +exceed unity.</p> +<p>This method is still used in the current version of KinGUII (v2.1 +from April 2014), with a modification that allows for fixing the pathway +to sink to zero. CAKE uses penalties in the objective function in order +to enforce this constraint.</p> +<p>In 2012, an alternative reparameterisation of the formation fractions +was proposed together with René Lehmann <span class="citation">(J. Ranke +and Lehmann 2012)</span>, based on isometric logratio transformation +(ILR). The aim was to improve the validity of the linear approximation +of the objective function during the parameter estimation procedure as +well as in the subsequent calculation of parameter confidence intervals. +In the current version of mkin, a logit transformation is used for +parameters that are bound between 0 and 1, such as the g parameter of +the DFOP model.</p> <div id="confidence-intervals-based-on-transformed-parameters" class="section level2"> <h2>Confidence intervals based on transformed parameters</h2> -<p>In the first attempt at providing improved parameter confidence intervals introduced to <code>mkin</code> in 2013, confidence intervals obtained from FME on the transformed parameters were simply all backtransformed one by one to yield asymmetric confidence intervals for the backtransformed parameters.</p> -<p>However, while there is a 1:1 relation between the rate constants in the model and the transformed parameters fitted in the model, the parameters obtained by the isometric logratio transformation are calculated from the set of formation fractions that quantify the paths to each of the compounds formed from a specific parent compound, and no such 1:1 relation exists.</p> -<p>Therefore, parameter confidence intervals for formation fractions obtained with this method only appear valid for the case of a single transformation product, where currently the logit transformation is used for the formation fraction.</p> -<p>The confidence intervals obtained by backtransformation for the cases where a 1:1 relation between transformed and original parameter exist are considered by the author of this vignette to be more accurate than those obtained using a re-estimation of the Hessian matrix after backtransformation, as implemented in the FME package.</p> +<p>In the first attempt at providing improved parameter confidence +intervals introduced to <code>mkin</code> in 2013, confidence intervals +obtained from FME on the transformed parameters were simply all +backtransformed one by one to yield asymmetric confidence intervals for +the backtransformed parameters.</p> +<p>However, while there is a 1:1 relation between the rate constants in +the model and the transformed parameters fitted in the model, the +parameters obtained by the isometric logratio transformation are +calculated from the set of formation fractions that quantify the paths +to each of the compounds formed from a specific parent compound, and no +such 1:1 relation exists.</p> +<p>Therefore, parameter confidence intervals for formation fractions +obtained with this method only appear valid for the case of a single +transformation product, where currently the logit transformation is used +for the formation fraction.</p> +<p>The confidence intervals obtained by backtransformation for the cases +where a 1:1 relation between transformed and original parameter exist +are considered by the author of this vignette to be more accurate than +those obtained using a re-estimation of the Hessian matrix after +backtransformation, as implemented in the FME package.</p> </div> <div id="parameter-t-test-based-on-untransformed-parameters" class="section level2"> <h2>Parameter t-test based on untransformed parameters</h2> -<p>The standard output of many nonlinear regression software packages includes the results from a test for significant difference from zero for all parameters. Such a test is also recommended to check the validity of rate constants in the FOCUS guidance <span class="citation">(FOCUS Work Group on Degradation Kinetics 2014, 96ff)</span>.</p> -<p>It has been argued that the precondition for this test, <em>i.e.</em> normal distribution of the estimator for the parameters, is not fulfilled in the case of nonlinear regression <span class="citation">(Ranke and Lehmann 2015)</span>. However, this test is commonly used by industry, consultants and national authorities in order to decide on the reliability of parameter estimates, based on the FOCUS guidance mentioned above. Therefore, the results of this one-sided t-test are included in the summary output from <code>mkin</code>.</p> -<p>As it is not reasonable to test for significant difference of the transformed parameters (<em>e.g.</em> <span class="math inline">\(log(k)\)</span>) from zero, the t-test is calculated based on the model definition before parameter transformation, <em>i.e.</em> in a similar way as in packages that do not apply such an internal parameter transformation. A note is included in the <code>mkin</code> output, pointing to the fact that the t-test is based on the unjustified assumption of normal distribution of the parameter estimators.</p> +<p>The standard output of many nonlinear regression software packages +includes the results from a test for significant difference from zero +for all parameters. Such a test is also recommended to check the +validity of rate constants in the FOCUS guidance <span class="citation">(FOCUS Work Group on Degradation Kinetics 2014, +96ff)</span>.</p> +<p>It has been argued that the precondition for this test, <em>i.e.</em> +normal distribution of the estimator for the parameters, is not +fulfilled in the case of nonlinear regression <span class="citation">(J. +Ranke and Lehmann 2015)</span>. However, this test is commonly used by +industry, consultants and national authorities in order to decide on the +reliability of parameter estimates, based on the FOCUS guidance +mentioned above. Therefore, the results of this one-sided t-test are +included in the summary output from <code>mkin</code>.</p> +<p>As it is not reasonable to test for significant difference of the +transformed parameters (<em>e.g.</em> <span class="math inline">\(log(k)\)</span>) from zero, the t-test is +calculated based on the model definition before parameter +transformation, <em>i.e.</em> in a similar way as in packages that do +not apply such an internal parameter transformation. A note is included +in the <code>mkin</code> output, pointing to the fact that the t-test is +based on the unjustified assumption of normal distribution of the +parameter estimators.</p> </div> </div> <div id="references" class="section level1"> <h1>References</h1> <!-- vim: set foldmethod=syntax: --> -<div id="refs" class="references hanging-indent"> -<div id="ref-bates1988"> -<p>Bates, D., and D. Watts. 1988. <em>Nonlinear Regression and Its Applications</em>. Wiley-Interscience.</p> +<div id="refs" class="references csl-bib-body hanging-indent"> +<div id="ref-bates1988" class="csl-entry"> +Bates, D., and D. Watts. 1988. <em>Nonlinear Regression and Its +Applications</em>. Wiley-Interscience. </div> -<div id="ref-FOCUS2006"> -<p>FOCUS Work Group on Degradation Kinetics. 2006. <em>Guidance Document on Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration. Report of the Focus Work Group on Degradation Kinetics</em>. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> +<div id="ref-FOCUS2006" class="csl-entry"> +FOCUS Work Group on Degradation Kinetics. 2006. <em>Guidance Document on +Estimating Persistence and Degradation Kinetics from Environmental Fate +Studies on Pesticides in EU Registration. Report of the FOCUS Work Group +on Degradation Kinetics</em>. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>. </div> -<div id="ref-FOCUSkinetics2014"> -<p>———. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> +<div id="ref-FOCUSkinetics2014" class="csl-entry"> +———. 2014. <em>Generic Guidance for Estimating Persistence and +Degradation Kinetics from Environmental Fate Studies on Pesticides in EU +Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>. </div> -<div id="ref-gao11"> -<p>Gao, Z., J. W. Green, J. Vanderborght, and W. Schmitt. 2011. “Improving Uncertainty Analysis in Kinetic Evaluations Using Iteratively Reweighted Least Squares.” Journal. <em>Environmental Science and Technology</em> 45: 4429–37.</p> +<div id="ref-gao11" class="csl-entry"> +Gao, Z., J. W. Green, J. Vanderborght, and W. Schmitt. 2011. +<span>“Improving Uncertainty Analysis in Kinetic Evaluations Using +Iteratively Reweighted Least Squares.”</span> Journal. <em>Environmental +Science and Technology</em> 45: 4429–37. </div> -<div id="ref-pkg:mkin"> -<p>Ranke, J. 2021. <em>‘mkin‘: Kinetic Evaluation of Chemical Degradation Data</em>. <a href="https://CRAN.R-project.org/package=mkin">https://CRAN.R-project.org/package=mkin</a>.</p> +<div id="ref-pkg:mkin" class="csl-entry"> +Ranke, J. 2021. <em>‘<span class="nocase">mkin</span>‘: +<span>K</span>inetic Evaluation of Chemical Degradation Data</em>. <a href="https://CRAN.R-project.org/package=mkin">https://CRAN.R-project.org/package=mkin</a>. </div> -<div id="ref-ranke2012"> -<p>Ranke, J., and R. Lehmann. 2012. “Parameter Reliability in Kinetic Evaluation of Environmental Metabolism Data - Assessment and the Influence of Model Specification.” In <em>SETAC World 20-24 May</em>. Berlin. <a href="https://jrwb.de/posters/Poster_SETAC_2012_Kinetic_parameter_uncertainty_model_parameterization_Lehmann_Ranke.pdf">https://jrwb.de/posters/Poster_SETAC_2012_Kinetic_parameter_uncertainty_model_parameterization_Lehmann_Ranke.pdf</a>.</p> +<div id="ref-ranke2012" class="csl-entry"> +Ranke, J., and R. Lehmann. 2012. <span>“Parameter Reliability in Kinetic +Evaluation of Environmental Metabolism Data - Assessment and the +Influence of Model Specification.”</span> In <em>SETAC World 20-24 +May</em>. Berlin. <a href="https://jrwb.de/posters/Poster_SETAC_2012_Kinetic_parameter_uncertainty_model_parameterization_Lehmann_Ranke.pdf">https://jrwb.de/posters/Poster_SETAC_2012_Kinetic_parameter_uncertainty_model_parameterization_Lehmann_Ranke.pdf</a>. </div> -<div id="ref-ranke2015"> -<p>———. 2015. “To T-Test or Not to T-Test, That Is the Question.” In <em>XV Symposium on Pesticide Chemistry 2-4 September 2015</em>. Piacenza. <a href="https://jrwb.de/posters/piacenza_2015.pdf">https://jrwb.de/posters/piacenza_2015.pdf</a>.</p> +<div id="ref-ranke2015" class="csl-entry"> +———. 2015. <span>“To t-Test or Not to t-Test, That Is the +Question.”</span> In <em>XV Symposium on Pesticide Chemistry 2-4 +September 2015</em>. Piacenza. <a href="https://jrwb.de/posters/piacenza_2015.pdf">https://jrwb.de/posters/piacenza_2015.pdf</a>. </div> -<div id="ref-ranke2019"> -<p>Ranke, Johannes, and Stefan Meinecke. 2019. “Error Models for the Kinetic Evaluation of Chemical Degradation Data.” <em>Environments</em> 6 (12). <a href="https://doi.org/10.3390/environments6120124">https://doi.org/10.3390/environments6120124</a>.</p> +<div id="ref-ranke2019" class="csl-entry"> +Ranke, Johannes, and Stefan Meinecke. 2019. <span>“Error Models for the +Kinetic Evaluation of Chemical Degradation Data.”</span> +<em>Environments</em> 6 (12). <a href="https://doi.org/10.3390/environments6120124">https://doi.org/10.3390/environments6120124</a>. </div> -<div id="ref-ranke2018"> -<p>Ranke, Johannes, Janina Wöltjen, and Stefan Meinecke. 2018. “Comparison of Software Tools for Kinetic Evaluation of Chemical Degradation Data.” <em>Environmental Sciences Europe</em> 30 (1): 17. <a href="https://doi.org/10.1186/s12302-018-0145-1">https://doi.org/10.1186/s12302-018-0145-1</a>.</p> +<div id="ref-ranke2018" class="csl-entry"> +Ranke, Johannes, Janina Wöltjen, and Stefan Meinecke. 2018. +<span>“Comparison of Software Tools for Kinetic Evaluation of Chemical +Degradation Data.”</span> <em>Environmental Sciences Europe</em> 30 (1): +17. <a href="https://doi.org/10.1186/s12302-018-0145-1">https://doi.org/10.1186/s12302-018-0145-1</a>. </div> -<div id="ref-schaefer2007"> -<p>Schäfer, D., B. Mikolasch, P. Rainbird, and B. Harvey. 2007. “KinGUI: A New Kinetic Software Tool for Evaluations According to FOCUS Degradation Kinetics.” In <em>Proceedings of the Xiii Symposium Pesticide Chemistry</em>, edited by Del Re A. A. M., Capri E., Fragoulis G., and Trevisan M., 916–23. Piacenza.</p> +<div id="ref-schaefer2007" class="csl-entry"> +Schäfer, D., B. Mikolasch, P. Rainbird, and B. Harvey. 2007. +<span>“<span>KinGUI</span>: A New Kinetic Software Tool for Evaluations +According to <span>FOCUS</span> Degradation Kinetics.”</span> In +<em>Proceedings of the XIII Symposium Pesticide Chemistry</em>, edited +by Del Re A. A. M., Capri E., Fragoulis G., and Trevisan M., 916–23. +Piacenza. </div> -<div id="ref-soetaert2010"> -<p>Soetaert, Karline, and Thomas Petzoldt. 2010. “Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME.” <em>Journal of Statistical Software</em> 33 (3): 1–28. <a href="https://doi.org/10.18637/jss.v033.i03">https://doi.org/10.18637/jss.v033.i03</a>.</p> +<div id="ref-soetaert2010" class="csl-entry"> +Soetaert, Karline, and Thomas Petzoldt. 2010. <span>“Inverse Modelling, +Sensitivity and Monte Carlo Analysis in <span>R</span> Using Package +<span>FME</span>.”</span> <em>Journal of Statistical Software</em> 33 +(3): 1–28. <a href="https://doi.org/10.18637/jss.v033.i03">https://doi.org/10.18637/jss.v033.i03</a>. </div> </div> </div> diff --git a/vignettes/prebuilt/2022_cyan_pathway.rmd b/vignettes/prebuilt/2022_cyan_pathway.rmd new file mode 100644 index 00000000..df34a6f2 --- /dev/null +++ b/vignettes/prebuilt/2022_cyan_pathway.rmd @@ -0,0 +1,536 @@ +--- +title: "Testing hierarchical pathway kinetics with residue data on cyantraniliprole" +author: Johannes Ranke +date: Last change on 6 January 2023, last compiled on `r format(Sys.time(), "%e + %B %Y")` +output: + pdf_document: + extra_dependencies: ["float", "listing"] +toc: yes +geometry: margin=2cm +--- + +```{r setup, echo = FALSE, cache = FALSE} +options(width = 80) # For summary listings +knitr::opts_chunk$set( + comment = "", tidy = FALSE, cache = TRUE, fig.pos = "H", fig.align = "center" +) +``` + +\clearpage + +# Introduction + +The purpose of this document is to test demonstrate how nonlinear hierarchical +models (NLHM) based on the parent degradation models SFO, FOMC, DFOP and HS, +with serial formation of two or more metabolites can be fitted with the mkin +package. + +It was assembled in the course of work package 1.2 of Project Number 173340 +(Application of nonlinear hierarchical models to the kinetic evaluation of +chemical degradation data) of the German Environment Agency carried out in 2022 +and 2023. + +The mkin package is used in version `r packageVersion("mkin")` which is +currently under development. The newly introduced functionality that is +used here is a simplification of excluding random effects for a set of fits +based on a related set of fits with a reduced model, and the documentation of +the starting parameters of the fit, so that all starting parameters of `saem` +fits are now listed in the summary. The `saemix` package is used as a backend +for fitting the NLHM, but is also loaded to make the convergence plot function +available. + +This document is processed with the `knitr` package, which also provides the +`kable` function that is used to improve the display of tabular data in R +markdown documents. For parallel processing, the `parallel` package is used. + +```{r, packages, cache = FALSE, message = FALSE} +library(mkin) +library(knitr) +library(saemix) +library(parallel) +n_cores <- detectCores() +if (Sys.info()["sysname"] == "Windows") { + cl <- makePSOCKcluster(n_cores) +} else { + cl <- makeForkCluster(n_cores) +} +``` + +\clearpage + +## Test data + +The example data are taken from the final addendum to the DAR from 2014 +and are distributed with the mkin package. Residue data and time step +normalisation factors are read in using the function `read_spreadsheet` from +the mkin package. This function also performs the time step normalisation. + +```{r data} +data_file <- system.file( + "testdata", "cyantraniliprole_soil_efsa_2014.xlsx", + package = "mkin") +cyan_ds <- read_spreadsheet(data_file, parent_only = FALSE) +``` + +The following tables show the covariate data and the `r length(cyan_ds)` +datasets that were read in from the spreadsheet file. + +```{r show-covar-data, dependson = "data", results = "asis"} +pH <- attr(cyan_ds, "covariates") +kable(pH, caption = "Covariate data") +``` + +\clearpage + +```{r show-data, dependson = "data", results = "asis"} +for (ds_name in names(cyan_ds)) { + print( + kable(mkin_long_to_wide(cyan_ds[[ds_name]]), + caption = paste("Dataset", ds_name), + booktabs = TRUE, row.names = FALSE)) + cat("\n\\clearpage\n") +} +``` + +\clearpage + +# Parent only evaluations + +As the pathway fits have very long run times, evaluations of the parent data +are performed first, in order to determine for each hierarchical parent +degradation model which random effects on the degradation model parameters are +ill-defined. + +```{r parent-only, dependson = "data"} +cyan_sep_const <- mmkin(c("SFO", "FOMC", "DFOP", "SFORB", "HS"), + cyan_ds, quiet = TRUE, cores = n_cores) +cyan_sep_tc <- update(cyan_sep_const, error_model = "tc") +cyan_saem_full <- mhmkin(list(cyan_sep_const, cyan_sep_tc)) +status(cyan_saem_full) |> kable() +``` + +All fits converged successfully. + +```{r dependson = "parent-only"} +illparms(cyan_saem_full) |> kable() +``` +In almost all models, the random effect for the initial concentration of the +parent compound is ill-defined. For the biexponential models DFOP and SFORB, +the random effect of one additional parameter is ill-defined when the two-component +error model is used. + +```{r dependson = "parent-only"} +anova(cyan_saem_full) |> kable(digits = 1) +``` + +Model comparison based on AIC and BIC indicates that the two-component error model +is preferable for all parent models with the exception of DFOP. The lowest AIC +and BIC values are are obtained with the FOMC model, followed by SFORB and DFOP. + +```{r parent-only-reduced, dependson = "parent-only", include = FALSE} +cyan_saem_reduced <- mhmkin(list(cyan_sep_const, cyan_sep_tc), + no_random_effect = illparms(cyan_saem_full)) +illparms(cyan_saem_reduced) +anova(cyan_saem_reduced) |> kable(digits = 1) +``` + +# Pathway fits + +## Evaluations with pathway established previously + +To test the technical feasibility of coupling the relevant parent degradation +models with different transformation pathway models, a list of `mkinmod` models +is set up below. As in the EU evaluation, parallel formation of metabolites +JCZ38 and J9Z38 and secondary formation of metabolite JSE76 from JCZ38 is used. + +```{r, cyan-path-1} +if (!dir.exists("cyan_dlls")) dir.create("cyan_dlls") +cyan_path_1 <- list( + sfo_path_1 = mkinmod( + cyan = mkinsub("SFO", c("JCZ38", "J9Z38")), + JCZ38 = mkinsub("SFO", "JSE76"), + J9Z38 = mkinsub("SFO"), + JSE76 = mkinsub("SFO"), quiet = TRUE, + name = "sfo_path_1", dll_dir = "cyan_dlls", overwrite = TRUE), + fomc_path_1 = mkinmod( + cyan = mkinsub("FOMC", c("JCZ38", "J9Z38")), + JCZ38 = mkinsub("SFO", "JSE76"), + J9Z38 = mkinsub("SFO"), + JSE76 = mkinsub("SFO"), quiet = TRUE, + name = "fomc_path_1", dll_dir = "cyan_dlls", overwrite = TRUE), + dfop_path_1 = mkinmod( + cyan = mkinsub("DFOP", c("JCZ38", "J9Z38")), + JCZ38 = mkinsub("SFO", "JSE76"), + J9Z38 = mkinsub("SFO"), + JSE76 = mkinsub("SFO"), quiet = TRUE, + name = "dfop_path_1", dll_dir = "cyan_dlls", overwrite = TRUE), + sforb_path_1 = mkinmod( + cyan = mkinsub("SFORB", c("JCZ38", "J9Z38")), + JCZ38 = mkinsub("SFO", "JSE76"), + J9Z38 = mkinsub("SFO"), + JSE76 = mkinsub("SFO"), quiet = TRUE, + name = "sforb_path_1", dll_dir = "cyan_dlls", overwrite = TRUE), + hs_path_1 = mkinmod( + cyan = mkinsub("HS", c("JCZ38", "J9Z38")), + JCZ38 = mkinsub("SFO", "JSE76"), + J9Z38 = mkinsub("SFO"), + JSE76 = mkinsub("SFO"), quiet = TRUE, + name = "hs_path_1", dll_dir = "cyan_dlls", overwrite = TRUE) +) +``` +To obtain suitable starting values for the NLHM fits, separate pathway fits are +performed for all datasets. + +```{r, f-sep-1, dependson = c("data", "cyan_path_1")} +f_sep_1_const <- mmkin( + cyan_path_1, + cyan_ds, + error_model = "const", + cluster = cl, + quiet = TRUE) +status(f_sep_1_const) |> kable() + +f_sep_1_tc <- update(f_sep_1_const, error_model = "tc") +status(f_sep_1_tc) |> kable() +``` + +Most separate fits converged successfully. The biggest convergence +problems are seen when using the HS model with constant variance. + +For the hierarchical pathway fits, those random effects that could not be +quantified in the corresponding parent data analyses are excluded. + +In the code below, the output of the `illparms` function for the parent only +fits is used as an argument `no_random_effect` to the `mhmkin` function. +The possibility to do so was introduced in mkin version `1.2.2` which is +currently under development. + +```{r, f-saem-1, dependson = "f-sep-1"} +f_saem_1 <- mhmkin(list(f_sep_1_const, f_sep_1_tc), + no_random_effect = illparms(cyan_saem_full), + cluster = cl) +``` + +```{r dependson = "f-saem-1"} +status(f_saem_1) |> kable() +``` + +The status information from the individual fits shows that all fits completed +successfully. The matrix entries Fth and FO indicate that the Fisher +Information Matrix could not be inverted for the fixed effects (theta) +and the random effects (Omega), respectively. For the affected fits, +ill-defined parameters cannot be determined using the `illparms` function, +because it relies on the Fisher Information Matrix. + +```{r dependson = "f-saem-1"} +illparms(f_saem_1) |> kable() +``` + +The model comparison below suggests that the pathway fits using +DFOP or SFORB for the parent compound provide the best fit. + +```{r, dependson = "f-saem-1"} +anova(f_saem_1) |> kable(digits = 1) +``` + +For these two parent model, successful fits are shown below. Plots of the fits +with the other parent models are shown in the Appendix. + +```{r fig.cap = "DFOP pathway fit with two-component error", dependson = "f-saem-1", fig.height = 8} +plot(f_saem_1[["dfop_path_1", "tc"]]) +``` + +\clearpage + +```{r fig.cap = "SFORB pathway fit with two-component error", dependson = "f-saem-1", fig.height = 8} +plot(f_saem_1[["sforb_path_1", "tc"]]) +``` + +A closer graphical analysis of these Figures shows that the residues of +transformation product JCZ38 in the soils Tama and Nambsheim observed +at later time points are strongly and systematically underestimated. + +\clearpage + + +## Alternative pathway fits + +To improve the fit for JCZ38, a back-reaction from JSE76 to JCZ38 was +introduced in an alternative version of the transformation pathway, in analogy +to the back-reaction from K5A78 to K5A77. Both pairs of transformation products +are pairs of an organic acid with its corresponding amide (Addendum 2014, p. +109). As FOMC provided the best fit for the parent, and the biexponential +models DFOP and SFORB provided the best initial pathway fits, these three +parent models are used in the alternative pathway fits. + +```{r, f-sep-2-const, dependson = "data"} +cyan_path_2 <- list( + fomc_path_2 = mkinmod( + cyan = mkinsub("FOMC", c("JCZ38", "J9Z38")), + JCZ38 = mkinsub("SFO", "JSE76"), + J9Z38 = mkinsub("SFO"), + JSE76 = mkinsub("SFO", "JCZ38"), + name = "fomc_path_2", quiet = TRUE, + dll_dir = "cyan_dlls", + overwrite = TRUE + ), + dfop_path_2 = mkinmod( + cyan = mkinsub("DFOP", c("JCZ38", "J9Z38")), + JCZ38 = mkinsub("SFO", "JSE76"), + J9Z38 = mkinsub("SFO"), + JSE76 = mkinsub("SFO", "JCZ38"), + name = "dfop_path_2", quiet = TRUE, + dll_dir = "cyan_dlls", + overwrite = TRUE + ), + sforb_path_2 = mkinmod( + cyan = mkinsub("SFORB", c("JCZ38", "J9Z38")), + JCZ38 = mkinsub("SFO", "JSE76"), + J9Z38 = mkinsub("SFO"), + JSE76 = mkinsub("SFO", "JCZ38"), + name = "sforb_path_2", quiet = TRUE, + dll_dir = "cyan_dlls", + overwrite = TRUE + ) +) +f_sep_2_const <- mmkin( + cyan_path_2, + cyan_ds, + error_model = "const", + cluster = cl, + quiet = TRUE) + +status(f_sep_2_const) |> kable() +``` + +Using constant variance, separate fits converge with the exception +of the fits to the Sassafras soil data. + +```{r f-sep-2-tc, dependson = "f-sep-2-const"} +f_sep_2_tc <- update(f_sep_2_const, error_model = "tc") +status(f_sep_2_tc) |> kable() +``` + +Using the two-component error model, all separate fits converge with the +exception of the alternative pathway fit with DFOP used for the parent and the +Sassafras dataset. + +```{r f-saem-2, dependson = c("f-sep-2-const", "f-sep-2-tc")} +f_saem_2 <- mhmkin(list(f_sep_2_const, f_sep_2_tc), + no_random_effect = illparms(cyan_saem_full[2:4, ]), + cluster = cl) +``` + +```{r dependson = "f-saem-2"} +status(f_saem_2) |> kable() +``` + +The hierarchical fits for the alternative pathway completed successfully. + +```{r dependson = "f-saem-2"} +illparms(f_saem_2) |> kable() +``` + +In both fits, the random effects for the formation fractions for the +pathways from JCZ38 to JSE76, and for the reverse pathway from JSE76 +to JCZ38 are ill-defined. + +```{r dependson = "f-saem-2"} +anova(f_saem_2) |> kable(digits = 1) +``` + +The variants using the biexponential models DFOP and SFORB for the parent +compound and the two-component error model give the lowest AIC and BIC values +and are plotted below. Compared with the original pathway, the AIC and BIC +values indicate a large improvement. This is confirmed by the plots, which show +that the metabolite JCZ38 is fitted much better with this model. + +\clearpage + +```{r fig.cap = "FOMC pathway fit with two-component error, alternative pathway", dependson = "f-saem-2", fig.height = 8} +plot(f_saem_2[["fomc_path_2", "tc"]]) +``` +\clearpage + +```{r fig.cap = "DFOP pathway fit with two-component error, alternative pathway", dependson = "f-saem-2", fig.height = 8} +plot(f_saem_2[["dfop_path_2", "tc"]]) +``` + +\clearpage + +```{r fig.cap = "SFORB pathway fit with two-component error, alternative pathway", dependson = "f-saem-2", fig.height = 8} +plot(f_saem_2[["sforb_path_2", "tc"]]) +``` + +\clearpage + +## Refinement of alternative pathway fits + +All ill-defined random effects that were identified in the parent only fits and +in the above pathway fits, are excluded for the final evaluations below. +For this purpose, a list of character vectors is created below that can be indexed +by row and column indices, and which contains the degradation parameter names for which +random effects should be excluded for each of the hierarchical fits contained +in `f_saem_2`. + +```{r f-saem-3, dependson = "f-saem-2"} +no_ranef <- matrix(list(), nrow = 3, ncol = 2, dimnames = dimnames(f_saem_2)) +no_ranef[["fomc_path_2", "const"]] <- c("log_beta", "f_JCZ38_qlogis", "f_JSE76_qlogis") +no_ranef[["fomc_path_2", "tc"]] <- c("cyan_0", "f_JCZ38_qlogis", "f_JSE76_qlogis") +no_ranef[["dfop_path_2", "const"]] <- c("cyan_0", "f_JCZ38_qlogis", "f_JSE76_qlogis") +no_ranef[["dfop_path_2", "tc"]] <- c("cyan_0", "log_k1", "f_JCZ38_qlogis", "f_JSE76_qlogis") +no_ranef[["sforb_path_2", "const"]] <- c("cyan_free_0", + "f_JCZ38_qlogis", "f_JSE76_qlogis") +no_ranef[["sforb_path_2", "tc"]] <- c("cyan_free_0", "log_k_cyan_free_bound", + "f_JCZ38_qlogis", "f_JSE76_qlogis") +clusterExport(cl, "no_ranef") + +f_saem_3 <- update(f_saem_2, + no_random_effect = no_ranef, + cluster = cl) +``` + +```{r dependson = "f-saem-3"} +status(f_saem_3) |> kable() +``` + +With the exception of the FOMC pathway fit with constant variance, all updated +fits completed successfully. However, the Fisher Information Matrix for the +fixed effects (Fth) could not be inverted, so no confidence intervals for the +optimised parameters are available. + +```{r dependson = "f-saem-3"} +illparms(f_saem_3) |> kable() +``` + +```{r dependson = "f-saem-3"} +anova(f_saem_3) |> kable(digits = 1) +``` + +While the AIC and BIC values of the best fit (DFOP pathway fit with +two-component error) are lower than in the previous fits with the alternative +pathway, the practical value of these refined evaluations is limited +as no confidence intervals are obtained. + +\clearpage + +# Conclusion + +It was demonstrated that a relatively complex transformation pathway with +parallel formation of two primary metabolites and one secondary metabolite +can be fitted even if the data in the individual datasets are quite different +and partly only cover the formation phase. + +The run times of the pathway fits were several hours, limiting the +practical feasibility of iterative refinements based on ill-defined +parameters and of alternative checks of parameter identifiability +based on multistart runs. + +# Acknowledgements + +The helpful comments by Janina Wöltjen of the German Environment Agency +are gratefully acknowledged. + +\clearpage + +# Appendix + +## Plots of fits that were not refined further + +```{r fig.cap = "SFO pathway fit with two-component error", dependson = "f-saem-1", fig.height = 8} +plot(f_saem_1[["sfo_path_1", "tc"]]) +``` + +\clearpage + +```{r fig.cap = "FOMC pathway fit with two-component error", dependson = "f-saem-1", fig.height = 8} +plot(f_saem_1[["fomc_path_1", "tc"]]) +``` + +\clearpage + + +```{r fig.cap = "HS pathway fit with two-component error", dependson = "f-saem-1", fig.height = 8} +plot(f_saem_1[["sforb_path_1", "tc"]]) +``` + +\clearpage + + +## Hierarchical fit listings + +### Pathway 1 + +```{r listings-1, results = "asis", echo = FALSE, cache = FALSE} +errmods <- c(const = "constant variance", tc = "two-component error") +degmods <- c( + sfo_path_1 = "SFO path 1", + fomc_path_1 = "FOMC path 1", + dfop_path_1 = "DFOP path 1", + sforb_path_1 = "SFORB path 1", + hs_path_1 = "HS path 1") +for (deg_mod in rownames(f_saem_1)) { + for (err_mod in c("const", "tc")) { + fit <- f_saem_1[[deg_mod, err_mod]] + if (!inherits(fit$so, "try-error")) { + caption <- paste("Hierarchical", degmods[deg_mod], "fit with", errmods[err_mod]) + summary_listing(fit, caption) + } + } +} +``` + +### Pathway 2 + +```{r listings-2, results = "asis", echo = FALSE, cache = FALSE} +degmods <- c( + fomc_path_2 = "FOMC path 2", + dfop_path_2 = "DFOP path 2", + sforb_path_2 = "SFORB path 2") +for (deg_mod in rownames(f_saem_2)) { + for (err_mod in c("const", "tc")) { + fit <- f_saem_2[[deg_mod, err_mod]] + if (!inherits(fit$so, "try-error")) { + caption <- paste("Hierarchical", degmods[deg_mod], "fit with", errmods[err_mod]) + summary_listing(fit, caption) + } + } +} +``` + +### Pathway 2, refined fits + +```{r listings-3, results = "asis", echo = FALSE, cache = FALSE} +degmods <- c( + fomc_path_2 = "FOMC path 2", + dfop_path_2 = "DFOP path 2", + sforb_path_2 = "SFORB path 2") +for (deg_mod in rownames(f_saem_3)) { + for (err_mod in c("const", "tc")) { + fit <- f_saem_3[[deg_mod, err_mod]] + if (!inherits(fit$so, "try-error")) { + caption <- paste("Hierarchical", degmods[deg_mod], "fit with reduced random effects,", errmods[err_mod]) + summary_listing(fit, caption) + } + } +} +``` + +## Session info + +```{r, echo = FALSE, cache = FALSE} +parallel::stopCluster(cl = cl) +sessionInfo() +``` + +## Hardware info + +```{r, echo = FALSE} +if(!inherits(try(cpuinfo <- readLines("/proc/cpuinfo")), "try-error")) { + cat(gsub("model name\t: ", "CPU model: ", cpuinfo[grep("model name", cpuinfo)[1]])) +} +if(!inherits(try(meminfo <- readLines("/proc/meminfo")), "try-error")) { + cat(gsub("model name\t: ", "System memory: ", meminfo[grep("MemTotal", meminfo)[1]])) +} +``` diff --git a/vignettes/prebuilt/2022_dmta_parent.rmd b/vignettes/prebuilt/2022_dmta_parent.rmd new file mode 100644 index 00000000..cc2a9124 --- /dev/null +++ b/vignettes/prebuilt/2022_dmta_parent.rmd @@ -0,0 +1,406 @@ +--- +title: "Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P" +author: Johannes Ranke +date: Last change on 5 January 2023, last compiled on `r format(Sys.time(), "%e %B %Y")` +geometry: margin=2cm +toc: true +bibliography: references.bib +output: + pdf_document: + extra_dependencies: ["float", "listing"] +--- + +```{r setup, echo = FALSE, cache = FALSE} +options(width = 80) # For summary listings +knitr::opts_chunk$set( + comment = "", tidy = FALSE, cache = TRUE, fig.pos = "H", fig.align = "center" +) +``` + +\clearpage + +# Introduction + +The purpose of this document is to demonstrate how nonlinear hierarchical +models (NLHM) based on the parent degradation models SFO, FOMC, DFOP and HS +can be fitted with the mkin package. + +It was assembled in the course of work package 1.1 of Project Number 173340 +(Application of nonlinear hierarchical models to the kinetic evaluation of +chemical degradation data) of the German Environment Agency carried out in 2022 +and 2023. + +The mkin package is used in version `r packageVersion("mkin")`. It contains the +test data and the functions used in the evaluations. The `saemix` package is +used as a backend for fitting the NLHM, but is also loaded to make the +convergence plot function available. + +This document is processed with the `knitr` package, which also provides the +`kable` function that is used to improve the display of tabular data in R +markdown documents. For parallel processing, the `parallel` package is used. + +```{r packages, cache = FALSE, message = FALSE} +library(mkin) +library(knitr) +library(saemix) +library(parallel) +n_cores <- detectCores() +if (Sys.info()["sysname"] == "Windows") { + cl <- makePSOCKcluster(n_cores) +} else { + cl <- makeForkCluster(n_cores) +} +``` + +\clearpage + +# Data + +The test data are available in the mkin package as an object of class +`mkindsg` (mkin dataset group) under the identifier `dimethenamid_2018`. The +following preprocessing steps are still necessary: + +- The data available for the enantiomer dimethenamid-P (DMTAP) are renamed + to have the same substance name as the data for the racemic mixture + dimethenamid (DMTA). The reason for this is that no difference between their + degradation behaviour was identified in the EU risk assessment. +- The data for transformation products and unnecessary columns are discarded +- The observation times of each dataset are multiplied with the + corresponding normalisation factor also available in the dataset, in order to + make it possible to describe all datasets with a single set of parameters + that are independent of temperature +- Finally, datasets observed in the same soil (`Elliot 1` and `Elliot 2`) are + combined, resulting in dimethenamid (DMTA) data from six soils. + +The following commented R code performs this preprocessing. + +```{r data} +# Apply a function to each of the seven datasets in the mkindsg object to create a list +dmta_ds <- lapply(1:7, function(i) { + ds_i <- dimethenamid_2018$ds[[i]]$data # Get a dataset + ds_i[ds_i$name == "DMTAP", "name"] <- "DMTA" # Rename DMTAP to DMTA + ds_i <- subset(ds_i, name == "DMTA", c("name", "time", "value")) # Select data + ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i] # Normalise time + ds_i # Return the dataset +}) + +# Use dataset titles as names for the list elements +names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) + +# Combine data for Elliot soil to obtain a named list with six elements +dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) # +dmta_ds[["Elliot 1"]] <- NULL +dmta_ds[["Elliot 2"]] <- NULL +``` + +\clearpage + +The following tables show the `r length(dmta_ds)` datasets. + +```{r results = "asis"} + +for (ds_name in names(dmta_ds)) { + print(kable(mkin_long_to_wide(dmta_ds[[ds_name]]), + caption = paste("Dataset", ds_name), + label = paste0("tab:", ds_name), booktabs = TRUE)) + cat("\n\\clearpage\n") +} +``` + +# Separate evaluations + +In order to obtain suitable starting parameters for the NLHM fits, separate +fits of the four models to the data for each soil are generated using the `mmkin` +function from the `mkin` package. In a first step, constant variance is assumed. +Convergence is checked with the `status` function. + +```{r f-sep-const, dependson = "data"} +deg_mods <- c("SFO", "FOMC", "DFOP", "HS") +f_sep_const <- mmkin( + deg_mods, + dmta_ds, + error_model = "const", + quiet = TRUE) + +status(f_sep_const) |> kable() +``` +In the table above, OK indicates convergence, and C indicates failure to +converge. All separate fits with constant variance converged, with the sole +exception of the HS fit to the BBA 2.2 data. To prepare for fitting NLHM using +the two-component error model, the separate fits are updated assuming +two-component error. + +```{r f-sep-tc, dependson = "f-sep-const"} +f_sep_tc <- update(f_sep_const, error_model = "tc") +status(f_sep_tc) |> kable() +``` + +Using the two-component error model, the one fit that did not converge with +constant variance did converge, but other non-SFO fits failed to converge. + +\clearpage + +# Hierarchichal model fits + +The following code fits eight versions of hierarchical models to the data, +using SFO, FOMC, DFOP and HS for the parent compound, and using either constant +variance or two-component error for the error model. The default parameter +distribution model in mkin allows for variation of all degradation parameters +across the assumed population of soils. In other words, each degradation +parameter is associated with a random effect as a first step. The `mhmkin` +function makes it possible to fit all eight versions in parallel (given a +sufficient number of computing cores being available) to save execution time. + +Convergence plots and summaries for these fits are shown in the appendix. + +```{r f-saem, dependson = c("f-sep-const", "f-sep-tc")} +f_saem <- mhmkin(list(f_sep_const, f_sep_tc), transformations = "saemix") +``` +The output of the `status` function shows that all fits terminated +successfully. + +```{r dependson = "f-saem"} +status(f_saem) |> kable() +``` +The AIC and BIC values show that the biphasic models DFOP and HS give the best +fits. + +```{r dependson = "f-saem"} +anova(f_saem) |> kable(digits = 1) +``` + +The DFOP model is preferred here, as it has a better mechanistic basis for +batch experiments with constant incubation conditions. Also, it shows the +lowest AIC and BIC values in the first set of fits when combined with the +two-component error model. Therefore, the DFOP model was selected for further +refinements of the fits with the aim to make the model fully identifiable. + +## Parameter identifiability based on the Fisher Information Matrix + +Using the `illparms` function, ill-defined statistical model parameters such +as standard deviations of the degradation parameters in the population and +error model parameters can be found. + +```{r dependson = "f-saem"} +illparms(f_saem) |> kable() +``` + +According to the `illparms` function, the fitted standard deviation of the +second kinetic rate constant `k2` is ill-defined in both DFOP fits. This +suggests that different values would be obtained for this standard deviation +when using different starting values. + +The thus identified overparameterisation is addressed by removing the random +effect for `k2` from the parameter model. + +```{r f-saem-dfop-tc-no-ranef-k2, dependson = "f-saem"} +f_saem_dfop_tc_no_ranef_k2 <- update(f_saem[["DFOP", "tc"]], + no_random_effect = "k2") +``` + +For the resulting fit, it is checked whether there are still ill-defined +parameters, + +```{r f-saem-dfop-tc-no-ranef-k2-illparms, dependson = "f-saem-dfop-tc-no-ranef-k2"} +illparms(f_saem_dfop_tc_no_ranef_k2) +``` +which is not the case. Below, the refined model is compared with the previous +best model. The model without random effect for `k2` is a reduced version of +the previous model. Therefore, the models are nested and can be compared using +the likelihood ratio test. This is achieved with the argument `test = TRUE` +to the `anova` function. + +```{r f-saem-dfop-tc-no-ranef-k2-comparison, dependson = "f-saem-dfop-tc-no-ranef-k2"} +anova(f_saem[["DFOP", "tc"]], f_saem_dfop_tc_no_ranef_k2, test = TRUE) |> + kable(format.args = list(digits = 4)) +``` + +The AIC and BIC criteria are lower after removal of the ill-defined random +effect for `k2`. The p value of the likelihood ratio test is much greater +than 0.05, indicating that the model with the higher likelihood (here +the model with random effects for all degradation parameters +`f_saem[["DFOP", "tc"]]`) does not fit significantly better than the model +with the lower likelihood (the reduced model `f_saem_dfop_tc_no_ranef_k2`). + +Therefore, AIC, BIC and likelihood ratio test suggest the use of the reduced model. + +The convergence of the fit is checked visually. + +```{r convergence-saem-dfop-tc-no-ranef-k2, fig.cap = "Convergence plot for the NLHM DFOP fit with two-component error and without a random effect on 'k2'", dependson = "f-saem-dfop-tc-no-ranef-k2", echo = FALSE, fig.width = 9, fig.height = 8} +plot(f_saem_dfop_tc_no_ranef_k2$so, plot.type = "convergence") +``` + +All parameters appear to have converged to a satisfactory degree. The final fit +is plotted using the plot method from the mkin package. + +```{r plot-saem-dfop-tc-no-ranef-k2, fig.cap = "Plot of the final NLHM DFOP fit", dependson = "f-saem-dfop-tc-no-ranef-k2", fig.width = 9, fig.height = 5} +plot(f_saem_dfop_tc_no_ranef_k2) +``` +Finally, a summary report of the fit is produced. + +```{r summary-saem-dfop-tc-no-ranef-k2, dependson = "f-saem-dfop-tc-no-ranef-k2"} +summary(f_saem_dfop_tc_no_ranef_k2) +``` + +\clearpage + +## Alternative check of parameter identifiability + +The parameter check used in the `illparms` function is based on a quadratic +approximation of the likelihood surface near its optimum, which is calculated +using the Fisher Information Matrix (FIM). An alternative way to check +parameter identifiability [@duchesne_2021] based on a multistart approach +has recently been implemented in mkin. + +The graph below shows boxplots of the parameters obtained in 50 runs of the +saem algorithm with different parameter combinations, sampled from the range of +the parameters obtained for the individual datasets fitted separately using +nonlinear regression. + +```{r multistart-full, dependson = "f-saem"} +f_saem_dfop_tc_multi <- multistart(f_saem[["DFOP", "tc"]], n = 50, cores = 15) +``` + +```{r multistart-full-par, dependson = "multistart_full", fig.cap = "Scaled parameters from the multistart runs, full model", fig.width = 10, fig.height = 6} +par(mar = c(6.1, 4.1, 2.1, 2.1)) +parplot(f_saem_dfop_tc_multi, lpos = "bottomright", ylim = c(0.3, 10), las = 2) +``` + +The graph clearly confirms the lack of identifiability of the variance of `k2` in +the full model. The overparameterisation of the model also indicates a lack of +identifiability of the variance of parameter `g`. + +The parameter boxplots of the multistart runs with the reduced model shown +below indicate that all runs give similar results, regardless of the starting +parameters. + +```{r multistart-reduced, dependson = "f-saem-dfop-tc-no-ranef-k2"} +f_saem_dfop_tc_no_ranef_k2_multi <- multistart(f_saem_dfop_tc_no_ranef_k2, + n = 50, cores = 15) +``` + +```{r multistart-reduced-par, dependson = "multistart_reduced", fig.cap = "Scaled parameters from the multistart runs, reduced model", fig.width = 10, fig.height = 5} +par(mar = c(6.1, 4.1, 2.1, 2.1)) +parplot(f_saem_dfop_tc_no_ranef_k2_multi, ylim = c(0.5, 2), las = 2, + lpos = "bottomright") +``` + +When only the parameters of the top 25% of the fits are shown (based on a feature +introduced in mkin 1.2.2 currently under development), the scatter is even less +as shown below. + +```{r multistart-reduced-par-llquant, dependson = "multistart_reduced", fig.cap = "Scaled parameters from the multistart runs, reduced model, fits with the top 25\\% likelihood values", fig.width = 10, fig.height = 5} +par(mar = c(6.1, 4.1, 2.1, 2.1)) +parplot(f_saem_dfop_tc_no_ranef_k2_multi, ylim = c(0.5, 2), las = 2, llquant = 0.25, + lpos = "bottomright") +``` + + +\clearpage + +# Conclusions + +Fitting the four parent degradation models SFO, FOMC, DFOP and HS as part of +hierarchical model fits with two different error models and normal +distributions of the transformed degradation parameters works without technical +problems. The biphasic models DFOP and HS gave the best fit to the data, but +the default parameter distribution model was not fully identifiable. Removing +the random effect for the second kinetic rate constant of the DFOP model +resulted in a reduced model that was fully identifiable and showed the lowest +values for the model selection criteria AIC and BIC. The reliability of the +identification of all model parameters was confirmed using multiple starting +values. + +# Acknowledgements + +The helpful comments by Janina Wöltjen of the German Environment Agency +are gratefully acknowledged. + +# References + +\vspace{1em} + +::: {#refs} +::: + +\clearpage + +# Appendix + +## Hierarchical model fit listings + +```{r listings, results = "asis", echo = FALSE} +for (deg_mod in deg_mods) { + for (err_mod in c("const", "tc")) { + caption <- paste("Hierarchical mkin fit of the", deg_mod, "model with error model", err_mod) + summary_listing(f_saem[[deg_mod, err_mod]], caption) + } +} +``` + +## Hierarchical model convergence plots + +```{r convergence-saem-sfo-const, fig.cap = "Convergence plot for the NLHM SFO fit with constant variance", dependson = "f-saem", echo = FALSE, fig.width = 9, fig.height = 5} +plot(f_saem[["SFO", "const"]]$so, plot.type = "convergence") +``` + +\clearpage + +```{r convergence-saem-sfo-tc, fig.cap = "Convergence plot for the NLHM SFO fit with two-component error", dependson = "f-saem", echo = FALSE, fig.width = 9, fig.height = 5} +plot(f_saem[["SFO", "tc"]]$so, plot.type = "convergence") +``` + +\clearpage + +```{r convergence-saem-fomc-const, fig.cap = "Convergence plot for the NLHM FOMC fit with constant variance", dependson = "f-saem", echo = FALSE, fig.width = 9, fig.height = 8} +plot(f_saem[["FOMC", "const"]]$so, plot.type = "convergence") +``` + +\clearpage + +```{r convergence-saem-fomc-tc, fig.cap = "Convergence plot for the NLHM FOMC fit with two-component error", dependson = "f-saem", echo = FALSE, fig.width = 9, fig.height = 8} +plot(f_saem[["FOMC", "tc"]]$so, plot.type = "convergence") +``` + +\clearpage + +```{r convergence-saem-dfop-const, fig.cap = "Convergence plot for the NLHM DFOP fit with constant variance", dependson = "f-saem", echo = FALSE, fig.width = 9, fig.height = 8} +plot(f_saem[["DFOP", "const"]]$so, plot.type = "convergence") +``` + +\clearpage + +```{r convergence-saem-dfop-tc, fig.cap = "Convergence plot for the NLHM DFOP fit with two-component error", dependson = "f-saem", echo = FALSE, fig.width = 9, fig.height = 8} +plot(f_saem[["DFOP", "tc"]]$so, plot.type = "convergence") +``` +\clearpage + +```{r convergence-saem-hs-const, fig.cap = "Convergence plot for the NLHM HS fit with constant variance", dependson = "f-saem", echo = FALSE, fig.width = 9, fig.height = 8} +plot(f_saem[["HS", "const"]]$so, plot.type = "convergence") +``` + +\clearpage + +```{r convergence-saem-hs-tc, fig.cap = "Convergence plot for the NLHM HS fit with two-component error", dependson = "f-saem", echo = FALSE, fig.width = 9, fig.height = 8} +plot(f_saem[["HS", "tc"]]$so, plot.type = "convergence") +``` + +\clearpage + +## Session info + +```{r, echo = FALSE} +parallel::stopCluster(cl) +sessionInfo() +``` + +## Hardware info + +```{r, echo = FALSE} +if(!inherits(try(cpuinfo <- readLines("/proc/cpuinfo")), "try-error")) { + cat(gsub("model name\t: ", "CPU model: ", cpuinfo[grep("model name", cpuinfo)[1]])) +} +if(!inherits(try(meminfo <- readLines("/proc/meminfo")), "try-error")) { + cat(gsub("model name\t: ", "System memory: ", meminfo[grep("MemTotal", meminfo)[1]])) +} +``` diff --git a/vignettes/prebuilt/2022_dmta_pathway.rmd b/vignettes/prebuilt/2022_dmta_pathway.rmd new file mode 100644 index 00000000..ff2b527c --- /dev/null +++ b/vignettes/prebuilt/2022_dmta_pathway.rmd @@ -0,0 +1,426 @@ +--- +title: "Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P" +author: Johannes Ranke +date: Last change on 8 January 2023, last compiled on `r format(Sys.time(), "%e %B %Y")` +geometry: margin=2cm +bibliography: references.bib +toc: true +output: + pdf_document: + extra_dependencies: ["float", "listing"] +--- + +```{r setup, echo = FALSE, cache = FALSE} +options(width = 80) # For summary listings +knitr::opts_chunk$set( + comment = "", tidy = FALSE, cache = TRUE, fig.pos = "H", fig.align = "center" +) +``` + +\clearpage + +# Introduction + +The purpose of this document is to test demonstrate how nonlinear hierarchical +models (NLHM) based on the parent degradation models SFO, FOMC, DFOP and HS, +with parallel formation of two or more metabolites can be fitted with the mkin package. + +It was assembled in the course of work package 1.2 of Project Number 173340 +(Application of nonlinear hierarchical models to the kinetic evaluation of +chemical degradation data) of the German Environment Agency carried out in 2022 +and 2023. + +The mkin package is used in version `r packageVersion("mkin")`, which is currently +under development. It contains the test data, and the functions used in the +evaluations. The `saemix` package is used as a backend for fitting the NLHM, +but is also loaded to make the convergence plot function available. + +This document is processed with the `knitr` package, which also provides the +`kable` function that is used to improve the display of tabular data in R +markdown documents. For parallel processing, the `parallel` package is used. + +```{r, packages, cache = FALSE, message = FALSE} +library(mkin) +library(knitr) +library(saemix) +library(parallel) +n_cores <- detectCores() +if (Sys.info()["sysname"] == "Windows") { + cl <- makePSOCKcluster(n_cores) +} else { + cl <- makeForkCluster(n_cores) +} +``` + +\clearpage + +# Data + +The test data are available in the mkin package as an object of class `mkindsg` +(mkin dataset group) under the identifier `dimethenamid_2018`. The following +preprocessing steps are done in this document. + +- The data available for the enantiomer dimethenamid-P (DMTAP) are renamed + to have the same substance name as the data for the racemic mixture + dimethenamid (DMTA). The reason for this is that no difference between their + degradation behaviour was identified in the EU risk assessment. +- Unnecessary columns are discarded +- The observation times of each dataset are multiplied with the + corresponding normalisation factor also available in the dataset, in order to + make it possible to describe all datasets with a single set of parameters + that are independent of temperature +- Finally, datasets observed in the same soil (`Elliot 1` and `Elliot 2`) are + combined, resulting in dimethenamid (DMTA) data from six soils. + +The following commented R code performs this preprocessing. + +```{r, data} +# Apply a function to each of the seven datasets in the mkindsg object to create a list +dmta_ds <- lapply(1:7, function(i) { + ds_i <- dimethenamid_2018$ds[[i]]$data # Get a dataset + ds_i[ds_i$name == "DMTAP", "name"] <- "DMTA" # Rename DMTAP to DMTA + ds_i <- subset(ds_i, select = c("name", "time", "value")) # Select data + ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i] # Normalise time + ds_i # Return the dataset +}) + +# Use dataset titles as names for the list elements +names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) + +# Combine data for Elliot soil to obtain a named list with six elements +dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) # +dmta_ds[["Elliot 1"]] <- NULL +dmta_ds[["Elliot 2"]] <- NULL +``` + +\clearpage + +The following tables show the `r length(dmta_ds)` datasets. + +```{r show-data, dependson = "data", results = "asis"} +for (ds_name in names(dmta_ds)) { + print( + kable(mkin_long_to_wide(dmta_ds[[ds_name]]), + caption = paste("Dataset", ds_name), + booktabs = TRUE, row.names = FALSE)) + cat("\n\\clearpage\n") +} +``` + +# Separate evaluations + +As a first step to obtain suitable starting parameters for the NLHM fits, we do +separate fits of several variants of the pathway model used previously +[@ranke2021], varying the kinetic model for the parent compound. Because the +SFORB model often provides faster convergence than the DFOP model, and can +sometimes be fitted where the DFOP model results in errors, it is included in +the set of parent models tested here. + +```{r, sep-1-const, dependson = "data"} +if (!dir.exists("dmta_dlls")) dir.create("dmta_dlls") +m_sfo_path_1 <- mkinmod( + DMTA = mkinsub("SFO", c("M23", "M27", "M31")), + M23 = mkinsub("SFO"), + M27 = mkinsub("SFO"), + M31 = mkinsub("SFO", "M27", sink = FALSE), + name = "m_sfo_path", dll_dir = "dmta_dlls", + unload = TRUE, overwrite = TRUE, + quiet = TRUE +) +m_fomc_path_1 <- mkinmod( + DMTA = mkinsub("FOMC", c("M23", "M27", "M31")), + M23 = mkinsub("SFO"), + M27 = mkinsub("SFO"), + M31 = mkinsub("SFO", "M27", sink = FALSE), + name = "m_fomc_path", dll_dir = "dmta_dlls", + unload = TRUE, overwrite = TRUE, + quiet = TRUE +) +m_dfop_path_1 <- mkinmod( + DMTA = mkinsub("DFOP", c("M23", "M27", "M31")), + M23 = mkinsub("SFO"), + M27 = mkinsub("SFO"), + M31 = mkinsub("SFO", "M27", sink = FALSE), + name = "m_dfop_path", dll_dir = "dmta_dlls", + unload = TRUE, overwrite = TRUE, + quiet = TRUE +) +m_sforb_path_1 <- mkinmod( + DMTA = mkinsub("SFORB", c("M23", "M27", "M31")), + M23 = mkinsub("SFO"), + M27 = mkinsub("SFO"), + M31 = mkinsub("SFO", "M27", sink = FALSE), + name = "m_sforb_path", dll_dir = "dmta_dlls", + unload = TRUE, overwrite = TRUE, + quiet = TRUE +) +m_hs_path_1 <- mkinmod( + DMTA = mkinsub("HS", c("M23", "M27", "M31")), + M23 = mkinsub("SFO"), + M27 = mkinsub("SFO"), + M31 = mkinsub("SFO", "M27", sink = FALSE), + name = "m_hs_path", dll_dir = "dmta_dlls", + unload = TRUE, overwrite = TRUE, + quiet = TRUE +) +deg_mods_1 <- list( + sfo_path_1 = m_sfo_path_1, + fomc_path_1 = m_fomc_path_1, + dfop_path_1 = m_dfop_path_1, + sforb_path_1 = m_sforb_path_1, + hs_path_1 = m_hs_path_1) + +sep_1_const <- mmkin( + deg_mods_1, + dmta_ds, + error_model = "const", + quiet = TRUE) + +status(sep_1_const) |> kable() +``` + +All separate pathway fits with SFO or FOMC for the parent and constant variance +converged (status OK). Most fits with DFOP or SFORB for the parent converged +as well. The fits with HS for the parent did not converge with default settings. + +```{r, sep-1-tc, dependson = "sep-1-const"} +sep_1_tc <- update(sep_1_const, error_model = "tc") +status(sep_1_tc) |> kable() +``` + +With the two-component error model, the set of fits with convergence problems +is slightly different, with convergence problems appearing for different data +sets when applying the DFOP and SFORB model and some additional convergence +problems when using the FOMC model for the parent. + +\clearpage + +# Hierarchichal model fits + +The following code fits two sets of the corresponding hierarchical models to +the data, one assuming constant variance, and one assuming two-component error. + +```{r saem-1, dependson = c("sep-1-const", "sep-1-tc")} +saem_1 <- mhmkin(list(sep_1_const, sep_1_tc)) +``` +The run time for these fits was around two hours on five year old hardware. After +a recent hardware upgrade these fits complete in less than twenty minutes. + +```{r, saem-1-status, dependson = "saem-1"} +status(saem_1) |> kable() +``` + +According to the `status` function, all fits terminated successfully. + +```{r saem-1-anova, dependson = "saem-1"} +anova(saem_1) |> kable(digits = 1) +``` + +When the goodness-of-fit of the models is compared, a warning is obtained, +indicating that the likelihood of the pathway fit with SFORB for the parent +compound and constant variance could not be calculated with importance sampling +(method 'is'). As this is the default method on which all AIC and BIC +comparisons are based, this variant is not included in the model comparison +table. Comparing the goodness-of-fit of the remaining models, HS model model +with two-component error provides the best fit. However, for batch experiments +performed with constant conditions such as the experiments evaluated here, +there is no reason to assume a discontinuity, so the SFORB model is +preferable from a mechanistic viewpoint. In addition, the information criteria +AIC and BIC are very similar for HS and SFORB. Therefore, the SFORB model is +selected here for further refinements. + +\clearpage + +## Parameter identifiability based on the Fisher Information Matrix + +Using the `illparms` function, ill-defined statistical model parameters such as +standard deviations of the degradation parameters in the population and error +model parameters can be found. + +```{r saem-1-illparms, dependson = "saem-1"} +illparms(saem_1) |> kable() +``` + +When using constant variance, no ill-defined variance parameters are identified +with the `illparms` function in any of the degradation models. When using +the two-component error model, there is one ill-defined variance parameter +in all variants except for the variant using DFOP for the parent compound. + +For the selected combination of the SFORB pathway model with two-component +error, the random effect for the rate constant from reversibly bound DMTA to +the free DMTA (`k_DMTA_bound_free`) is not well-defined. Therefore, the fit is +updated without assuming a random effect for this parameter. + +```{r saem-sforb-path-1-tc-reduced, dependson = "saem-1"} +saem_sforb_path_1_tc_reduced <- update(saem_1[["sforb_path_1", "tc"]], + no_random_effect = "log_k_DMTA_bound_free") +illparms(saem_sforb_path_1_tc_reduced) +``` + +As expected, no ill-defined parameters remain. The model comparison below shows +that the reduced model is preferable. + +```{r saem-sforb-path-1-tc-reduced-anova, dependson = "saem-sforb-path-1-tc-reduced"} +anova(saem_1[["sforb_path_1", "tc"]], saem_sforb_path_1_tc_reduced) |> kable(digits = 1) +``` + +The convergence plot of the refined fit is shown below. + +```{r saem-sforb-path-1-tc-reduced-convergence, dependson = "saem-sforb-path-1-tc-reduced", fig.height = 12} +plot(saem_sforb_path_1_tc_reduced$so, plot.type = "convergence") +``` + +For some parameters, for example for `f_DMTA_ilr_1` and `f_DMTA_ilr_2`, i.e. +for two of the parameters determining the formation fractions of the parallel +formation of the three metabolites, some movement of the parameters is still +visible in the second phase of the algorithm. However, the amplitude of this +movement is in the range of the amplitude towards the end of the first phase. +Therefore, it is likely that an increase in iterations would not improve the +parameter estimates very much, and it is proposed that the fit is acceptable. +No numeric convergence criterion is implemented in saemix. + +\clearpage + +## Alternative check of parameter identifiability + +As an alternative check of parameter identifiability [@duchesne_2021], +multistart runs were performed on the basis of the refined fit shown above. + +```{r saem-sforb-multistart, dependson = "saem-sforb-path-1-tc-reduced"} +saem_sforb_path_1_tc_reduced_multi <- multistart(saem_sforb_path_1_tc_reduced, + n = 32, cores = 10) +``` + +```{r dependson = "saem-sforb-multistart"} +print(saem_sforb_path_1_tc_reduced_multi) +``` + +Out of the 32 fits that were initiated, only 17 terminated without an error. +The reason for this is that the wide variation of starting parameters in combination +with the parameter variation that is used in the SAEM algorithm leads to +parameter combinations for the degradation model that the numerical integration +routine cannot cope with. Because of this variation of initial parameters, +some of the model fits take up to two times more time than the original fit. + +```{r dependson = "saem-sforb-multistart", fig.cap = "Parameter boxplots for the multistart runs that succeeded", fig.height = 6, fig.width = 10} +par(mar = c(12.1, 4.1, 2.1, 2.1)) +parplot(saem_sforb_path_1_tc_reduced_multi, ylim = c(0.5, 2), las = 2) +``` + +However, visual analysis of the boxplot of the parameters obtained in the +successful fits confirms that the results are sufficiently independent of the +starting parameters, and there are no remaining ill-defined parameters. + +\clearpage + + +# Plots of selected fits + +The SFORB pathway fits with full and reduced parameter distribution model are +shown below. + +```{r fig.cap = "SFORB pathway fit with two-component error", dependson = "saem-1", fig.height = 8} +plot(saem_1[["sforb_path_1", "tc"]]) +``` + +\clearpage + +```{r fig.cap = "SFORB pathway fit with two-component error, reduced parameter model", dependson = "saem-sforb-path-1-tc-reduced", fig.height = 8} +plot(saem_sforb_path_1_tc_reduced) +``` + +Plots of the remaining fits and listings for all successful fits are shown in +the Appendix. + + +# Conclusions + +Pathway fits with SFO, FOMC, DFOP, SFORB and HS models for the parent compound +could be successfully performed. + +\clearpage + +# Acknowledgements + +The helpful comments by Janina Wöltjen of the German Environment Agency +on earlier versions of this document are gratefully acknowledged. + +# References + +\vspace{1em} + +::: {#refs} +::: + +\clearpage + +# Appendix + +## Plots of hierarchical fits not selected for refinement + +```{r fig.cap = "SFO pathway fit with two-component error", dependson = "saem-1", fig.height = 8} +plot(saem_1[["sfo_path_1", "tc"]]) +``` + +\clearpage + +```{r fig.cap = "FOMC pathway fit with two-component error", dependson = "saem-1", fig.height = 8} +plot(saem_1[["fomc_path_1", "tc"]]) +``` + +\clearpage + + +```{r fig.cap = "HS pathway fit with two-component error", dependson = "saem-1", fig.height = 8} +plot(saem_1[["sforb_path_1", "tc"]]) +``` + +\clearpage + +## Hierarchical model fit listings + +### Fits with random effects for all degradation parameters + +```{r listings-1, results = "asis", echo = FALSE} +errmods <- c(const = "constant variance", tc = "two-component error") +degmods <- c( + sfo_path_1 = "SFO path 1", + fomc_path_1 = "FOMC path 1", + dfop_path_1 = "DFOP path 1", + sforb_path_1 = "SFORB path 1", + hs_path_1 = "HS path 1") +for (deg_mod in rownames(saem_1)) { + for (err_mod in c("const", "tc")) { + fit <- saem_1[[deg_mod, err_mod]] + if (!inherits(fit$so, "try-error")) { + caption <- paste("Hierarchical", degmods[deg_mod], "fit with", errmods[err_mod]) + tex_listing(fit, caption) + } + } +} +``` + +### Improved fit of the SFORB pathway model with two-component error + +```{r listings-2, results = "asis", echo = FALSE, dependson = "listings-1"} +caption <- paste("Hierarchical SFORB pathway fit with two-component error") +tex_listing(saem_sforb_path_1_tc_reduced, caption) +``` + +## Session info + +```{r, echo = FALSE} +parallel::stopCluster(cl) +sessionInfo() +``` + +## Hardware info + +```{r, echo = FALSE} +if(!inherits(try(cpuinfo <- readLines("/proc/cpuinfo")), "try-error")) { + cat(gsub("model name\t: ", "CPU model: ", cpuinfo[grep("model name", cpuinfo)[1]])) +} +if(!inherits(try(meminfo <- readLines("/proc/meminfo")), "try-error")) { + cat(gsub("model name\t: ", "System memory: ", meminfo[grep("MemTotal", meminfo)[1]])) +} +``` diff --git a/vignettes/prebuilt/references.bib b/vignettes/prebuilt/references.bib new file mode 100644 index 00000000..08c51747 --- /dev/null +++ b/vignettes/prebuilt/references.bib @@ -0,0 +1,187 @@ +@BOOK{bates1988, + title = {Nonlinear regression and its applications}, + publisher = {Wiley-Interscience}, + year = {1988}, + author = {D. Bates and D. Watts} +} + +@MANUAL{FOCUSkinetics2011, + title = {Generic guidance for estimating persistence and degradation kinetics + from environmental fate studies on pesticides in EU registration}, + author = {{FOCUS Work Group on Degradation Kinetics}}, + edition = {1.0}, + month = {November}, + year = {2011}, + file = {FOCUS kinetics 2011 Generic guidance:/home/ranke/dok/orgs/focus/FOCUSkineticsvc_1_0_Nov23.pdf:PDF}, + url = {http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics} +} + +@MANUAL{FOCUSkinetics2014, + title = {Generic guidance for estimating persistence and degradation kinetics + from environmental fate studies on pesticides in EU registration}, + author = {{FOCUS Work Group on Degradation Kinetics}}, + edition = {1.1}, + month = {December}, + year = {2014}, + file = {FOCUS kinetics 2011 Generic guidance:/home/ranke/dok/orgs/focus/dk/FOCUSkineticsvc1.1Dec2014.pdf:PDF}, + url = {http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics} +} + +@MANUAL{FOCUS2006, + title = {Guidance Document on Estimating Persistence and Degradation Kinetics + from Environmental Fate Studies on Pesticides in EU Registration. + Report of the FOCUS Work Group on Degradation Kinetics}, + author = {{FOCUS Work Group on Degradation Kinetics}}, + year = {2006}, + note = {EC Document Reference Sanco/10058/2005 version 2.0}, + url = {http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics} +} + +@MANUAL{rcore2016, + title = {\textsf{R}: A Language and Environment for Statistical Computing}, + author = {{R Development Core Team}}, + organization = {R Foundation for Statistical Computing}, + address = {Vienna, Austria}, + year = {2016}, + note = {{ISBN} 3-900051-07-0}, + url = {https://www.R-project.org} +} + +@MANUAL{pkg:mkin, + title = {`{mkin}`: {K}inetic evaluation of chemical degradation data}, + author = {J. Ranke}, + year = {2021}, + url = {https://CRAN.R-project.org/package=mkin} +} + +@Inproceedings{ schaefer2007, + title = {{KinGUI}: a new kinetic software tool for evaluations according to {FOCUS} degradation kinetics}, + author = {D. Sch\"{a}fer and B. Mikolasch and P. Rainbird and B. Harvey}, + booktitle = {Proceedings of the XIII Symposium Pesticide Chemistry}, + editor = {Del Re A. A. M. and Capri E. and Fragoulis G. and Trevisan M.}, + year = {2007}, + address = {Piacenza}, + pages = {916--923} +} + +@ARTICLE{soetaert2010, + author = {Karline Soetaert and Thomas Petzoldt}, + title = {Inverse Modelling, Sensitivity and Monte Carlo Analysis in {R} Using + Package {FME}}, + journal = {Journal of Statistical Software}, + year = {2010}, + volume = {33}, + pages = {1--28}, + number = {3}, + doi = {10.18637/jss.v033.i03} +} + +@Inproceedings{ ranke2012, + title = {Parameter reliability in kinetic evaluation of environmental metabolism data - Assessment and the influence of model specification}, + author = {J. Ranke and R. Lehmann}, + booktitle = {SETAC World 20-24 May}, + year = {2012}, + address = {Berlin}, + url = {https://jrwb.de/posters/Poster_SETAC_2012_Kinetic_parameter_uncertainty_model_parameterization_Lehmann_Ranke.pdf} +} +@Inproceedings{ ranke2015, + title = {To t-test or not to t-test, that is the question}, + author = {J. Ranke and R. Lehmann}, + booktitle = {XV Symposium on Pesticide Chemistry 2-4 September 2015}, + year = {2015}, + address = {Piacenza}, + url = {https://jrwb.de/posters/piacenza_2015.pdf} +} +@Techreport{ranke2014, + title = {{Prüfung und Validierung von Modellierungssoftware als Alternative zu + ModelMaker 4.0}}, + author = {J. Ranke}, + year = 2014, + institution = {Umweltbundesamt}, + volume = {Projektnummer 27452} +} + +@Article{ranke2018, + author="Ranke, Johannes + and W{\"o}ltjen, Janina + and Meinecke, Stefan", + title="Comparison of software tools for kinetic evaluation of chemical degradation data", + journal="Environmental Sciences Europe", + year="2018", + month="May", + day="18", + volume="30", + number="1", + pages="17", + abstract="For evaluating the fate of xenobiotics in the environment, a variety of degradation or environmental metabolism experiments are routinely conducted. The data generated in such experiments are evaluated by optimizing the parameters of kinetic models in a way that the model simulation fits the data. No comparison of the main software tools currently in use has been published to date. This article shows a comparison of numerical results as well as an overall, somewhat subjective comparison based on a scoring system using a set of criteria. The scoring was separately performed for two types of uses. Uses of type I are routine evaluations involving standard kinetic models and up to three metabolites in a single compartment. Evaluations involving non-standard model components, more than three metabolites or more than a single compartment belong to use type II. For use type I, usability is most important, while the flexibility of the model definition is most important for use type II.", + issn="2190-4715", + doi="10.1186/s12302-018-0145-1", + url="https://doi.org/10.1186/s12302-018-0145-1" +} + +@Article{ranke2019, + author = {Ranke, Johannes and Meinecke, Stefan}, + title = {Error Models for the Kinetic Evaluation of Chemical Degradation Data}, + journal = {Environments}, + year = {2019}, + volume = {6}, + number = {12}, + issn = {2076-3298}, + abstract = {In the kinetic evaluation of chemical degradation data, degradation models are fitted to the data by varying degradation model parameters to obtain the best possible fit. Today, constant variance of the deviations of the observed data from the model is frequently assumed (error model “constant variance”). Allowing for a different variance for each observed variable (“variance by variable”) has been shown to be a useful refinement. On the other hand, experience gained in analytical chemistry shows that the absolute magnitude of the analytical error often increases with the magnitude of the observed value, which can be explained by an error component which is proportional to the true value. Therefore, kinetic evaluations of chemical degradation data using a two-component error model with a constant component (absolute error) and a component increasing with the observed values (relative error) are newly proposed here as a third possibility. In order to check which of the three error models is most adequate, they have been used in the evaluation of datasets obtained from pesticide evaluation dossiers published by the European Food Safety Authority (EFSA). For quantitative comparisons of the fits, the Akaike information criterion (AIC) was used, as the commonly used error level defined by the FOrum for the Coordination of pesticide fate models and their USe(FOCUS) is based on the assumption of constant variance. A set of fitting routines was developed within the mkin software package that allow for robust fitting of all three error models. Comparisons using parent only degradation datasets, as well as datasets with the formation and decline of transformation products showed that in many cases, the two-component error model proposed here provides the most adequate description of the error structure. While it was confirmed that the variance by variable error model often provides an improved representation of the error structure in kinetic fits with metabolites, it could be shown that in many cases, the two-component error model leads to a further improvement. In addition, it can be applied to parent only fits, potentially improving the accuracy of the fit towards the end of the decline curve, where concentration levels are lower.}, + article-number = {124}, + doi = {10.3390/environments6120124}, + url = {https://www.mdpi.com/2076-3298/6/12/124}, +} + + +@Article{ranke2021, + AUTHOR = {Ranke, Johannes and Wöltjen, Janina and Schmidt, Jana and Comets, Emmanuelle}, + TITLE = {Taking Kinetic Evaluations of Degradation Data to the Next Level with Nonlinear Mixed-Effects Models}, + JOURNAL = {Environments}, + VOLUME = {8}, + YEAR = {2021}, + NUMBER = {8}, + ARTICLE-NUMBER = {71}, + URL = {https://www.mdpi.com/2076-3298/8/8/71}, + ISSN = {2076-3298}, + ABSTRACT = {When data on the degradation of a chemical substance have been collected in a number of environmental media (e.g., in different soils), two strategies can be followed for data evaluation. Currently, each individual dataset is evaluated separately, and representative degradation parameters are obtained by calculating averages of the kinetic parameters. However, such averages often take on unrealistic values if certain degradation parameters are ill-defined in some of the datasets. Moreover, the most appropriate degradation model is selected for each individual dataset, which is time consuming and then requires workarounds for averaging parameters from different models. Therefore, a simultaneous evaluation of all available data is desirable. If the environmental media are viewed as random samples from a population, an advanced strategy based on assumptions about the statistical distribution of the kinetic parameters across the population can be used. Here, we show the advantages of such simultaneous evaluations based on nonlinear mixed-effects models that incorporate such assumptions in the evaluation process. The advantages of this approach are demonstrated using synthetically generated data with known statistical properties and using publicly available experimental degradation data on two pesticidal active substances.}, + DOI = {10.3390/environments8080071} +} + +@Article{gao11, + Title = {Improving uncertainty analysis in kinetic evaluations using iteratively reweighted least squares}, + Author = {Gao, Z. and Green, J.W. and Vanderborght, J. and Schmitt, W.}, + Journal = {Environmental Science and Technology}, + Year = {2011}, + Pages = {4429-4437}, + Volume = {45}, + Type = {Journal} +} + + +@article{efsa_2018_dimethenamid, + author = {EFSA}, + issue = {4}, + journal = {EFSA Journal}, + pages = {5211}, + title = {Peer review of the pesticide risk assessment of the active substance dimethenamid-P}, + volume = {16}, + year = {2018} +} + +@techreport{dimethenamid_rar_2018_b8, + author = {{Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria}}, + year = {2018}, + title = {{Renewal Assessment Report Dimethenamid-P Volume 3 - B.8 Environmental fate and behaviour, Rev. 2 - November 2017}}, + url = {https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716} +} + +@article{duchesne_2021, + title={Practical identifiability in the frame of nonlinear mixed effects models: the example of the in vitro erythropoiesis}, + author={Ronan Duchesne and Anissa Guillemin and Olivier Gandrillon and Fabien Crauste}, + journal={BMC Bioinformatics}, + year={2021}, + volume={22}, + number = {478}, + url = {https://doi.org/10.1186/s12859-021-04373-4} +} diff --git a/vignettes/references.bib b/vignettes/references.bib index 69845b1b..08c51747 100644 --- a/vignettes/references.bib +++ b/vignettes/references.bib @@ -7,7 +7,7 @@ @MANUAL{FOCUSkinetics2011, title = {Generic guidance for estimating persistence and degradation kinetics - from environmental fate studies on pesticides in EU registration}, + from environmental fate studies on pesticides in EU registration}, author = {{FOCUS Work Group on Degradation Kinetics}}, edition = {1.0}, month = {November}, @@ -18,7 +18,7 @@ @MANUAL{FOCUSkinetics2014, title = {Generic guidance for estimating persistence and degradation kinetics - from environmental fate studies on pesticides in EU registration}, + from environmental fate studies on pesticides in EU registration}, author = {{FOCUS Work Group on Degradation Kinetics}}, edition = {1.1}, month = {December}, @@ -29,8 +29,8 @@ @MANUAL{FOCUS2006, title = {Guidance Document on Estimating Persistence and Degradation Kinetics - from Environmental Fate Studies on Pesticides in EU Registration. - Report of the FOCUS Work Group on Degradation Kinetics}, + from Environmental Fate Studies on Pesticides in EU Registration. + Report of the FOCUS Work Group on Degradation Kinetics}, author = {{FOCUS Work Group on Degradation Kinetics}}, year = {2006}, note = {EC Document Reference Sanco/10058/2005 version 2.0}, @@ -67,7 +67,7 @@ @ARTICLE{soetaert2010, author = {Karline Soetaert and Thomas Petzoldt}, title = {Inverse Modelling, Sensitivity and Monte Carlo Analysis in {R} Using - Package {FME}}, + Package {FME}}, journal = {Journal of Statistical Software}, year = {2010}, volume = {33}, @@ -160,13 +160,13 @@ @article{efsa_2018_dimethenamid, - author = {EFSA}, - issue = {4}, - journal = {EFSA Journal}, - pages = {5211}, - title = {Peer review of the pesticide risk assessment of the active substance dimethenamid-P}, - volume = {16}, - year = {2018} + author = {EFSA}, + issue = {4}, + journal = {EFSA Journal}, + pages = {5211}, + title = {Peer review of the pesticide risk assessment of the active substance dimethenamid-P}, + volume = {16}, + year = {2018} } @techreport{dimethenamid_rar_2018_b8, @@ -175,3 +175,13 @@ title = {{Renewal Assessment Report Dimethenamid-P Volume 3 - B.8 Environmental fate and behaviour, Rev. 2 - November 2017}}, url = {https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716} } + +@article{duchesne_2021, + title={Practical identifiability in the frame of nonlinear mixed effects models: the example of the in vitro erythropoiesis}, + author={Ronan Duchesne and Anissa Guillemin and Olivier Gandrillon and Fabien Crauste}, + journal={BMC Bioinformatics}, + year={2021}, + volume={22}, + number = {478}, + url = {https://doi.org/10.1186/s12859-021-04373-4} +} diff --git a/vignettes/twa.html b/vignettes/twa.html index fd0fe2d6..0b079516 100644 --- a/vignettes/twa.html +++ b/vignettes/twa.html @@ -28,22 +28,6 @@ document.addEventListener('DOMContentLoaded', function(e) { } }); </script> -<script>// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - -document.addEventListener('DOMContentLoaded', function() { - const codeList = document.getElementsByClassName("sourceCode"); - for (var i = 0; i < codeList.length; i++) { - var linkList = codeList[i].getElementsByTagName('a'); - for (var j = 0; j < linkList.length; j++) { - if (linkList[j].innerHTML === "") { - linkList[j].setAttribute('aria-hidden', 'true'); - } - } - } -}); -</script> <style type="text/css"> code{white-space: pre-wrap;} @@ -59,6 +43,29 @@ document.addEventListener('DOMContentLoaded', function() { +<style type="text/css"> +/* for pandoc --citeproc since 2.11 */ +div.csl-bib-body { } +div.csl-entry { + clear: both; +} +.hanging div.csl-entry { + margin-left:2em; + text-indent:-2em; +} +div.csl-left-margin { + min-width:2em; + float:left; +} +div.csl-right-inline { + margin-left:2em; + padding-left:1em; +} +div.csl-indent { + margin-left: 2em; +} +</style> + <style type="text/css">body { background-color: #fff; margin: 1em auto; @@ -251,19 +258,32 @@ code > span.er { color: #a61717; background-color: #e3d2d2; } -<h1 class="title toc-ignore">Calculation of time weighted average concentrations with mkin</h1> +<h1 class="title toc-ignore">Calculation of time weighted average +concentrations with mkin</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 18 September 2019 (rebuilt 2022-03-02)</h4> +<h4 class="date">Last change 18 September 2019 (rebuilt 2023-01-05)</h4> -<p>Since version 0.9.45.1 of the ‘mkin’ package, a function for calculating time weighted average concentrations for decline kinetics (<em>i.e.</em> only for the compound applied in the experiment) is included. Strictly speaking, they are maximum moving window time weighted average concentrations, <em>i.e.</em> the maximum time weighted average concentration that can be found when moving a time window of a specified width over the decline curve.</p> -<p>Time weighted average concentrations for the SFO, FOMC and the DFOP model are calculated using the formulas given in the FOCUS kinetics guidance <span class="citation">(FOCUS Work Group on Degradation Kinetics 2014, 251)</span>:</p> +<p>Since version 0.9.45.1 of the ‘mkin’ package, a function for +calculating time weighted average concentrations for decline kinetics +(<em>i.e.</em> only for the compound applied in the experiment) is +included. Strictly speaking, they are maximum moving window time +weighted average concentrations, <em>i.e.</em> the maximum time weighted +average concentration that can be found when moving a time window of a +specified width over the decline curve.</p> +<p>Time weighted average concentrations for the SFO, FOMC and the DFOP +model are calculated using the formulas given in the FOCUS kinetics +guidance <span class="citation">(FOCUS Work Group on Degradation +Kinetics 2014, 251)</span>:</p> <p>SFO:</p> -<p><span class="math display">\[c_\textrm{twa} = c_0 \frac{\left( 1 - e^{- k t} \right)}{ k t} \]</span></p> +<p><span class="math display">\[c_\textrm{twa} = c_0 \frac{\left( 1 - +e^{- k t} \right)}{ k t} \]</span></p> <p>FOMC:</p> -<p><span class="math display">\[c_\textrm{twa} = c_0 \frac{\beta}{t (1 - \alpha)} - \left( \left(\frac{t}{\beta} + 1 \right)^{1 - \alpha} - 1 \right) \]</span></p> +<p><span class="math display">\[c_\textrm{twa} = c_0 \frac{\beta}{t (1 - +\alpha)} + \left( \left(\frac{t}{\beta} + 1 \right)^{1 - \alpha} +- 1 \right) \]</span></p> <p>DFOP:</p> <p><span class="math display">\[c_\textrm{twa} = \frac{c_0}{t} \left( \frac{g}{k_1} \left( 1 - e^{- k_1 t} \right) + @@ -271,15 +291,25 @@ code > span.er { color: #a61717; background-color: #e3d2d2; } <p>HS for <span class="math inline">\(t > t_b\)</span>:</p> <p><span class="math display">\[c_\textrm{twa} = \frac{c_0}{t} \left( \frac{1}{k_1} \left( 1 - e^{- k_1 t_b} \right) + - \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)} \right) \right) \]</span></p> -<p>Often, the ratio between the time weighted average concentration <span class="math inline">\(c_\textrm{twa}\)</span> and the initial concentration <span class="math inline">\(c_0\)</span></p> -<p><span class="math display">\[f_\textrm{twa} = \frac{c_\textrm{twa}}{c_0}\]</span></p> -<p>is needed. This can be calculated from the fitted initial concentration <span class="math inline">\(c_0\)</span> and the time weighted average concentration <span class="math inline">\(c_\textrm{twa}\)</span>, or directly from the model parameters using the following formulas:</p> + \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)} +\right) \right) \]</span></p> +<p>Often, the ratio between the time weighted average concentration +<span class="math inline">\(c_\textrm{twa}\)</span> and the initial +concentration <span class="math inline">\(c_0\)</span></p> +<p><span class="math display">\[f_\textrm{twa} = +\frac{c_\textrm{twa}}{c_0}\]</span></p> +<p>is needed. This can be calculated from the fitted initial +concentration <span class="math inline">\(c_0\)</span> and the time +weighted average concentration <span class="math inline">\(c_\textrm{twa}\)</span>, or directly from the +model parameters using the following formulas:</p> <p>SFO:</p> -<p><span class="math display">\[f_\textrm{twa} = \frac{\left( 1 - e^{- k t} \right)}{k t} \]</span></p> +<p><span class="math display">\[f_\textrm{twa} = \frac{\left( 1 - e^{- k +t} \right)}{k t} \]</span></p> <p>FOMC:</p> -<p><span class="math display">\[f_\textrm{twa} = \frac{\beta}{t (1 - \alpha)} - \left( \left(\frac{t}{\beta} + 1 \right)^{1 - \alpha} - 1 \right) \]</span></p> +<p><span class="math display">\[f_\textrm{twa} = \frac{\beta}{t (1 - +\alpha)} + \left( \left(\frac{t}{\beta} + 1 \right)^{1 - \alpha} +- 1 \right) \]</span></p> <p>DFOP:</p> <p><span class="math display">\[f_\textrm{twa} = \frac{1}{t} \left( \frac{g}{k_1} \left( 1 - e^{- k_1 t} \right) + @@ -287,11 +317,19 @@ code > span.er { color: #a61717; background-color: #e3d2d2; } <p>HS for <span class="math inline">\(t > t_b\)</span>:</p> <p><span class="math display">\[f_\textrm{twa} = \frac{1}{t} \left( \frac{1}{k_1} \left( 1 - e^{- k_1 t_b} \right) + - \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)} \right) \right) \]</span></p> -<p>Note that a method for calculating maximum moving window time weighted average concentrations for a model fitted by ‘mkinfit’ or from parent decline model parameters is included in the <code>max_twa_parent()</code> function. If the same is needed for metabolites, the function <code>pfm::max_twa()</code> from the ‘pfm’ package can be used.</p> -<div id="refs" class="references hanging-indent"> -<div id="ref-FOCUSkinetics2014"> -<p>FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> + \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)} +\right) \right) \]</span></p> +<p>Note that a method for calculating maximum moving window time +weighted average concentrations for a model fitted by ‘mkinfit’ or from +parent decline model parameters is included in the +<code>max_twa_parent()</code> function. If the same is needed for +metabolites, the function <code>pfm::max_twa()</code> from the ‘pfm’ +package can be used.</p> +<div id="refs" class="references csl-bib-body hanging-indent"> +<div id="ref-FOCUSkinetics2014" class="csl-entry"> +FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for +Estimating Persistence and Degradation Kinetics from Environmental Fate +Studies on Pesticides in EU Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>. </div> </div> diff --git a/vignettes/web_only/FOCUS_Z.R b/vignettes/web_only/FOCUS_Z.R new file mode 100644 index 00000000..0c19794e --- /dev/null +++ b/vignettes/web_only/FOCUS_Z.R @@ -0,0 +1,115 @@ +## ---- include = FALSE--------------------------------------------------------- +require(knitr) +options(digits = 5) +opts_chunk$set(engine='R', tidy = FALSE) + +## ---- echo = TRUE, fig = TRUE, fig.width = 8, fig.height = 7------------------ +library(mkin, quietly = TRUE) +LOD = 0.5 +FOCUS_2006_Z = data.frame( + t = c(0, 0.04, 0.125, 0.29, 0.54, 1, 2, 3, 4, 7, 10, 14, 21, + 42, 61, 96, 124), + Z0 = c(100, 81.7, 70.4, 51.1, 41.2, 6.6, 4.6, 3.9, 4.6, 4.3, 6.8, + 2.9, 3.5, 5.3, 4.4, 1.2, 0.7), + Z1 = c(0, 18.3, 29.6, 46.3, 55.1, 65.7, 39.1, 36, 15.3, 5.6, 1.1, + 1.6, 0.6, 0.5 * LOD, NA, NA, NA), + Z2 = c(0, NA, 0.5 * LOD, 2.6, 3.8, 15.3, 37.2, 31.7, 35.6, 14.5, + 0.8, 2.1, 1.9, 0.5 * LOD, NA, NA, NA), + Z3 = c(0, NA, NA, NA, NA, 0.5 * LOD, 9.2, 13.1, 22.3, 28.4, 32.5, + 25.2, 17.2, 4.8, 4.5, 2.8, 4.4)) + +FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z) + +## ----FOCUS_2006_Z_fits_1, echo=TRUE, fig.height=6----------------------------- +Z.2a <- mkinmod(Z0 = mkinsub("SFO", "Z1"), + Z1 = mkinsub("SFO")) +m.Z.2a <- mkinfit(Z.2a, FOCUS_2006_Z_mkin, quiet = TRUE) +plot_sep(m.Z.2a) +summary(m.Z.2a, data = FALSE)$bpar + +## ----FOCUS_2006_Z_fits_2, echo=TRUE, fig.height=6----------------------------- +Z.2a.ff <- mkinmod(Z0 = mkinsub("SFO", "Z1"), + Z1 = mkinsub("SFO"), + use_of_ff = "max") + +m.Z.2a.ff <- mkinfit(Z.2a.ff, FOCUS_2006_Z_mkin, quiet = TRUE) +plot_sep(m.Z.2a.ff) +summary(m.Z.2a.ff, data = FALSE)$bpar + +## ----FOCUS_2006_Z_fits_3, echo=TRUE, fig.height=6----------------------------- +Z.3 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE), + Z1 = mkinsub("SFO"), use_of_ff = "max") +m.Z.3 <- mkinfit(Z.3, FOCUS_2006_Z_mkin, quiet = TRUE) +plot_sep(m.Z.3) +summary(m.Z.3, data = FALSE)$bpar + +## ----FOCUS_2006_Z_fits_5, echo=TRUE, fig.height=7----------------------------- +Z.5 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE), + Z1 = mkinsub("SFO", "Z2", sink = FALSE), + Z2 = mkinsub("SFO"), use_of_ff = "max") +m.Z.5 <- mkinfit(Z.5, FOCUS_2006_Z_mkin, quiet = TRUE) +plot_sep(m.Z.5) + +## ----FOCUS_2006_Z_fits_6, echo=TRUE, fig.height=8----------------------------- +Z.FOCUS <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE), + Z1 = mkinsub("SFO", "Z2", sink = FALSE), + Z2 = mkinsub("SFO", "Z3"), + Z3 = mkinsub("SFO"), + use_of_ff = "max") +m.Z.FOCUS <- mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, + parms.ini = m.Z.5$bparms.ode, + quiet = TRUE) +plot_sep(m.Z.FOCUS) +summary(m.Z.FOCUS, data = FALSE)$bpar +endpoints(m.Z.FOCUS) + +## ----FOCUS_2006_Z_fits_7, echo=TRUE, fig.height=8----------------------------- +Z.mkin.1 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE), + Z1 = mkinsub("SFO", "Z2", sink = FALSE), + Z2 = mkinsub("SFO", "Z3"), + Z3 = mkinsub("SFORB")) +m.Z.mkin.1 <- mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin, quiet = TRUE) +plot_sep(m.Z.mkin.1) +summary(m.Z.mkin.1, data = FALSE)$cov.unscaled + +## ----FOCUS_2006_Z_fits_9, echo=TRUE, fig.height=8----------------------------- +Z.mkin.3 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE), + Z1 = mkinsub("SFO", "Z2", sink = FALSE), + Z2 = mkinsub("SFO")) +m.Z.mkin.3 <- mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE) +plot_sep(m.Z.mkin.3) + +## ----FOCUS_2006_Z_fits_10, echo=TRUE, fig.height=8---------------------------- +Z.mkin.4 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE), + Z1 = mkinsub("SFO", "Z2", sink = FALSE), + Z2 = mkinsub("SFO", "Z3"), + Z3 = mkinsub("SFO")) +m.Z.mkin.4 <- mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin, + parms.ini = m.Z.mkin.3$bparms.ode, + quiet = TRUE) +plot_sep(m.Z.mkin.4) + +## ----FOCUS_2006_Z_fits_11, echo=TRUE, fig.height=8---------------------------- +Z.mkin.5 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE), + Z1 = mkinsub("SFO", "Z2", sink = FALSE), + Z2 = mkinsub("SFO", "Z3"), + Z3 = mkinsub("SFORB")) +m.Z.mkin.5 <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, + parms.ini = m.Z.mkin.4$bparms.ode[1:4], + quiet = TRUE) +plot_sep(m.Z.mkin.5) + +## ----FOCUS_2006_Z_fits_11a, echo=TRUE----------------------------------------- +m.Z.mkin.5a <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, + parms.ini = c(m.Z.mkin.5$bparms.ode[1:7], + k_Z3_bound_free = 0), + fixed_parms = "k_Z3_bound_free", + quiet = TRUE) +plot_sep(m.Z.mkin.5a) + +## 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) 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) 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* Includes: core.js, widget.js, mouse.js, position.js, draggable.js, droppable.js, resizable.js, selectable.js, sortable.js, accordion.js, autocomplete.js, button.js, dialog.js, menu.js, progressbar.js, selectmenu.js, slider.js, spinner.js, tabs.js, tooltip.js, effect.js, effect-blind.js, effect-bounce.js, effect-clip.js, effect-drop.js, effect-explode.js, effect-fade.js, effect-fold.js, effect-highlight.js, effect-puff.js, effect-pulsate.js, effect-scale.js, effect-shake.js, effect-size.js, effect-slide.js, effect-transfer.js @@ -309,7 +315,7 @@ float: none; self._setEventHandlers(); // Binding to the Window load event to make sure the correct scrollTop is calculated - $(window).load(function() { + $(window).on("load", function() { // Sets the active TOC item self._setActiveElement(true); @@ -1297,11 +1303,12 @@ window.initializeCodeFolding = function(show) { // select all R code blocks var rCodeBlocks = $('pre.r, pre.python, pre.bash, pre.sql, pre.cpp, pre.stan, pre.julia, pre.foldable'); rCodeBlocks.each(function() { + // skip if the block has fold-none class + if ($(this).hasClass('fold-none')) return; // create a collapsable div to wrap the code in var div = $('<div class="collapse r-code-collapse"></div>'); var showThis = (show || $(this).hasClass('fold-show')) && !$(this).hasClass('fold-hide'); - if (showThis) div.addClass('in'); var id = 'rcode-643E0F36' + currentIndex++; div.attr('id', id); $(this).before(div); @@ -1309,11 +1316,13 @@ window.initializeCodeFolding = function(show) { // add a show code button right above var showCodeText = $('<span>' + (showThis ? 'Hide' : 'Code') + '</span>'); - var showCodeButton = $('<button type="button" class="btn btn-default btn-xs code-folding-btn pull-right"></button>'); + var showCodeButton = $('<button type="button" class="btn btn-default btn-xs btn-secondary btn-sm code-folding-btn pull-right float-right"></button>'); showCodeButton.append(showCodeText); showCodeButton .attr('data-toggle', 'collapse') + .attr('data-bs-toggle', 'collapse') // BS5 .attr('data-target', '#' + id) + .attr('data-bs-target', '#' + id) // BS5 .attr('aria-expanded', showThis) .attr('aria-controls', id); @@ -1325,13 +1334,27 @@ window.initializeCodeFolding = function(show) { div.before(buttonRow); + // show the div if necessary + if (showThis) div.collapse('show'); + // update state of button on show/hide - div.on('hidden.bs.collapse', function () { + // * Change text + // * add a class for intermediate states styling + div.on('hide.bs.collapse', function () { showCodeText.text('Code'); + showCodeButton.addClass('btn-collapsing'); + }); + div.on('hidden.bs.collapse', function () { + showCodeButton.removeClass('btn-collapsing'); }); div.on('show.bs.collapse', function () { showCodeText.text('Hide'); + showCodeButton.addClass('btn-expanding'); + }); + div.on('shown.bs.collapse', function () { + showCodeButton.removeClass('btn-expanding'); }); + }); } @@ -1366,11 +1389,6 @@ color: #d14; </style> <style type="text/css">code{white-space: pre;}</style> -<style type="text/css"> - pre:not([class]) { - background-color: white; - } -</style> <script type="text/javascript"> if (window.hljs) { hljs.configure({languages: []}); @@ -1383,47 +1401,40 @@ if (window.hljs) { + + <style type="text/css"> -h1 { - font-size: 34px; -} -h1.title { - font-size: 38px; -} -h2 { - font-size: 30px; +/* for pandoc --citeproc since 2.11 */ +div.csl-bib-body { } +div.csl-entry { + clear: both; } -h3 { - font-size: 24px; +.hanging div.csl-entry { + margin-left:2em; + text-indent:-2em; } -h4 { - font-size: 18px; +div.csl-left-margin { + min-width:2em; + float:left; } -h5 { - font-size: 16px; +div.csl-right-inline { + margin-left:2em; + padding-left:1em; } -h6 { - font-size: 12px; -} -.table th:not([align]) { - text-align: left; +div.csl-indent { + margin-left: 2em; } </style> - <style type="text/css"> .main-container { max-width: 940px; margin-left: auto; margin-right: auto; } -code { - color: inherit; - background-color: rgba(0, 0, 0, 0.04); -} img { max-width:100%; } @@ -1439,6 +1450,12 @@ button.code-folding-btn:focus { summary { display: list-item; } +details > summary > p:only-child { + display: inline; +} +pre code { + padding: 0; +} </style> @@ -1451,13 +1468,12 @@ summary { max-height: 500px; min-height: 44px; overflow-y: auto; - background: white; border: 1px solid #ddd; border-radius: 4px; } -.tabset-dropdown > .nav-tabs > li.active:before { - content: ""; +.tabset-dropdown > .nav-tabs > li.active:before, .tabset-dropdown > .nav-tabs.nav-tabs-open:before { + content: "\e259"; font-family: 'Glyphicons Halflings'; display: inline-block; padding: 10px; @@ -1465,16 +1481,9 @@ summary { } .tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before { - content: ""; - border: none; -} - -.tabset-dropdown > .nav-tabs.nav-tabs-open:before { - content: ""; + content: "\e258"; font-family: 'Glyphicons Halflings'; - display: inline-block; - padding: 10px; - border-right: 1px solid #ddd; + border: none; } .tabset-dropdown > .nav-tabs > li.active { @@ -1580,7 +1589,7 @@ div.tocify { <!-- setup 3col/9col grid for toc_float and main content --> -<div class="row-fluid"> +<div class="row"> <div class="col-xs-12 col-sm-4 col-md-3"> <div id="TOC" class="tocify"> </div> @@ -1591,11 +1600,11 @@ div.tocify { -<div class="fluid-row" id="header"> +<div id="header"> -<div class="btn-group pull-right"> -<button type="button" class="btn btn-default btn-xs dropdown-toggle" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"><span>Code</span> <span class="caret"></span></button> -<ul class="dropdown-menu" style="min-width: 50px;"> +<div class="btn-group pull-right float-right"> +<button type="button" class="btn btn-default btn-xs btn-secondary btn-sm dropdown-toggle" data-toggle="dropdown" data-bs-toggle="dropdown" aria-haspopup="true" aria-expanded="false"><span>Code</span> <span class="caret"></span></button> +<ul class="dropdown-menu dropdown-menu-right" style="min-width: 50px;"> <li><a id="rmd-show-all-code" href="#">Show All Code</a></li> <li><a id="rmd-hide-all-code" href="#">Hide All Code</a></li> </ul> @@ -1605,15 +1614,19 @@ div.tocify { <h1 class="title toc-ignore">Example evaluation of FOCUS dataset Z</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 16 January 2018 (rebuilt 2021-02-15)</h4> +<h4 class="date">Last change 16 January 2018 (rebuilt 2023-01-05)</h4> </div> -<p><a href="http://www.jrwb.de">Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany</a><br /> <a href="http://chem.uft.uni-bremen.de/ranke">Privatdozent at the University of Bremen</a></p> +<p><a href="http://www.jrwb.de">Wissenschaftlicher Berater, Kronacher +Str. 12, 79639 Grenzach-Wyhlen, Germany</a><br /> <a href="http://chem.uft.uni-bremen.de/ranke">Privatdozent at the +University of Bremen</a></p> <div id="the-data" class="section level1"> <h1>The data</h1> -<p>The following code defines the example dataset from Appendix 7 to the FOCUS kinetics report <span class="citation">(FOCUS Work Group on Degradation Kinetics 2014, 354)</span>.</p> +<p>The following code defines the example dataset from Appendix 7 to the +FOCUS kinetics report <span class="citation">(FOCUS Work Group on +Degradation Kinetics 2014, 354)</span>.</p> <pre class="r"><code>library(mkin, quietly = TRUE) LOD = 0.5 FOCUS_2006_Z = data.frame( @@ -1632,7 +1645,11 @@ FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)</code></pre> </div> <div id="parent-and-one-metabolite" class="section level1"> <h1>Parent and one metabolite</h1> -<p>The next step is to set up the models used for the kinetic analysis. As the simultaneous fit of parent and the first metabolite is usually straightforward, Step 1 (SFO for parent only) is skipped here. We start with the model 2a, with formation and decline of metabolite Z1 and the pathway from parent directly to sink included (default in mkin).</p> +<p>The next step is to set up the models used for the kinetic analysis. +As the simultaneous fit of parent and the first metabolite is usually +straightforward, Step 1 (SFO for parent only) is skipped here. We start +with the model 2a, with formation and decline of metabolite Z1 and the +pathway from parent directly to sink included (default in mkin).</p> <pre class="r"><code>Z.2a <- mkinmod(Z0 = mkinsub("SFO", "Z1"), Z1 = mkinsub("SFO"))</code></pre> <pre><code>## Temporary DLL for differentials generated and loaded</code></pre> @@ -1640,7 +1657,7 @@ FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)</code></pre> <pre><code>## Warning in mkinfit(Z.2a, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data</code></pre> <pre class="r"><code>plot_sep(m.Z.2a)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>summary(m.Z.2a, data = FALSE)$bpar</code></pre> <pre><code>## Estimate se_notrans t value Pr(>t) Lower Upper ## Z0_0 97.01488 3.301084 29.3888 3.2971e-21 91.66556 102.3642 @@ -1648,8 +1665,14 @@ FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)</code></pre> ## k_Z1 0.48212 0.063265 7.6207 2.8154e-08 0.40341 0.5762 ## f_Z0_to_Z1 1.00000 0.094764 10.5525 5.3560e-11 0.00000 1.0000 ## sigma 4.80411 0.635638 7.5579 3.2592e-08 3.52677 6.0815</code></pre> -<p>As obvious from the parameter summary (the component of the summary), the kinetic rate constant from parent compound Z to sink is very small and the t-test for this parameter suggests that it is not significantly different from zero. This suggests, in agreement with the analysis in the FOCUS kinetics report, to simplify the model by removing the pathway to sink.</p> -<p>A similar result can be obtained when formation fractions are used in the model formulation:</p> +<p>As obvious from the parameter summary (the component of the summary), +the kinetic rate constant from parent compound Z to sink is very small +and the t-test for this parameter suggests that it is not significantly +different from zero. This suggests, in agreement with the analysis in +the FOCUS kinetics report, to simplify the model by removing the pathway +to sink.</p> +<p>A similar result can be obtained when formation fractions are used in +the model formulation:</p> <pre class="r"><code>Z.2a.ff <- mkinmod(Z0 = mkinsub("SFO", "Z1"), Z1 = mkinsub("SFO"), use_of_ff = "max")</code></pre> @@ -1658,7 +1681,7 @@ FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)</code></pre> <pre><code>## Warning in mkinfit(Z.2a.ff, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data</code></pre> <pre class="r"><code>plot_sep(m.Z.2a.ff)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>summary(m.Z.2a.ff, data = FALSE)$bpar</code></pre> <pre><code>## Estimate se_notrans t value Pr(>t) Lower Upper ## Z0_0 97.01488 3.301084 29.3888 3.2971e-21 91.66556 102.3642 @@ -1666,9 +1689,17 @@ FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)</code></pre> ## k_Z1 0.48212 0.063265 7.6207 2.8154e-08 0.40341 0.5762 ## f_Z0_to_Z1 1.00000 0.094764 10.5525 5.3560e-11 0.00000 1.0000 ## sigma 4.80411 0.635638 7.5579 3.2592e-08 3.52677 6.0815</code></pre> -<p>Here, the ilr transformed formation fraction fitted in the model takes a very large value, and the backtransformed formation fraction from parent Z to Z1 is practically unity. Here, the covariance matrix used for the calculation of confidence intervals is not returned as the model is overparameterised.</p> -<p>A simplified model is obtained by removing the pathway to the sink. </p> -<p>In the following, we use the parameterisation with formation fractions in order to be able to compare with the results in the FOCUS guidance, and as it makes it easier to use parameters obtained in a previous fit when adding a further metabolite.</p> +<p>Here, the ilr transformed formation fraction fitted in the model +takes a very large value, and the backtransformed formation fraction +from parent Z to Z1 is practically unity. Here, the covariance matrix +used for the calculation of confidence intervals is not returned as the +model is overparameterised.</p> +<p>A simplified model is obtained by removing the pathway to the sink. +</p> +<p>In the following, we use the parameterisation with formation +fractions in order to be able to compare with the results in the FOCUS +guidance, and as it makes it easier to use parameters obtained in a +previous fit when adding a further metabolite.</p> <pre class="r"><code>Z.3 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE), Z1 = mkinsub("SFO"), use_of_ff = "max")</code></pre> <pre><code>## Temporary DLL for differentials generated and loaded</code></pre> @@ -1676,18 +1707,24 @@ FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)</code></pre> <pre><code>## Warning in mkinfit(Z.3, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data</code></pre> <pre class="r"><code>plot_sep(m.Z.3)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>summary(m.Z.3, data = FALSE)$bpar</code></pre> <pre><code>## Estimate se_notrans t value Pr(>t) Lower Upper ## Z0_0 97.01488 2.597342 37.352 2.0106e-24 91.67597 102.3538 ## k_Z0 2.23601 0.146904 15.221 9.1477e-15 1.95354 2.5593 ## k_Z1 0.48212 0.041727 11.554 4.8268e-12 0.40355 0.5760 ## sigma 4.80411 0.620208 7.746 1.6110e-08 3.52925 6.0790</code></pre> -<p>As there is only one transformation product for Z0 and no pathway to sink, the formation fraction is internally fixed to unity.</p> +<p>As there is only one transformation product for Z0 and no pathway to +sink, the formation fraction is internally fixed to unity.</p> </div> <div id="metabolites-z2-and-z3" class="section level1"> <h1>Metabolites Z2 and Z3</h1> -<p>As suggested in the FOCUS report, the pathway to sink was removed for metabolite Z1 as well in the next step. While this step appears questionable on the basis of the above results, it is followed here for the purpose of comparison. Also, in the FOCUS report, it is assumed that there is additional empirical evidence that Z1 quickly and exclusively hydrolyses to Z2.</p> +<p>As suggested in the FOCUS report, the pathway to sink was removed for +metabolite Z1 as well in the next step. While this step appears +questionable on the basis of the above results, it is followed here for +the purpose of comparison. Also, in the FOCUS report, it is assumed that +there is additional empirical evidence that Z1 quickly and exclusively +hydrolyses to Z2.</p> <pre class="r"><code>Z.5 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE), Z1 = mkinsub("SFO", "Z2", sink = FALSE), Z2 = mkinsub("SFO"), use_of_ff = "max")</code></pre> @@ -1696,8 +1733,10 @@ FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)</code></pre> <pre><code>## Warning in mkinfit(Z.5, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data</code></pre> <pre class="r"><code>plot_sep(m.Z.5)</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> -<p>Finally, metabolite Z3 is added to the model. We use the optimised differential equation parameter values from the previous fit in order to accelerate the optimization.</p> +<p><img 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" /><!-- --></p> +<p>Finally, metabolite Z3 is added to the model. We use the optimised +differential equation parameter values from the previous fit in order to +accelerate the optimization.</p> <pre class="r"><code>Z.FOCUS <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE), Z1 = mkinsub("SFO", "Z2", sink = FALSE), Z2 = mkinsub("SFO", "Z3"), @@ -1712,7 +1751,7 @@ FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)</code></pre> <pre><code>## Warning in mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin, parms.ini = m.Z.5$bparms.ode, : Optimisation did not converge: ## false convergence (8)</code></pre> <pre class="r"><code>plot_sep(m.Z.FOCUS)</code></pre> -<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAMACAIAAAB0NULIAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdd0BTV98H8JtA2FsQkFEFFRBBEFFRROsAq4gLJ4rWqlQKVrFa96qrtVWfWpxoUUsVZDgYbgT1EVSWOHCDQECQkTADGe8feV6kASmEJDcJ389fN4eTc36BXH53nHsOhcfjEQAAACBfqGQHAAAAAKKHBA8AACCHkOABAADkEBI8AACAHEKCBwAAkENI8AAAAHIICR4AAEAOIcEDAADIISR4AAAAOYQEDwAAIIeQ4AEAAOQQEjwAAIAcQoIHAACQQ0jwAAAAcggJHgAAQA4hwQMAAMghJHgAAAA5hAQPAAAgh5DgAQAA5BASPAAAgBxCggcAAJBDSPAAAAByCAkeAABADiHBAwAAyCEkeAAAADmEBA8AACCHkOABAADkEBI8AACAHEKCBwAAkENI8AAAAHIICV66vH37dsqUKQsWLDh69CjZsQAAgAyj8Hg8smOAT9asWTNnzhxHR0c3N7fk5GSywwEAAFmFM3jpsm3bNgcHh4KCAn19fbF2lJSUNHXqVEm+kcFgtL8ym83++PFjqz+qqKjIzc0VIgAAgC4FCV66qKqqRkZGBgQEHDp0iOxYRInJZLq5ufG3f/rpJ9r/U1RUdHJyall/w4YNK1euJAgiLy/PxcXF2tr6999/5/9o48aNLBZLYpEDAMgoJHjpEhcXl5KSEhMTY2Rk1M63NDY2trrddk1J2rVr15AhQ5hMJv/lpk2bGv9fUFDQ999/L1A/KSnp5MmT/O3du3dv3br16dOnp06dYjKZL1++JAjCyspKkvEDAMgiJHgyrVq1qm/fvg8ePCAIIjIy0sbGJioq6sWLFzNnzpw/f75A5YMHD1pYWPTp02fVqlVcLvfevXs+Pj5z5szZvn17822CIHbu3GlpaWlhYbFmzRqBmi1jEGh2/PjxFy5c4P/I2dk5OTlZoIIQH9PDw2PXrl0tyx89epSTk+Pr69u8sLKycvXq1Zs3b+a/1NbWfvbsWX5+fn19PY1G27lz54YNG4SIAQCgy+EBqaKiojw8PGpra7/44ouEhITPVbt9+/aAAQNKSkqqq6tnz569Y8eOu3fvamtr5+Tk8Hi85ttXrlyxsrJiMBj19fWurq6nT59u/tPmDU6ZMqVls3/++ef8+fN5PN6bN28sLCwSExMFKvDf2Lypr7766osWHj9+3LxOQUFBz549BT7UiBEjnj59KlA4Z86cS5cunTt3bt68eTwej8Fg+Pn5jR079tq1aw8fPly3bl1Hf8MAAF2TItkHGF3d1KlT165du2zZMnt7+/Hjx3+u2s2bNxkMxsyZMwmCKC0traqqGjVqlIODQ9PF6qbtW7dueXt7a2lpEQQxZ86cW7duWVhYNK/ZdrNhYWFr165tbGwMDw9fsGDBrVu3BCq4uroKNBIfHy/EB09ISOjevXu/fv2aF4aFhWloaEyaNCk8PJxfoqWldeTIEf72jBkzjh8/fvny5eDgYDs7ux07digrKwvRNQBAV4AETzIKhbJ48eJNmzY9ffq0jWoaGhp+fn5r164lCKK+vr6hoSE7O1tTU7OpQtM2j8ejUCj8bQUFBQ6H0/yn/9qslpbW0KFDExMTIyIioqOjz58/L1AhIyNDoBEPD4+WwcfHx9vb27fxif7666/p06cLFEZGRqampvbq1aumpqa2tnbixIlxcXH8H127dm3w4MEcDmfr1q0xMTEhISHBwcFBQUFtdAEA0JXhHjz5MjIyNDU1zczM2qjj7u5+6tSp0tJSFos1ffr0e/fufa7m6NGjo6KiqqurGxoawsLCRo8e3dFmZ8+evXv3bl1d3V69erWn36tXrxa00HZ2Z7FYV65cabpiwePxXrx4wWazY2Ji6HT6u3fvDh48OHXq1KbszuPxgoODAwMDa2pqdHV1zczM7OzsysrK2ugCAKCLQ4In2b1795KSkoYNGxYZGdlGNQcHhxUrVri4uFhaWlpaWn711Vefqzl+/Pi5c+c6Ojra2NgMHjxYYAhbe5qdNGnSgwcPFixY0KF+O+TatWsDBgzQ1dXlv2xoaLC2ti4sLPxc/YiICC8vLxUVFXNzc1dXV1tb24MHDy5fvlwkwQAAyCXMZEcmLpc7ePDggICAnj17/vDDDykpKYqKuGkCAAAigHRCptDQUAqFsmDBAgqFYmNj06tXr+Tk5F69epEdFwAAyDycwQMAAMgh3IMHAACQQ0jwAAAAckiM9+C5XG5WVlZxcTGbzTYxMXFwcKBScTwBAAAgCeJK8ImJicuWLTM2NjYxMSEIgk6n5+fnnzhxomlJMQAAABAfcQ2ys7Ozu3DhgqWlZVNJfn6+t7d3amqqOLoDAACA5sR1zZzD4RgbGzcvMTQ0xIh9AAAAyRDXJfqAgAAnJ6fJkyebmppSKBQ6nX7p0qWAgID2t3Dv3r3Lly+LKTwAmWBubu7v7092FJKA/R1A5Pu7GJ+Dz8/PT0hIoNPpBEEYGxtPmDCh7enWBaxZs+bp06e4Zw9dFoPBOHPmTH5+PtmBSAL2d+jixLG/izHBd3IU/Zo1awwMDFavXi1QXlxc/PPPPz979qx///4//vhj9+7dRRo1gLQoKChwcXGRywQ/ffr0zMzM5iVFRUXOzs5JSUlkhQRALnHs7zI2ir6urm7MmDGTJk1atWrV1atXx40b9+DBAywKDiBbQkJCKioqmpdMmzZNQ0ODrHgA5JK4Evzy5cvj4uLaP4o+ISFB4Mjl/v37Dg4OAtXu3btnaGi4Z88egiDc3d2HDx+empqKy3oAskVXV7dpLUE+ZWVlCoVCVjwAcklcCb6jo+gfP3789u3b5iUvX75sWa22tlZLS6vppba2dm1tbaeDBQAAkDfSMor+xx9/FCjJzMzU1tYWKBw+fPiyZcsiIyPd3d3j4+MfP37s4uIi+uhBvjAYDGtraxaLRXYgn2hpab19+xZzOwKA+Igrwfv7+0+aNKlpFL25uXl8fHyHRtG3qlu3btHR0atWrfrmm28GDBhw8eLFlgcBAAJqa2t5PN6bN2/IDuST7t27c7lcJHgAEB8xzkVvZma2dOlSkTc7ZMiQO3fu6OrqJicni7xxkFdUKlXgpi8AgHyTyRMICoVSXV3NZrPJDgQAAEBKiesM/siRI60+0nr27FmRtK+srNzQ0KCoKMYrEAAgJXg8XllZmb6+PtmBAMgScSXI+fPnp6amMplMMU20qayszGKx1NTUxNE4AEiPv//+e8WKFVwuV11d/eTJk2PGjCE7IgDZIK4Er66uvnLlynPnzolpb1RSUmpoaBBHywAgPV69erV69eqkpCQbG5vk5ORZs2a9ePGi+bOyAPA5YrzEbW9vb29vL6bG+WfwYmocAKTE3bt3PTw8bGxsCIJwc3OzsrLKzMzE3FYA7SGTg+wInMEDdA3du3fPzc3lb7PZ7IKCAgMDA1IjApAZsprgcQYPUuX06dO9/h9/cqf3798fOnSoX79+9vb2iYmJZAcodlwuNyMjIyEh4fLly+np6VwuVyTNjh07lslkzps37/Dhw15eXv3797e2thZJywByT1ZHoeMMHkQrKirq5s2b3bp1+/bbb/krJHWIr6+vr68vfzsoKOj9+/cUCuXgwYNpaWl0Ot3T0/PZs2dyPK2NmBaXIghCWVk5KSnp2LFj2dnZ06ZNW7hwIaasB2gnWU3wOIMHEdq0aVN8fPzixYtzc3MHDx784MEDIXI8X2xs7IULF9LT0yMiIiZPnqympta7d28jI6OsrCxHR0fRhi09Orq4VIfwR+x2vh2ArkZWEzzO4EEIbDY7KSlJYNEjLpe7b9++M2fOaGlp9enT5+3btxs3bvTx8RF4r7m5ed++fdtuv7CwcMmSJTExMTo6OkVFRaampvxyU1PT4uJiEX4QadPRxaUAQAJkNcHjDB6EUFRU9PPPPwskHjab3dDQcPjwYf7L/Pz82tragoICgfe6ublt2rSpjcY5HM7cuXODgoKGDh1KEASXy21+MVm+J17s6OJSACABsprglZSUkOCho8zMzK5du9ayfPDgwVOmTPnuu+8+fPjg4eGxd+/eSZMmdbTxbdu2qaqq/vDDD/yXPXr0eP/+PX+bTqf36NGjM5FLuY4uLhUQEPDixYvmJTk5Obi5DiBaspfgKyoqfv7550ePHh06dMjBwaHpKiiA0MLCwubPn79u3ToKhbJmzRohsntiYuLJkyfT09ObstRXX301ceLELVu2lJSU5Ofny/ENeL4OLS61bNmyoqKi5iWBgYFYGRJAtGQswXM4nAkTJgwcONDGxkZVVXXkyJEZGRmY1go6qU+fPikpKVVVVWpqagoKCkK0sHfv3urq6uHDhzeV3Lx587vvvnN1dSUIIiQkRI6H0AvB1tbW1ta2eYmWlhaNRiMrHgC5JGMJPjs7u6amJjg4eOHChV9++SWbzb558+bUqVPJjgvkgaamptDvjY+Pb1n47bfffvvtt52ISGaIe3EpABCCjCX42tpa/vk6f5CdlpZWTU0N2UEBdHXiXlwKAIQgLQl+z5497969a16Sm5vbcjVYR0fHwsLCP//8kyCIjIyMGzdu/Pzzz5KLEgBaI+7FpQBACNKS4B0dHfX09JqXXL9+XVVVVaCaqqrq5cuXv//++3v37hkaGoaHh8v34GQAWSHWxaUAQAjSkuA9PDwESk6cOKGiotKyZv/+/W/evLl+/XpNTU0sKgUAANAqWR3Zi+fgAQAA2iAtZ/AdpaysXFVVRXYUACCN3r59Gx0draio6O3tjakyoMvCGTwAyJU7d+64uLgUFBS8evXKyckpKyuL7IgAyCHDZ/BYbAbaj8Fg+Pn5kR3FJ6JaLh1a2rp165EjR/jTYzg6Ou7cuTMiIoLsoABIIKsJnkaj4Qwe2snQ0PDQoUNS9YUZNWpUy6dAQSTy8/Pt7Oz423Z2diEhIeTGA0AW2fsX8+7du6VLl96+fZtGozk5OUnVaRlIJyqVumDBArKjAAkZOnToqVOnfvrpJy6Xe+bMGf7ifgBdkOzdg589e7aHh0doaOjo0aN//fXX27dvkx0RAEiRvXv3Xrt2zdLSslevXk+fPt22bRvZEbWFxWJxOByyowD5JGMJvqys7O3btz/88IOqqqqysvLixYuvXLlCdlAAIEUMDQ3v379/5cqVxMTExMREqV2k7uPHj56ennp6etra2t9//z2GZYDIyViCV1dXZ7FYNTU1/LnoS0pKdHR0yA4KoKtrfg6akZERFhYmsNy7hFGp1D59+lhYWJAYw78KDAy0trZmMBgFBQXPnj0LDg4mOyKQNzJ2D15FRWXevHmTJk0aNmzYixcv0tPTU1NTyQ4KQPYsW7as1fLDhw8L0ZqRkVFpaSlBEPv37//1119Hjx69YcOG7du3+/r6dirKz+DxeAkJCc+fP+/fv3/LSTBlxY0bN3JychQVFXV0dJYvXx4SEhIYGEh2UCBXWknwmZmZmZmZPj4+xcXFZmZmko+pbb///vuJEyfCw8Pr6uru3r0rhRECSD9PT09xNPvbb789evTI2Ni4tLR0+PDhn0vw7Vxc6nPmzJmTm5vr6ur6999/nzt37s8//3z69OmdO3cMDQ0nTZokK48ndOvWraCgoFu3bgRBNG2IXExMzNGjR1ks1rRp0/z9/RUUFMTRC0gnwT0hNDR09+7ddXV13t7eI0aM2Lhx4+LFi0mJ7HMUFRX9/PwGDBiwcuVKKb8EByC1Jk6cKFDC5XI3btzYsrxD9PX1jY2N+Rtt5BIHB4f2LC7VqtTU1CdPnmRmZioqKrJYrH79+m3dujUkJGTSpEkvX77cvXt3UlJSO5si16pVq2bMmBEUFFRRUXHgwIGEhASRd3Hp0qXVq1fv27dPQ0Nj8+bNTCZzw4YNIu8FpJZggt+/f39KSoqvr6+GhkZmZuagQYOkLcHz8e/Bkx0FgGz75ZdfDhw4UFFRYWhoWFJS8uWXXwrXDo1Gs7W1NTc3ZzKZp06d8vX1Xbx4saur6+fqjx8/XqDkc4tLtZSQkEChUI4fPz5v3jxNTc1+/fr9+uuvT5486dmzJ0EQM2fOPH36tEw8PbtkyRJzc/Po6Gg1NbXExMR+/fqJvIvTp0/v3r3by8uLIIgePXp4eXkhwXcpggm+pqamaTdTU1Oj0WhCN83lcrOysoqLi9lstomJiYODA5UqsjF9mKoWoPP++OOP58+fr1+/3t/fn8lk/vnnn8K1Q6fT6+rq8vLycnNzjYyMeDyehYVFUFCQaKOtqan5/vvv4+Liampqbty4sX///ujo6NTUVA0NDX52JwhiyJAhr1+/FrqLsrKyM2fOMBiM8ePHDxkyRDRxf56Hh4dYxxA0NDQ0/T9XUVFpbGwUX18ghQQzro+Pj5eX17t3706ePDl27FgfHx/h2uUfkAYFBYWFhYWHh//www9WVlbJycmdDvh/MFUtQOcpKytraWk5Ojqmpqa6uLh0Ztp2VVVVa2vr8ePH8w/lN2zYIKrr5O/evUtISEhOTra1tT116pSlpaWSklJiYiKDwRg6dOiOHTsUFBRSUlIIgmhsbLx8+fLAgQOF64hOpzs4ODx79ozL5c6aNevEiRMiiZ9EM2bM2Lx5c1pa2qtXr5YvXz5jxgyyIwKJEjyD37ZtW1xc3N27dwsLC3ft2tXGRba2LV++PC4uztLSsqkkPz/f29tbVIPecQYP0Hlubm6LFi1at27djBkzPnz4IIWPjP/000+HDx92cHBITEwcOXJkfX393bt39+7d+/DhQ2NjYzabvXTp0p49e06ZMsXKyio3N3fEiBGzZs1qtSkmk8mfAXPUqFGtHnwEBwfPmzdv9+7dBEHMmTNnzJgx33zzjXg/npjNnz+fwWAsWLCgvr5+xowZW7duJTsikCjBBF9WVjZ06NCmyR3LysqEG9vJ4XD4Y22aGBoa8ng84aJsCWfwAJ0XEhLy8uXLvn377tu3Ly0tTehL9GKSk5Nz/Pjxp0+f6urqWllZZWRkqKioxMbGjhs37q+//srLy/vxxx8JgnB3d3/58mVmZqaRkVHfvn1bberZs2fu7u4DBgxgsVgBAQFJSUktl5EtKCgYO3Ysf7tPnz7l5eWNjY2duU0pDQICAgICAsiOAsghmOCbBr8wGIxXr14NGDAgMzNTiHYDAgKcnJwmT55sampKoVDodPqlS5dE+D3DIDuAzisvL9fX1y8rK3NwcHBwcCA7HEGPHz92cXHR1dUlCKJ///5v376dO3duYGBgbW1tZWWlv7//tGnT+DW1tLTc3NzaaGrNmjXbt29ftGgRQRA7d+7kD7wXqDN8+PC//vrL29tbVVU1NDR04MCBsp7doYsTTPAPHz5s2s7OzhZ6ngp/f/9JkyYlJCTQ6XSCIMzNzePj49t4Zt3b2zsjI6N5SWFhYf/+/T9XX0lJCWfwAJ0kqgN6Menbt29aWlp9fb2KisrmzZudnJxiY2OtrKyys7NTU1M7dETy9OnTP/74g789ZsyYlStXtqyzePHilJQUExOTbt260Wi0qKgo0XwMAJK0NSOEnZ1dZ0ZdamlpLVmyhEKhpKWlPX/+vLq6uo3Khw8frqqqal4yderU7t27f64+zuABOk9UB/Ri4uDg4OHh4eDgMGzYsLt373777bfjxo2jUqmjR49WV1fvUFO2trY3btzgP/R748aNVk8eqFTqyZMnf/nll8rKSgsLCxE+9QNACsEE3/zANicnx8rKSrh2jx07tm/fvuzs7L1794aEhIwYMWLDhg0bN25csmRJq/UNDAwMDAyal6ioqLSxg/Hn0OBwOJiYCUAkOnlALybBwcEpKSnPnz9ftmyZs7Oz0O3s3bvX3d09KiqKxWLl5+cnJSV9rqa+vr6+vr7QHQFID8EE33zY/MSJE4We+GLnzp3Z2dk0Gu3w4cNZWVl6enqlpaXDhg37XIIXAv8qvUxMWQUgnUR1QC9WzYf9Cs3GxubZs2dJSUlKSkpubm7tnFQHSFRaWlpaWtqnTx+MhBDapwTPf4BNYGTpo0ePhJvtwcDAgH+PXEdHh38iLvJTbf5VeiR4AKGJ6oBeJmhqaoppBn4QLR6PFxAQEB4e3r1797q6uoiIiM5cvOnKPiX4AwcOEARBp9Pv3Lljb2+voaGRmpo6derUiIgIIdrduXPnsGHDvLy8+vbtO2rUKHd397i4ONGulYRxdgBCE+0BPYAIhYeHZ2Vl5eXlqaurX7p0ad68eeSuPszHZrNv3bpVUVHh5uYm8BC41PqU4M+ePUsQxLhx454/f86/Uvfu3bulS5cK166Hh8eDBw8uXryoo6NjbW1tYGAQFRVlbW0tkqD5MM4OQGiiPaAHEKGUlJSZM2fyx1F6eXktWrRI6BlZRKWqqmrkyJFqamomJibLly//66+/xo0bR2I87SR4Dz4vL69ppoiePXvyH3ITjo6OzoIFC4QP7TM4HM6mTZtOnjz58ePH9evXHz9+HLfTADpKtAf0YvXx48f4+HgKhTJhwgRy/8uDZJiamj558oS/XVBQwOFw+HMhkOj3338fNGjQsWPHCIJITk5eunRpTk4OuSG1h+AwdRcXF29v7+jo6KioqBkzZkjhnY8DBw48ePDg4cOHlpaWHz582Lx5M9kRAcgqER7Qc7ncjIyMhISEy5cvp6enc7lckUT45MkTOzu7K1euxMfH9+/f/9mzZyJpFqTZ4sWLb9++PXfu3M2bN48cOXLr1q2kP7L4/PnzkSNH8rddXV1zc3Ol8JGTlgTP4I8dO3bkyJG///6bSqWOHDlSCo/oY2Njt27damZmpqamtmzZsvXr1//yyy9kBwUgk/gH9D4+Pjwe7+zZs0If0CcmJi5btszY2NjExIQgCDqdnp+ff+LEibZnl2uPTZs27dixgz8n/LFjxzZt2oT5Z+Sejo5Oenp6WFhYSUnJX3/95eLiQnZEhJ2d3bVr1/irr926dcvS0lImxvZ/SvCBgYE//fTTpk2bCILgjyB4+fLlDz/8cPDgQdKia426unpFRQVBEMrKyuXl5RoaGmRHBCCrRHVA39HFpf7+++/8/PzmJXl5eR8/fvz555/5L/mLxxAEkZ2d3atXL355UVHR06dP+RXS09OvX7/e9Pam+iiXj/KXL19WVlYqKSklJycnJyeTHs+IESMOHz5samqqqalZUFBw+PBhfgUR9hsZGVlbW0uI1KcEP3bsWBUVlaapK6WWn59fYGBgVVUVk8ncsWPHxo0byY4IQPaI9oC+o4tLVVVV8Q/Tm7DZ7MbGxqbCpgdk+vTpk5aWxj+HS0tLaxqoy2KxmrfQVB/lKBdHOYVCmTVrVmFhIYvF8vDwGDBggMj7ZTAYIlyP7X9hC7SYk5Nz7969b775ZuPGjbdv396zZ4/QK8Z20pAhQwwMDGJjY1v+6Nq1aydOnLh9+7afn9/27dslHxuABBQUFLi4uAic6YrKxYsXPTw8bt68KVA+ceJEIVo7dOjQwYMHWy4u5efn184WPre/FxYWuru7q6qq8ng8Fot1/fp1WXlCCaBDxLG/C45c+P777zU0NB48eHD37t09e/bs3LlThJ2Jiru7e3h4uLOzM57ZBRDO5MmTVVRULC0ti4uLJ06ceP/+/d27dwu9Hry/v/+1a9csLCw+fvxYWlrKX1yq/dm9DSYmJpmZmcHBwYcOHcrMzER2B2g/wUF2xcXFs2bNWrt27bx581xdXd++fUtKWO2BiW4AOun7779ftGhR8wP6hIQE4ZoyMzMT05hcGo2GQ3kAIQiewVtZWa1atSosLMzT03P//v1aWlqkhNUemOgGoJP4B/TR0dHSf0APAB0lmOCDg4NNTEwuXLhgZGRUVlbGnw1DOuEMHqCTZOiAHgA6SjDBGxgYjB49Ojs7u7Gx0c/Pr3fv3qSE1TYmk7l9+/bk5OTw8PDy8nKywwGQVTJ0QA8AHSWY4ENDQ2fNmrV582YWizVixIiQkBBSwmoDm812d3d///69jY1NdXX1qFGj6uvryQ4KQCbJxAE9AAhHMMHv378/JSVlwIABGhoamZmZe/bsISWsNqSlpbHZ7JCQEBsbm8mTJ+vp6d25c4fsoABkkvQf0JeUlCxcuLBXr17Dhg0TegAgQNckmOBramqa1m5RU1OT2Gx8tbW1Ff/EZrNbfeqfwWDo6+sTBKGkpMRisQwMDBgMhmSCBJAz0n9AP3v2bAMDg8TExM2bNy9evPjx48dkRwQgMwQfk/Px8fHy8ioqKjp58mRoaCh/6l0JmDFjxv3795uXVFVVUSiUljUHDx6cnZ19+fJlGo2Wk5Nz9+7dP/74QzJBAsgZsg7o2+njx49Pnjy5efMmhULp2bPnN998c+HCBXt7e7LjApANggl+27ZtcXFxd+/eLSws3LVrl8SmsYuLixMo4c9s1bKmjo7O+fPnAwICsrKyunXrdu7cOUNDQ4nECCBvyDqgbydFRUXO/7t48WJKSoqtrS3ZQQHIDMEETxDExIkTm6arrKqq0tTUlGxI/27YsGHp6em//fZbUVHR6NGjyQ4HQFaRdUDfTjo6Om5ubvPnz3/w4IGGhsbr16+zsrIGDhw4f/58skMDkAGfEnx6evq6devKy8snTJgQGBi4bt269+/fZ2VlFRcXkxhfG/AcPEDnieSAnsPhKCgo8LczMjKePXs2aNAg/jJZnXT69OnJkydXV1c7ODiEhIRoaWmNGDFi3rx5rd6/A4DmPg2y+/rrr11cXPbv30+n0+3t7U1MTNatW5eSkkJicG3DTHYAQktPT/fw8HB2dt6yZcvHjx+XLFni4eHRp08f4VozMjLib+zfv9/T0/PKlSseHh6nT5/ufJyamprW1tbbtm2Liopydna2srJqbGxkMpmdbxlA7n06g6+oqNi6dStBEDY2NhcvXuRvSzOcwQMI7euvv546derYsWNPnTplb2+/dOlSHx+fnj17drLZ33777dGjR8bGxqWlpcOHD/f19e18qE5OTpGRkd988w2NRrt+/VVspeEAACAASURBVLqBgYHQi+IAdCmfEnzTFbZu3bqpq6uTFE8H4AweQGhiOqDX19fnL/imr6/f9C+lkxYuXHj16tXevXubmZm9e/cuPDxcJM0CyL1WBtnJCv5z8GRHASCTRHtAT6PRbG1tzc3NmUzmqVOnfH19Fy9e3MaQvbt37xYVFTUvKS8v/9ztfwUFhYiIiBcvXpSVlQ0YMEAmTj8ApMGnBJ+Xl9e0g9XU1DRtV1VVkRBXOygrK+MSPYA0oNPpdXV1eXl5ubm5RkZGPB7PwsIiKCjoc/WvXLny8uXL5iXl5eWtPhbbRCRD9gC6lE8Jvq6ujsQ4hIAzeAChifyAXlVV1dra2tramv9yw4YNbVTesWOHQMmQIUP09PSE6xoAWvUpwSsrK5MYhxBwBg8gNLEe0I8aNer27dviax8A2kOG78FjkB2A0MR6QM/hcMTXOAC0kwwneDwmByCdRPJ0HF9VVdWBAwfS09P79u27atWq7t27i6plALknuJqcDMEZPIB0WrJkiUja4XA4EydOfPv27YIFCzgcjpubW01NjUhaBugKcAYPAFIqOzu7oqLi5MmTFAplypQpT58+vXnzppeXF9lxAcgGMSZ4LpfLn8qezWabmJg4ODhQqaK8YIAzeAD5xmQy9fT0mqadNzAwwCS1AO0nrgSfmJi4bNkyY2NjExMTgiDodHp+fv6JEyfc3NxE1QXO4AHk28CBA9++fRsZGTl58uS7d+9ev359165dZAcFIDPEleCXL18eFxdnaWnZVJKfn+/t7Z2amiqqLnAGDyDfNDQ0YmJivvvuu7lz51pZWZ0+fdrU1JTsoABkhrgSPIfD4U9J3cTQ0JDH44mwC5zBA8i9QYMGifCsAKBLEVeCDwgIcHJymjx5sqmpKYVCodPply5dCggIEGEXOIMHAAD4HHEleH9//0mTJiUkJNDpdIIgzM3N4+PjzczMPle/1cUntLS02uhCUVGRy+VyOBxRLVoFAAAgN8Q4ir6kpMTQ0HDWrFlNizfHxsZ6enq2WjkhIeHVq1fNS/518Qni/6/Sq6qqiiRgAAAAuSGuBL979+4jR464urquWLEiOjra0dGRIIglS5YInKY32blzp0BJexaf4E9HjwQPAAAgQFwJPjg4OCsrq1u3btnZ2dOnT09LS/vcYs+dgdvwAAAArRLXVLWamprq6uoEQdjZ2S1btmzFihXi6AUD6QEAAFolrjP4OXPmDB8+3M/Pb+nSpStWrJg2bdrs2bNra2tF2wvO4AFk0bt378rLy5uX1NTU6OrqkhUPgFwSV4LfvHnz8OHDy8rKCIKgUCiRkZEREREi34FxBg8gi7Zt2/bkyZPmJbm5uWpqamTFAyCXxDiKfsyYMU3bCgoKc+bMmTNnjmi7wBk8gCwKDQ0VKBkyZMi/PjUDAB0iw8vFEjiDBwAA+AzZTvA4gwcAAGiVbCd4nMEDAAC0SrYTPM7gAQAAWiWrCf7NmzeLFi1KTU09evSowPM2n5OXlzdx4kQNDY3evXuHhYWJO0IAAAASyWSCLy8vHz16tI2NzYABA+rr6z09PTkczr++a+bMmSNGjPjw4UN4ePiGDRtSUlIkEKpINDQ0xMTEnDhx4vXr12THAgAAskEmE/zVq1ddXFxWr17Nf7D+6dOn69ate/TokZ+f36xZs06fPs3lcgmCKC8vP3/+fExMTE1NTUlJSV5e3tq1a9XV1Z2cnBYvXhwfH0/252iX6upqZ2fnw4cP379/f8SIEeHh4eLu8dGjR3v37g0NDRX5xEQAACAxMpng6+vr1dTUSkpKrly5oqCgoKKi8vvvvzs7O9+/f//LL788dOjQtm3bsrKybG1tz507FxISYmtrW15eXl9f33TDvry8XENDo6P9pqambty48eeff+avgSsZR44ccXJyunbtWkhIyPXr18U06W+Tw4cPe3t7l5aWxsbGOjk5MRgMsXYHAABiIpMJfuzYsQkJCVu2bHF2ds7MzKyoqPDy8lJTUxs7dmxERER0dPShQ4fWrFmzd+/eqKiouLg4Pz+/X3/9dfLkydOnT4+Njd27d++5c+eaZt1JTk4eNmyYkZGRp6enwJK1zZ05c2bmzJnKysp0Ot3JyenNmzeS+bCvXr0aOnQof7t///41NTVVVVVi6ovL5W7YsCE5OfmXX36JjIwcNmzYiRMnxNQXyJPm98gyMjLCwsJevHhBYjwAQMhogjczM4uIiEhISHj48GFpaencuXPV1dU1NDT8/f0zMjIUFRXr6+tfvHjh5ubGrz9y5MicnJzjx4+PHj366NGjr1+/vn37tpmZGUEQ79+/nzVr1vr167Oysjw8PDw9PRsbG1vtdPv27RcvXty0adN//vOfwMDA//znP5L5sI6OjrGxsfx/oLdu3TI0NBTHunx8lZWVVCrV3Nyc/9LBwSE3N1dMfUkGnU4vKCggOwr5Z2RkxN/Yv3+/p6fnlStXPDw8Tp8+TW5UAF2cTCZ4giBGjBgRExOjp6fn6Oj4+PFjRUVFJpPJYrE4HM6ePXvGjBljZ2eXkJDAr5yQkGBvb6+kpBQUFHT58uWjR4/27duX/6Pr169PnDjR09PT0NAwMDBQXV1dYIpsPh6PV1RU1PQua2triV2l/+abbwiCsLKyGjVq1Lx588R6Sq2np6erq8v/vdXV1UVGRjZdPJA5TCbTw8Nj4MCBgwcPHjlyJH9ZBGlz6dKlJUuWrFy5Micnh+xYROO333579OjRmTNnHj58uGPHDrLDAejSxDgXPZfLzcrKKi4uZrPZJiYmDg4OVKoojyccHR1//vnnoKCg4uJiBoMxYcKEgQMHcrncvLy8Y8eOVVRUuLu7R0RE1NfXV1RUJCYmttqIiopK01AyHo9XW1uroqLSshqFQhk+fPjhw4dXrVpVV1d38uRJd3d3EX6WNtBotEuXLmVnZ5eVlTk5OYnv9J2PfydCT0+vuLjY09NT5MsHSMyWLVt69eoVHx9PoVDWrFnz448/hoSEkB3UPxw4cCAkJOT7778vKSkZOXLkjRs37OzsyA6qs/T19Y2NjfkbCgoKZIcD0KVReDyeONpNTExctmyZsbGxiYkJQRB0Oj0/P//EiRNNl83/FX/xidjY2H+tOX78eAaD0a1bt7Fjx/r7+yspKfHLa2pq/vvf/9JotOHDh9NotFbfW1ZW5ujoGBgYOGTIkHPnzj1+/Dg5ObnVA5Hc3Fxvb++ioqLa2topU6YcP35cUVGMh0ckYrFYOTk5+vr6/L+djBo8eHBwcLCzszNBEC9fvvTy8pK2s+Qvvvjixo0bffr0IQhi3759b968CQ4Obl6hoKDAxcUlPz+fpAA7oEePHrq6uubm5i9evNiyZYuvr+/ixYupVOrx48fb2UL793cAuSSO/V1cKWr58uVxcXGWlpZNJfn5+d7e3qmpqa3WP3To0Pv375uX5OfnKysrt6ev9evXT5w40dbWNj09PTAwkCAIbW3t5kn6ypUrbbx9woQJZ86cOXjwYI8ePYYOHbp+/frP1Rw7dmxVVZWSkpKysvLGjRvbExuQpbKyctu2bf379ycI4tWrV/X19WvXriU7qH8oLi4+cuQIjUZbsmSJqanp53YNmUCn0+vq6vLy8nJzc42MjHg8noWFRVBQENlxAXRp4krwHA6Hf6WuiaGhYRtXC7p37y4wONzExMTW1rY9fbm5ub179y4nJ+f169f8qekZDAb/Ufj20NXV7dWrV/srt7MmkMvDwyM0NJTJZFKp1LS0tLlz50rb365Pnz4ZGRnjx49vaGg4ePDgwoULyY6oU1RVVa2tra2trfkvN2zYQG48ACCuBB8QEODk5DR58mRTU1MKhUKn0y9duhQQEPC5+t7e3gIlZWVl7V8fWl9f39XV1dXVVfiIQe6sXLkyPDycx+MdPXrUysqK7HAELVy4cOHChdu2bfvpp5++++67RYsWkR2R5NTV1dXX1zcvYbPZYrpdCNBliSvB+/v7T5o0KSEhgT/a3NzcPD4+nv9kGoBkWFhYrFu3juwoPsvQ0DAhIaG+vp5Go8n6eLQjR44kJSW1LD979myr9adNmyZwS6KqqkpVVVUswQF0VWIcJmZmZrZ06dLOtJCamnrs2LFWfxQdHd3+83uRqKyspNFo6urqEuuRx+MVFxcL3OkQt7KyMg0NjXaOfhAJNptdVlZmaGgosR4Jgvjw4YOEh3mzWCx1dXUXF5f2v6WiokJ88YjW/PnzU1NTmUymv79/e+o3PcLaZM2aNW/fviV9f6fT6T169BB3LxLbtblcbklJSdMsBeLD4XA+fvwogb24sbGxoqKie/fu4u6ooaGBwWBI4FtXX1+vpaU1ZMgQcezv0jsOfPz48eHh4WlpaS1/xOPxrl69amNjI8l4Pnz4QKPR9PT0JNZjY2NjXl5e7969JdYjQRCFhYVaWlrifhivubq6ug8fPvTs2VNiPRIEkZuba2hoKMlTRiaTyeFwmh7xaKeVK1eKKR7RUldXX7ly5blz58aMGSNcC9Kwv3M4nNevX0vgbk5jY2Nubi7/AQqxYrFYBQUFzQc7i0ldXV1xcXH7BzMJraam5uPHj1988YW4O6qqqqqsrJTAVWf+dOD8Z7JEv7/zZBCbzVZQUJBwpytWrNi/f78ke8zLyzM3N5dkjzwez9vb+/z585Ls8d69e8OGDZNkjzweb+jQoffv35dkj+Hh4TNnzpRkj3JDYvt7ZWWltra2BDrKz883NTWVQEfPnz+3traWQEcPHjxwdnaWQEc3b94cPXq0BDq6ePGil5eXBDr666+/fHx8xNS4rM5kBwAAAG1AggcAERs1ahTZIQAAEjwAiFrzxeUAgCxI8AAgYr6+vmSHAACymeCpVKqjo6OEO+3Ro4cEnjZpTkNDo2n9OokxNTWVwCMozenp6Ul4CD1BED179pTkAxEEQRgaGpqamkqyRxItWbJEhK1JbH9XVlZu5+yZnaSuri6ZmZe0tbUl8xhOh+YD7Qx9ff2m9azFysDAQDITtxgaGopv1Q9xLTYDAAAAJJLJM3gAAABoGxI8AACAHEKCBwAAkENI8AAAAHIICR4AAEAOIcEDAADIISR4AAAAOSR7Cf7QoUP9+vWzt7dPTEwUd18//fRTv379zMzMdu/eLcne9+7dGx8fL7Eer1696uzs/MUXXzStxi3uTteuXdurVy9LS8u//vpLAj3W1NQcOXKk6WXLvkTeu0CPZH2R5IC4f1ES/tNIYNeW2O4sgb1YYnuuxHZYgY74xPitENMqdWLy/v17a2vrmpqaV69eWVlZcTgc8fV148aNgQMH1tXVlZaWmpubp6WlSab3u3fvKikphYaG8iTyeSsqKvr06VNaWspgMKysrBgMhrg7TUpKcnBwqK+vLyoq0tbWrq6uFmuPjx8/XrRo0ezZs/kvW/Yl8t4FeiTriyQHxP2LkvCfRgK7tsR2ZwnsxRLbcyW2wwp0xCfWb4WMncEnJCRMnjxZTU2td+/eRkZGWVlZ4uurrKzMz89PRUVFX1/f1dU1Pz9fAr0zmcx169bNnj2b/1ICPfKXPdbX19fS0srOztbU1BR3pwoKCkpKSjQaTUVFRVFRkRDzxzx79mxlZWXTy5Z9ibx3gR5J+SLJB3H/oiT5p5HMri2x3VkCe7HE9lyJ7bACHRHi/1bIWIIvKipqmtDb1NS0uLhYfH3NnDlz6dKlBEFkZ2f/97//HTVqlAR6DwwM3LJlS9M06RLoMT8/Pzc3187Oztzc/Ndff6VQKOLudPjw4X379u3Ro4eZmdnWrVvV1dXF2uOuXbv8/f2bXrbsS+S9C/RIyhdJPoj7FyXJP41kdm2J7c4S2IsltudKbIcV6IgQ/7dCxhI8l8ulUChNL9lstli74/F4+/fvnz59enR0tLa2trh7Dw8P19PTGzNmTFOJBD5vXV3dq1evkpOTMzMzjx8/npaWJu5Ob9269erVq9u3b8fHx+/fv59Op0vyz9qyLwn0LuEvktyQmz+NxHZtie3Okt+LJbnnys23QrGT75ewHj16vH//nr9Np9N79Oghvr44HM6MGTN0dHQePXqkpaUlgd5DQ0PfvHlz48aNoqKi8+fPs1gsCXxeAwMDDw8PXV1dgiBGjBiRk5Mj7k7j4+PnzZtnbW1tbW09dOjQ5ORkSf5ZW/Yl7t4l/0WSG3Lzp5HYri2x3Vnye7HE9ly5+lZ08h6+hOXl5fXv35/FYuXn5/fu3Vuso5P+/vvvWbNmkdL7ihUr+GMuJNDj8+fPbW1ty8rKSkpKzM3Nnz17Ju5Ojx07NmHChKYxLBkZGeLu8caNG00DW1r2JY7em/dI4hdJ1on7FyX5P424d22J7c6S2YsltudKbIdt3lET8X0rZOwM3tzc/LvvvnN1dSUIIiQkhEoV4y2GO3fuJCQkGBsb818eP37c09NTYr3zSeDzWltbL126dMiQITweb/369TY2NgRBiLXTRYsWZWRk9OvXjyCI1atXOzg4iLvH5lr+SsX9S5aGL5KMkuM/jZg+msR2Z8nvxRLbc+XpW4H14AEAAOQQTh0AAADkEBI8AACAHEKCBwAAkENI8AAAAHIICR4AAEAOIcEDAADIISR4AAAAOYQEDwAAIIeQ4AEAAOQQEjwAAIAcQoIHAACQQ0jwAAAAcggJHgAAQA4hwQMAAMghJHgAAAA5hAQPAAAgh5DgAQAA5BASPAAAgBxCggcAAJBDSPDS5e3bt1OmTFmwYMHRo0fJjgUAAGQYhcfjkR0DfLJmzZo5c+Y4Ojq6ubklJyeTHQ4AAMgqRbIDgH/Ytm2biopKQUGBvr4+2bEAAIAMwyV66aKqqhoZGRkQEHDo0CGxdpSUlDR16lRJvpHBYAjxrpYqKipyc3NF0hQAgBxDgpcucXFxKSkpMTExRkZGZMciSkwm083NremllZWVoqIijUaj0WjR0dHNax4/ftza2trIyGj+/Pl1dXV5eXkuLi7W1ta///47v8LGjRtZLJZEowcAkEG4RC9dYmJiiouLZ86cqaKicubMmfa8pbGxkUajtdxuu6Yk7dq168yZM/X19fyXPB6vvLyczWa3rJmWlrZr16779++rqanNmTPnl19+KSoq2rp169ixYwcPHrxw4cLi4mKCIKysrCT6AQAAZBDO4Mm0atWqvn37PnjwgCCIyMhIGxubw4cPx8bGnj9/vmV2P3jwoIWFRZ8+fVatWsXlcu/du+fj4zNnzpzt27c33yYIYufOnZaWlhYWFmvWrBGo2TIGgWbHjx9/4cIF/o+cnZ2Tk5MFKgjxMT08PHbt2tX0srCw0NjYuNWa79+//+6774yMjLS0tKZPn/769Wttbe1nz57l5+fX19fTaLSdO3du2LBBiBgAALocHpAqKirKw8Ojtrb2iy++SEhI+Fy127dvDxgwoKSkpLq6evbs2Tt27Lh79662tnZOTg6Px2u+feXKFSsrKwaDUV9f7+rqevr06eY/bd7glClTWjb7559/zp8/n8fjvXnzxsLCIjExUaAC/43Nm/rqq6++aOHx48fN6xQUFPTs2ZO/nZycbGZmNmbMGENDw6+//rq2trbl5y0rK3N2do6JiWEwGH5+fmPHjr127drDhw/XrVsn5C8aAKCLwSV6kk2dOnXt2rXLli2zt7cfP37856rdvHmTwWDMnDmTIIjS0tKqqqpRo0Y5ODg0Xaxu2r5165a3t7eWlhZBEHPmzLl165aFhUXzmm03GxYWtnbt2sbGxvDw8AULFty6dUuggqurq0Aj8fHxHfrI2traK1asCAwMZDKZPj4+v/zyy5YtW5pXCAsL27p169atW6dMmUIQxJEjR/jlM2bMOH78+OXLl4ODg+3s7Hbs2KGsrNyhrgEAug4keJJRKJTFixdv2rTp6dOnbVTT0NDw8/Nbu3YtQRD19fUNDQ3Z2dmamppNFZq2eTwehULhbysoKHA4nOY//ddmtbS0hg4dmpiYGBERER0dff78eYEKGRkZAo14eHi0DD4+Pt7e3r7VTvv162dra6ugoNCtWzcfH5+YmJimH3G53IULFzIYjDt37ggMM7x27drgwYM5HM7WrVtjYmJCQkKCg4ODgoLa+KUBAHRluAdPvoyMDE1NTTMzszbquLu7nzp1qrS0lMViTZ8+/d69e5+rOXr06KioqOrq6oaGhrCwsNGjR3e02dmzZ+/evVtXV7dXr17t6ffq1asFLXwuuxMEERwcPHHixPr6eiaTef78eVdXVx6P9+LFCzabffHiRTqdfuHCBYHszuPxgoODAwMDa2pqdHV1zczM7OzsysrK2vhoAABdHBI8ye7du5eUlDRs2LDIyMg2qjk4OKxYscLFxcXS0tLS0vKrr776XM3x48fPnTvX0dHRxsZm8ODBvr6+HW120qRJDx48WLBgQYf6bb/vvvvOwcHB1ta2f//+vXv3Xr58eUNDg7W1dWFh4Z07d+7cuaOlpaWpqampqblw4UL+WyIiIry8vFRUVMzNzV1dXW1tbQ8ePLh8+fLOBwMAIK8wVS2ZuFzu4MGDAwICevbs+cMPP6SkpCgq4qYJAACIANIJmUJDQykUyoIFCygUio2NTa9evZKTk3v16kV2XAAAIPNwBg8AACCHcA8eAABADiHBAwAAyCEkeAAAADmEBA8AACCHkOABAADkEBI8AACAHJLe5+DPnDnzn//8p+06alSFWi5HMvEASF7//v1DQ0PJjkIS2rO/A8g3ke/v0pvgs7OzXV1d58+f/7kKtPxirYgbFUuncTXVJBkYgGSUlJQsXbqU7Cgk5F/3dwD5Jo79XXoTPEEQJiYmTk5Orf+My6Wf/YViYWaa9dbg+wWSjQtAEgoKCsgOQaLa2t8B5J049ndZvQdfdeO/VBVlo00BrJw39c/fkB0OAACAdJHJBM+tq688n6C3aDpFWUl37qTyPyMJTLgLAADQjEwm+MrweLVBdkoW5gRBqLsOoiorVd9OJTsoAAAAKSLV9+Bbxf7wsSohSXWQXdnRs/8rolIrwi6puzpRaDRSQwMpxWQyra2t6+vryQ7kE21t7Tdv3lCpMnmEDQAyQfYSPFVTXW/JTKLZJXklC3OKogJFQYG8oECq1dTUcLncN2+kaKxG9+7duVwuEjwAiI8YEzyXy83KyiouLmaz2SYmJg4ODiL5d0ZVU9UcO7zz7UCXQqVSdXV1yY4CAEByxJXgExMTly1bZmxsbGJiQhAEnU7Pz88/ceKEm5ubmHoEABKJ6YCej9fIptBk73IjALnEtc8sX748Li7O0tKyqSQ/P9/b2zs1FaPhAOSNWA/o2WWV9KCdRluWK1mYdb41gK5DXAmew+EYGxs3LzE0NOThYTYAeSTWA/qKvy4oGuqX/xlptH0FQaF0vkGALkJcCT4gIMDJyWny5MmmpqYUCoVOp1+6dCkgIEBM3QEAicR3QM96+Y71/E2P/Rs+bP295r8Z6sMHdr5NgC5CXAne399/0qRJCQkJdDqdIAhzc/P4+HgzMxFfYeMwqqgqyhRlJdE2CwAdIq4Deh6v/GSkrs9kqqqK3iLv0v1/qg3qj/0doJ3EOG5FS0tryZIlFAolLS3t+fPn1dXVbVT+8ssvb9++LVA4dOjQ1atXt/GuynOxSr3MNN1dOx8tAAitowf0mzdvzsnJaV5y//79wYMHC1SrTkwhqFR1VyeCIJStLJStejEu3tCZOUEMnwBADokrwR87dmzfvn3Z2dl79+4NCQkZMWLEhg0bNm7cuGTJklbrJyYmCpQMGTKkW7dubffCa2Bzq2tEEzF0cTzehx2HtDy/VHXsJ8S7T58+vWXLFv52Y2NjYWFhXl6eubl5TU3NmTNnvv32W5HGKo3MzMzavxbWuHHj7OzsmpdkZGQwGIzmJTxWQ8XZWGVL84qwS/8roiowLt7QHDdcQVdbFCEDyDlxJfidO3dmZ2fTaLTDhw9nZWXp6emVlpYOGzbscwleODx2I7e2ToQNQpdVnfywsbC4/OT5Hvs3UhQ7PGmSr6+vr68vfzsoKOj9+/fm5ubZ2dkHDhyora3tCgm+Q0aMGCFQ8uuvv6qoqPyjSEFBe/IYHpvTVKCkoab0hQku0QO0k7gSvIGBQUNDA0EQOjo6/MdhFcQw0xyPzeHWIMFDZ3HrWRVhlwyCvmZeuMGMS9SePFbopmJjYy9cuJCenk4QxNmzZysrK5WU5D8hHTlyJCkpqWX52bNnWxa2E0VRQctzdCeCAujqxHgGP2zYMC8vr759+44aNcrd3T0uLi4wMFBkHXC5lZFXeI1sJHhoPx6Hy3r+mscVHN1dfeu+kqkxr4GtNtSx/MR5RQM9qoa6QB2akb5i93+5Z1RYWLhkyZKYmBgdHR2CIHbt2nXz5s2QkBARfgTpNH/+/NTUVCaT6e/vT3YsAPA/4krwHh4eDx48uHjxoo6OjrW1tYGBQVRUlLW1tajar7rx38rzCTQjg3/9nwvQhFPBYMRcE1hcmNfQyHqVp9y3J/PCNYIgqBqq5aFRSqZGAu9VtumtM+OrthrncObOnRsUFDR06FCRRy7l1NXVV65cee7cuTFjxpAdCwD8jxhH0evo6CxYsEAcLXNraisj4rv/sLh030nckIP2U9TXNdwk+OxW6W8nlK16qY9w5r/k1tVXhF3UXfttR+dN27Ztm6qq6g8//CCaWGWNvb29vb092VEAwCcyOb1zZUS82pABakMGULXV2SUfyQ4HZBvNzJiqodbw9n1TieZoFx6X26FGEhMTT548mZ6eTsFUawAgHWQvwTcWfqi586jHfzYSBKGgpdlQWcR6nafc+wuy4wJZJZLnqvfu3VtdXT18+Kd1Dm/evGlubt75lgEAhCN7Cb48NErzq5EUKpVbU0uwORSCVx4aZfzTSkxSDSSKj49vtXzMmDG4LQ0ApJCxBM9hVje8ec96mcuMTSQIgldXx+PyGguK2aXlGG0HIK9q/puuam9N1VAjOxAAWSJjCV5BS8Ps9Co9tAAAIABJREFU5J6mlwX+W9glZSb7NijoYWYrAPnUkFtYeiBUc5xrtyUzCYIgeLySvce1vcYqW1uQHRqAVKOSHUCn8BrZVDVVTGYHIMcqTkfrzvGsTc1oyCskCKLm7iNWztuyExFEBwdCAnQ1Mp7g2WwFHU1uTS3ZgQCAWNTcz2BXMLW8xup4f1UeGsVjNVSEXTJYvYSqqlp16z7Z0QFINdlO8ASbo6ClicnsAOQSr7Gx4swFvYXTKQpUTXdXLrPm4x9nVPr1VrGx1Fs0vfJcHPZ9gDbIdoLnsdlUbSR4APnEuHhT2dJcdYA1QRAElao91b02JUt75gSCIJR6mqoNsquMukJyiABSTFoG2YWFhRUUFDQvodPpqqqqbb+Lx+YoaGngEj2AXKq+fpddVlnj/Y/JB6tu/FfZ0pwgCKWeJuWnojXHDqf16E5SgABSTVoSfF1dXUVFRfMSLpfL4wkuCtIcj82hUKlUTTUMsoN/xWAw/Pz8yI7iEy4GiLWD6dEdzV+WnzjPqWRyPnys/fC/+SvVBtlxq2vICA1ABkhLgl+8eLFASWJioqam5mffwONxWQ0UmiJVTRV7OLTN0NAwODiYv36xlBg5cqSiorTsfbJC75sZZIcAIEtk9V9M+Z9R7LIKQlGBqq7K/oDp6KEtVCp14cKFZEcBACBRMpngG/OLau6lURQVCIJCVVPDIDsAcnE4HAUFBf52RkbGs2fPBg0aZGVlRW5UAF2cTI6iLz8Vre09Xmv6eF59PVVNBQkegFxGRkb8jf3793t6el65csXDw+P06dPkRgXQxcneGXztw8fsjxWa7q7skvJKKrX+2WuMogeQEr/99tujR4+MjY1LS0uHDx/u6+vbarX4+HiBp2ZKSkrU1dUlEiNAVyFjCZ7H5lScuaC3aAZFQYFgs6l62lU37ymoYQkKAKmgr69vbGzM32i6aN/SkydP3rx507yktrZWqkZBAsgBGUvwVQlJHGZ1/ZOX9U9echhVvDoWVZHGqWCQHRdAl0aj0Wxtbc3NzZlM5qlTp3x9fRcvXuzq6vq5+mvWrBEoyczM1NHR+Vx9Vs5bgkIoW2F1GYAOkLEEr2Rprj1lHH+bW8+i0BQ1xrkyIuLIjQpA5ixbtqzV8sOHDwvRGp1Or6ury8vLy83NNTIy4vF4FhYWQUFBnYvxf3iNjaW/nyIoFJMDGyk0GfuXBUCiVvaWzMzMzMxMHx+f4uJiMzMzycfUBpV+vVX69eZv1z9/05hXqDPdgxF5hdfQSFGikRsbgAzx9PQUbYOqqqrW1tbW1tb8lxs2bBBVy/wJa3lcLvPyLe1p7qJqFkDuCSb40NDQ3bt319XVeXt7jxgxYuPGjS2noJESvEY2oahIEARVXZVbU6ughCXhAdpr4sSJAiVcLnfjxo0ty8nFKa9kxt4y3rOaQqEUrf1VY9QQBT3s6QDtIviY3P79+1NSUgYMGKChoZGZmblnzx6hm+ZyuRkZGQkJCZcvX05PTxf93JxsNv96HVVdFU/KAQjhl19+6dGjh6qqas+ePTU0NLKyssiOSFBF2CVNjxE0IwNFQ32NscMqzl4mOyIAmSF4Bl9TU6OiosLfVlNTo9GEvO6dmJi4bNkyY2NjExMTgiDodHp+fv6JEyfc3Nw6E25zPDaHoqhA8BM8pqMH6Lg//vjj+fPn69ev9/f3ZzKZf/75J9kR/QPr5bu6xy9Mft9EEASnkqnU06T81APW6zzl3l+QHRqADBBM8D4+Pl5eXkVFRSdPngwNDfXx8RGu3eXLl8fFxVlaWjaV5Ofne3t7p6amCh/sP/HYbIqiIkEQFBVlnMEDCEFZWVlLS8vR0TE1NXXRokUrVqwgO6J/qE5M5VQw3s//4Z+FKUjwAO0hmOC3bdsWFxd39+7dwsLCXbt2tfGgS9s4HA7/cdgmhoaGba8O11H8M/iae2msp6/ZwxxF2DJAF+Hm5rZo0aJ169bNmDHjw4cP2trSdXu7m9/sbn6zCYJgvXxX+tsJjdEuDbkF3ZbMIjsuANkgmODLysqGDh06dOjQppfdunUTot2AgAAnJ6fJkyebmppSKBQ6nX7p0qWAgIB/f2f7NbIJKrXizAWqtkZN0kPNsUIeiwB0WSEhIS9fvuzbt+++ffvS0tKk7RL9//B45ScjdedNVnMZSA/aWZfxTNWxH9kxAcgAwQQ/fvx4/gaDwXj16tWAAQMyMzOFaNff33/SpEkJCQl0Op0gCHNz8/j4+DYeuhs/fvyDBw+al1RVVdnb27fRBY/NbigoVra2VNDRqr5xryG3UKmniRChAnRZ5eXl+vr6ZWVlDg4ODg4OZIfTuurbqQSVqu46iKBQdOdPLT8V3cPeivL5afIAgE8wwT98+LBpOzs7+3NTSbeHmZnZ0qVL+dvHjh1r+5H6ixcv1tb+Y0r5sWPHClzkF8BhVDe+p3df9U3N3UfKVhbloVFGW5cLHS1AFySqA3rx4dazKs7Gdl+zhKBQCIJQc7aruppcdfWu1oSRQrTGqWBQFBWpmpj0HrqEtqaFsrOza2xsFK7d+Pj40tLSppfbt29XVlYmCGLBggWt1ldWVuZX+BSZ4r9MWVX7IFPZwkzRQI+qrqZooMd6nVubkqk2VErPQgCkkAgP6MWk9sFjTnll0dq9zQvZHytaJvi6rBzlvj2pqiqfbYvHK9l7XEFLs/taP3GECiBtBJPoypUrm7ZzcnKEXtH56dOnGzdunD17tp6eHkEQ9fX1/DODzyX4jmK9zmvILaSZG5cdPcv+8LHhPZ1bW19+OkZtyAD+kT4AdEhnDujFR8PNWcPN+V+rsYs/luw5ounhprdw2ufqVCc/JLi8xsLiusznqg42Ig0TQBoJJvjmw+YnTpz45ZdfCtfu6tWrhw0btm7dOl9f3zFjxty4cWP//v3Ch9kCzVBfbVB/HkFRsjCnUKn1T18raGuq9OuN7A7QfqI6oCdd+aloza9G1txO1Rw3nGZi2LICt55VEXbJIOhrbnVteWhUj9/W4S4+yL1PCZ7/hLqpqWnzHz969GjIkCHCNT18+PDY2NjAwMDY2FgWi9WZKFuiaqrTzHpQ1VQ0xw0vO/QXRU3FcPv3xet+41QyFXS0RNsXgLwS1QE9ueqzXza8pxsELVLU1S4/ed5wUytP6zCir6naWalYWxIEUXX1TtXVO1oTRrXaGqec0fCejlN8kAOfEvyBAwcIgqDT6Xfu3LG3t9fQ0EhNTZ06dWpERITQrWtpaZ06dercuXM5OTkiCPaf+BPdsD9W1KZmU9SUaUYG6iMHV4bH8x+cBYA2iPyAnjRcbvmfkXoLp1FoipoTRlXdul+X/lR1oG3zKuySsurr93r8to7/Um/BtOIt/1EfMUhBU6Nle2Unz9dn5Zj8vklBV7pmBQDoqE8J/uzZswRBjBs37vnz5/wrde/evWsaBt8Zs2fPnj1b9EmX18gmFBUqTsdojHetirtNEITOrAmFy3/SHDdMycJc5N0ByBNxHNCTgnnlDlVbQ83ZniAIigJVb+H08uPhPeysCB6PoFL5s1lXnk8gKMTHP043vYvXyGZeuqXr4yXQWv2Tlw1v8zXGuFSEXdIPmC+B+D/+cUa59xea40U2hzdAE8F78Hl5eX379uVv9+zZk/8Uu5RiczhlFTX3M7S+cuM2sitORRMKCop62uVnLhhtwfNyAG0R3wG9hDEv3+Qwq98vWNNUwq2prUt7Up30gKqhpv/dPIIgtKd7qI8QHKlH69FdsC0utzw0Ss93qoqDTeHynyQw6X39s9d1j3Pq0p+qDRuooNXK5QSAzhBM8C4uLt7e3j4+Pjwe7+zZs87O/z5+lSw8Npuqoc4/BqeqKhMKClQNNTUXRzzkCtBOIjyg53K5WVlZxcXFbDbbxMTEwcGBShVcrFIceuzfQHA4AoWsV3mN+UXchgZ+kqYZGdCMDP61qarr96ga6vxHbXXneJafPG+8c5UYx+3yeOWh0Xpfe7NevK08F9ttKe4tgogJJvhjx44dOXLk77//plKpI0eOlOYjeh6bo6ivq+46iCCI2gdZaoPtlfv2IjsoAFkiqgN6Cawe+TlUFWWBEh6HW3E6RnfBNG51TfnJSOOdQe1J0tya2srzCYYbv+O/1Bg1pOrqneo7j9rzkJ5wqm7coygpqg91ULW3Lvw/9u48Lqpy/wP4c2ZhZ9gFZFFBEVwQU0QFvaZesdxKSeGaeDOXNDQqKy1LzdRui9ybP5fc0rQsd4zFuu5bbohIKWpujAzINszGMNs5vz+mizQsAs45w4yf98s/zjycOd/vwDx+z/o8b3zsOhzXFsHMHhX4uXPnLlu27MMPPySEGIeQu3nz5vz581evXm2x7JpUO5scIYTvJjLIFJbNB8DqmGuHvqWzR5aVlSkUf+mw1dXVSqXyzp07xpcBAQHGka/UanVxcXHtas1pv7XzgJrW1ng5E08nvk6rOn3JeVD0Y7ejOnlRKinSLFplx+cTQmoM+ofyKmFVZbtAryfMp8F2VaX06pad3q8lqe/eJYSIXhxRuWWv37JUdU2NWbaPdqtrLywspGmamBfzPwcOHFCr1Rn1MBbSr1+/UaNGNbHCwxXrVBevGpfL138v/+U0J3kBcEQsFgcGBrK08ZSUFKlUmlJP67YWERGhUqnqtmg0mujo6MbWT0hICPkroVBob29f+zI9Pd245pYtW+qu9tj2zevWB7u6dwruYGzfs26DeOYHdI2mWdvp1OnAj7sMSpVBqdq8bn1Ip04hnTo9YT6Nta+enhLs5tnRv73x3w9LVt77x5uqc7nm2j7ara49ODjY0dGRMSuK+esUrgUFBWfOnHn11VcXLVp0/PjxTz/9tNUzxj6hmJgYHx+fjIyMBn9afSlfnnHc7cXhjr0iCCFVP2QQgcA9YSS3OQKw6MGDBwMGDBCLxWxsPD09PT4+/siRIybto0aNasXW1q5du3r16vqzR86a1dxBYZvu781XuXWf6myOU58etS2qX3NFo591T3juCbdsXopDJ7X3i0wanfr1wkR5Ty02+rvpNfg33nhj2rRpFy5cOH369Keffrp8+fLs7GwzxjMLXXFZ2RebeCKX2lP0PDdXvaTUslkBWJFx48YRQkJDQ0126Fu3tZbOHskep/69TEayswsJtguxQCZNw3NxwAHTAl9SUjJp0qQFCxa8/PLLcXFxtZfE2hTp1r2iscNlBw/rK6TGFr67SHPtD8tmBWB1zLhDX3f2SAtyCA81DlcHAKYFvmvXrm+//fauXbsuXryYlpYmEnE07Ovu3btNdiaKi4udnJzqr1mTf0NbVOIzf7rq5AV51nGXwf0IIXw3V9xkB9BSVrFDDwCtY/qg6po1awICAg4cOODn51dRUWEcDYMDZWVl0r/S6/WGeo+3Mga68pu9nv+cQAkFPCdHRqWuvnCVEMJ3dzVUocADtIxxh/67774bPXo0lzv0AMAB0yN4Hx+foUOHXrlyJTIyctasWZxdRZszZ45Jy7Fjx+r/d6M4dJLn5urUtychhNEb3CaMrNy2z7F3BI7gAVphzZo127dv536HHgA4YHoEv3Xr1kmTJn300UcajWbQoEGbNm2ySFqNkWce09y8Wzj13cKp7+qKSyu37NGXVlRf+o3n5MhotYxOb+kEAayJcYc+Pz9fp9PNmjWrc+fOls4IAMzG9Ag+LS3t3LlzycnJLi4uV65c6du37/Tp0y2SWYMC0j5g9H9W8aLU5X6LU/gebjwnR0JRfJGLQa4UeLlbNkMAK7J169aVK1eq1eqEhIRBgwYtWrSoTfV3AHgSpkfwKpXKwcHBuOzk5CQUCjlPqSmUvR3P2cn4jxgMPJErz9nJOA4lz01Ey+SWThDAmhh36Hv16mXcoW/1Y3Jso6vVlVv2kDrjfBmqFMWLVtGqagtmBdDGmRb4yZMnjx079u7du1u2bBk+fPjkyZMtklZzMDo9JXx0BgL32QG0VBvfoa8l23NI8cspxX/P1LZU7fxJX1xW9WOWBbMCaONMC/zSpUtTU1PHjBlTVFS0YsWKRYsWWSSt5mD0BuNkz0a4zw6gpaxih15fUq48dt73gzlVu7JohYoQor37oPry7/4r56vO5mgL2/CU1gAWZXoNnhAyatSo2uEqFQqFq6srtyk1V93JZgghfHeRoQqn6AFaYOnSpZmZmadPnzbu0FtqXOqmVX6zR/Ti3x16dnXqH1W1J9vzlYTKb/Z4JI4WtPNyGx9f+c1ev8VzLZ0jQFv0qEBevnx54cKFlZWVzz///Ny5cxcuXFhYWGic4NmC+TWGMdCER9WdBZLv5qIvr7JgSgDWqI3v0Kuv/jmwFSHEI2lMUeoyvqc7ra5xeTaGEOIaP1h5+Gz1pXzjo7MAUNejU/SvvPLKgAED0tLSJBJJZGRkQEDAwoULz507Z8HkmvLXw3fy54yxOIIHaJbLly/Hx8dHR0cvXry4vLx8xowZ8fHxXbp0sXRef8EYDJVbdouef9ZQWaV/WE6rql3ioqt+yHDoHqY6n6f6Nbf6Qp59t87SbfsYvemgWADwqEZKpdIlS5YQQiIiItLT043LbZbJBXiCm+wAWuKVV1558cUXhw8fvm3btsjIyJkzZ06ePLljx46Wzusv9A8riN4gzzwmzzxmbGFomhII9KXlhv/NQ0EIsQvtwGg0lKCBka0BnmZ1rmHz/6yXXl5ezs7OT75pmqaNZ/j1en1AQEBUVBSPZ3pPX6uZ3EJPCOG5udK4yQ6geaxih17Yvl3A/y22dBYA1qqBm+zM4tixY7Nnz/b39w8ICCCESCQSsVi8efPmwYPNM0kiU/8UPW6yA2g2s+/QA0Bb86hG3r9/v/b+GpVKVbusULTmsHjevHmZmZmhoY/mbRSLxQkJCefPn3+CbOvQG4jJKXpXZ7pazRhoim+28wQA0BwGg6F2jyE3N/fatWt9+/bt2rWrZbMCeMo9qoVqtbr8f+out267BoPB39+/bouvry/DME+UbB31j+AJRfFcnGmF0lwhAGyYcYfeqO5y67bm5+dnXEhLSxs9evShQ4fi4+O//fZb8+ULAC32qEba29ubcbspKSl9+vQZN25cYGAgRVESieTgwYMpKSlm2Tij01ds3l33GTkj4312fHdMeQnwGGq1mo3Nfvnll5cuXfL39y8rK4uNjU1OTmYjCgA0B1vX4OfMmTNmzJjs7GyJREIICQ4OzsrKamLy2eeff97kkTyFQtGrV68GV5ZnHNXcusezMx1WU+DjpX9Ybtcx4InTB7Bx5t2hr+Xt7W08deft7V170h4ALIKtAk8IKS0t9fX1nTRpkpubm7ElIyNj9OjRDa68Z88ejUZTt2X48OG+vr711zRUKeQHj3pOS6jc8KPmdqF9aHDtj4SBfrqiEkIa3i0AAJYIhcLu3bsHBwfL5fJt27YlJydPnz69iXHxJkyYcOXKlbotRUVFPXr0YD9TgKcIWwV+5cqV69evj4uLS01N3bdvX+/evQkhM2bMKC4ubnB9JycnJ6e/PMYqEAioeifhCSHSHQdc/j5Q6OMl8PWq/GaP/7I3a8/VCwN8a/JvmPujAMBjSCQStVp9//79e/fu+fn5MQwTEhLy1ltvNbb+pk2bpFJp3Zbx48c3uEMPAK3GVoFfs2ZNXl6el5dXfn7+hAkTcnJyzDIEpvZOoTqvIOCrDzUFdwTtvGi5UvVrrvPAZ4w/FQb4Kg6dfPIoANBSjo6O4eHh4eHhxpcffPBBEyt7eHh4eHjUbbG3t29whx4AWo2tJ8pcXV2ND9f27Nlz9uzZqampZtgow1Rs2u3Ut4fm1n3NH/fparVjnx7S7QcYrc74c71Upr1XRMx3rz4AtMKQIUMsnQIAsFbgk5KSYmNjN2zYQAhJTU2trKxMTEysrq5+km3SNRqes6P+Ybn8wC/V53L1JeWagtvC9u0MUhkhhNEbqr49wNAG1fk883wGAGgVg4GtkeFplZquZuX+fwDbw9Yp+o8++ig2NraiooIQQlHUnj17du3aZXJSrqV4jg6+H8wxLqtOXay+fM3njam1P5VnHBUG+RM7gXT7Aed+kcR8w+ICQIuw93Rc2arNlFDYbsEslrYPYEtYrILDhg2bOHGicZnP5yclJa1bt85cGzeZbMYgU8gPHvGY8oJDty6EohRHfzVXIABoqRkzZrCx2eqL+fqKKl1RiTrvOhvbB7Ax1nqYazKSnfS7gy5D+wvbtxMG+NqHBFX9kEmrcB4PwHYweoN0+37Pf07wSB5f+c1ehrWrAAA2w2oLvE5PCf88gtfeKVSdumTfpaP66g1ao9NJSgV+3lV7D1k2QwAwI3nGUWGAn2NUhFN0T4GPp+LnU5bOCKCts9oCrzfUHsEbpHKHiBDFz6fkB36pycnXFZXw7ISExr30ADai9hqc8aXn1PGyvT8bMPEEQJNYHMmOXXo9+V+Bd+zTw7HPozGwCqfM93n7VZ6zUyPvBAArI9t7iPCoqp0/PWqiGfmBw7UlHwDqs9YCb3KTXV3CAF9d0UP7sE4cpwQALHH9e5x9ROe6LU4Dn7ELbm+pfACsgvUWeD3PwaHBHwkD/FDgAWyJMMhfGOT/+PUAoA6rvgbfyBF8oK+uqITjfAAAANoUay3wda/Bm7ALCa4puMNxOgAAAG1KWzlFn52dLRaL67aUlZU1MT8NozPUPiZnwqFbZ11hMa2s5rngPjsAjtA0nZeXV1JSotfrAwICoqKieBhNEsCi2kqBv3r16p07fznsVqlUanWjg9WYDHRTFyUUOHTrrM4rcI59xsxZAkBDjh07Nnv2bH9//4CAAEKIRCIRi8WbN28ePHiwpVMDeHq1lQL/3nvvmbRcuXKlibHrmyjwhBDH3t3Uub+jwANwY968eZmZmaGhobUtYrE4ISHh/PnzFswK4ClntefQ9AbSyE12hBDHPj3UudcwbywANwwGg7//X+5y9/X1ZdABASyqrRzBt1TTR/ACbw+eq7Pmjlhz7Q+em6vL4GgucwN42qSkpPTp02fcuHGBgYEURUkkkoMHD6akpFg6L4CnmvUW+EYfkzNyfKa76vRF5bHzlIDv1Kc7BrYDYM+cOXPGjBmTnZ0tkUgIIcHBwVlZWUFBQZbOC+CpZrUFXtfUETwhxKl3t9Ivt7iNHaavqKraleX5SgJnuQE8hYKCgmbOnGnpLADgEWst8ERvII08JmfE0AxTXe3YL1Lg7laUusxl2EAMbAnQRnz44Yc3btyo2/LHH38ImtxlB4CWstYe1fQ1eELT0m/3O8f2ke392eeNf7q9GF/5zV6/xXM5TBDgKbJ+/foTJ07Ub9+5c2eD648YMSIyMrJui1Qq7dGjR4MrA0Dr2GaBVxw+S6trnOKiy1d/q/jvaWH7dto74uqc35z64H8QAPObMmXK+fPn5XL5nDlzmrP+oEGDTFouXrzo4+PDQmoATy/rLfBN3WRHK1RCP29F5lG+yLlqZ4ZdxwD70CBDZVUTGzRUyviebixkCmD7nJ2d33zzzR9++GHYsGGWzgUA/mS9Bb6pI3i3CfFuE+IJIYxOX/TGMreXnneI+HMIDs2te3YdAyihsO766su/l36+KeA/iwTtvFhNG8BWRUZGmpx1BwDLss2BbmpRQoHnqy+Vrdqie1BCCNGXS0sW/0e2/79112EMhsqt+xx6dJFuP/DYDVZfytdJSludNQAAADestcAzOj0lbNbpB6c+PTymvPDwkzX6h+XS7ftdBveTZ5/Ql0trV1BkHhf4+7R7d4bmdqE6r6CJTRmqFOX/2VaxvuH7hlhVfT7v4cerMTYfWIUhQ4ZYOgUAsN4C3/Rd9H/lMjjabUJ88ftf1Fy94fnP8aLn/ibdvt/4I4NMITvwX8+pL1JCoceUFyq37mUMdGPbkX5/0PXvsXSNRvVrrhk+Q7MxWl3ltn26knLlyYtcxgVoHYPBYOkUAIDNAk/TdG5ubnZ29k8//XT58mWabrRwtsJjR7Iz4To8lu/izHNyLFm2RuDnU3PzXs21W4SQqp0Zzn/rJ2zvSwhxHtCbL3JRHj3b4Ba0dx+oc6+5JYz0nDZBum0fo9Ga5YM0hyz9sH3nDj6p/5TuSKfVNZzFBWid5ORkS6cAAKzdZMf29JGMXk9aMiyG4shZysW5/bJU1Zkc5alLtEzxcOV6536RylOX7DsGPVz2f0QgoPg8RqOt2pnhMiTG5C48wjCVW3Z7JI3mOTk6hIfad+ko++moe8JIs3yWphkqqxRZJ/z/9Y6gnZdjZFfZgf96JI3hIC5Aq82YMcPSKQAAawW+pdNHJiQk5Ob+5bx3UVFRr169Gt46w/CEQorfgtMPsj2HCCGSBV8YXwoCfHX3HtAMcejWhVZV6ySljEZDa3TG43Lx9PcJoYxr8lycCCFMjYZWqfWVsqq9PxNCGAOtOp+nOHy2RTm0jkGmoPj8kqWrCSGEpvUVVcoTFyh+C85eQBvn88ZU+7BOls4CAGwNWwW+pdNHrlu3TqFQ1G1Zvnx5SEhIw2tTVOCGT1qUj9+SN+jqapOtCIPbN3ien1b9b03mz+Wyr77V3Lijf1hedzWXQX1dhw9sURotpb0rLk3b4jZqBGVnZ2ypvpDHF7l4Tp/IalzgksDH09IpAIANYqvAt3T6SB8fH5NxrLy8vOz+V9Xq4zk5tigfgZ9381euO/Wc8Qjef/lbLQpnLgZltevQgQa5srbFLiRI4OMp8G3BxwEAgKcQWwUe00eahX1osH1osKWzAAAA68PiSHZPPn1kdnZ2RUVFgz9KT093d3d/ko23lEKhEAgEjo4tO3PwJGiarqys9Pbm9GBdJpM5ODjY29tzFlGv18vlck9PTk9TV1ZWikQiLqcv02g0IpGof//+zX+LyUUrm2fx/s4wTHl5OQdD4jMMU1FRwUHX5uz/EM56sU6nUyqVHh4ebAfSarXV1dUcfOs0Go2Hh0d0dDQb/b3tDlWbmJjo5dXwwLE0Td+4cWPkSC5uYq+/dH77AAAgAElEQVRVUlLi6OjYvj13c86q1er8/PwuXbpwFpEQcu/ePX9/fw76Ty2pVFpWVlb3fkwOFBQUeHl5cbmPWFxcLBaLn3vuuea/xcPDY+XKleyl1Ka0hf6u0+lycnLCwsLYDlRTU5OXl8dB11YqlcXFxRwEkslkDx8+5KAXV1RUlJeXN3p7lvk8fPiwsrKyUyfW734tKioSi8UjRoxgpb8zVkiv1/P5fI6DpqampqWlcRnx/v37wcHBXEZkGCYhIWH37t1cRjxz5szAgQO5jMgwTP/+/X/99VcuI/74448TJ07kMqLN4Ky/V1VVubm5cRBILBYHBgZyEOj69evh4eEcBLpw4UJ0dDQHgY4cOTJ06FAOAqWnp48dO5aDQDt27Jg8eTJLG7fWkewAAACgCSjwAAAANggFHgAAwAahwAMAANigtnsXfRN4PN6YMVyPx96tWzcub6EnhLi5uXE/7WZUVFSHDh24jOjn59evXz8uIxJCYmJi/Pz8uIzYoUOHRodehiZx1t8dHR1HjBjBQSCRSMRN1/by8oqLi+MgkK+vb4seAW21gICAvn37chAoODi4d+/eHATq2LFjZGQkSxunGEwxDgAAYHNwih4AAMAGocADAADYIBR4AAAAG4QCDwAAYINQ4AEAAGwQCjwAAIANQoEHAACwQdZX4NeuXdutW7fIyMhjx46xHWvZsmXdunULCgqqncWPm+iff/55VlYWZxF//vnn6OjoDh06bNiwgZugCxYs6NSpU2ho6I4dOziIqFKp1q9fX/uyfiyzRzeJaKkvkg1g+xfF8Z+Gg67NWXfmoBdz1nM567AmgYxY/FawNEsdSwoLC8PDw1Uq1a1bt7p27WowGNiLdfjw4WeeeUatVpeVlQUHB+fk5HAT/fTp03Z2dlu3bmU4+bxSqbRLly5lZWUymaxr164ymYztoCdOnIiKiqqpqSkuLnZzc1MqlaxGvHr16rRp0xITE40v68cye3STiJb6ItkAtn9RHP9pOOjanHVnDnoxZz2Xsw5rEsiI1W+FlR3BZ2dnjxs3zsnJqXPnzn5+fnl5eezFqqiomDVrloODg7e3d1xcnFgs5iC6XC5fuHBhYmKi8SUHEY3THnt7e4tEovz8fFdXV7aD8vl8Ozs7oVDo4OAgEAgIyx9z586dVVVVtS/rxzJ7dJOIFvki2Qa2f1Fc/mm46dqcdWcOejFnPZezDmsSiLD/rbCyAl9cXBwYGGhcDgwMLCkpYS/WxIkTZ86cSQjJz88/e/bskCFDOIg+d+7cxYsXe3p6Gl9yEFEsFt+7d69nz57BwcFffPEFRVFsB42NjQ0LC2vfvn1QUNCSJUucnZ1ZjbhixYo5c+bUvqwfy+zRTSJa5ItkG9j+RXH5p+Gma3PWnTnoxZz1XM46rEkgwv63wsoKPE3TFEXVvtTr9ayGYxgmLS1twoQJ+/btc3NzYzv6jz/+6OnpOWzYsNoWDj6vWq2+devWyZMnr1y5snHjxpycHLaDHj169NatW8ePH8/KykpLS5NIJFz+WevH4iA6x18km2EzfxrOujZn3Zn7Xsxlz7WZb4WVzSbXvn37wsJC47JEImF1ejeDwfDSSy+5u7tfunRJJBJxEH3r1q23b98+fPhwcXHx7t27NRoNB5/Xx8cnPj7ew8ODEDJo0KCCggK2g2ZlZb388svh4eHh4eH9+/c/efIkl3/W+rHYjs79F8lm2MyfhrOuzVl35r4Xc9Zzbepb8YTX8Dl2//79Hj16aDQasVjcuXNnVu9O+v777ydNmmSR6KmpqcZ7LjiIeP369e7du1dUVJSWlgYHB1+7do3toBs2bHj++edr72HJzc1lO+Lhw4drb2ypH4uN6HUjWvCLZO3Y/kVx/6dhu2tz1p256cWc9VzOOmzdQLXY+1ZY2RF8cHDw66+/bpzheNOmTTwei5cYTp06lZ2d7e/vb3y5cePG0aNHcxbdiIPPGx4ePnPmzJiYGIZh3n///YiICEIIq0GnTZuWm5vbrVs3Qsg777wTFRXFdsS66v9K2f4lt4UvkpWy4T8NSx+Ns+7MfS/mrOfa0rcC88EDAADYIBw6AAAA2CAUeAAAABuEAg8AAGCDUOABAABsEAo8AACADUKBBwAAsEEo8AAAADYIBR4AAMAGocADAADYIBR4AAAAG4QCDwAAYINQ4AEAAGwQCjwAAIANQoEHAACwQSjwAAAANggFHgAAwAahwAMAANggFHgAAAAbhAIPAABgg1Dg25Y7d+688MILU6dO/frrry2dCwAAWDGKYRhL5wCPvPvuu0lJSb179x48ePDJkyctnQ4AAFgrgaUTgL9YunSpg4PDgwcPvL29LZ0LAABYMZyib1scHR337NmTkpKydu1aVgOdOHHixRdf5PKNMpmsFe+qTyqV3rt3zyybAgCwYSjwbUtmZua5c+f279/v5+dn6VzMSS6XDx48uPblihUrOnTo0KlTp7S0NJM1c3Nz+/fv3759++nTpxNC7t+/P2DAgPDw8K+++sq4wqJFizQaDWeZAwBYKRT4tmX//v03btyYOHHilClTmvkWnU7X4HLTa3JpxYoVMTExcrnc+PKnn37auXPnuXPnzpw5s23btjNnztSuWVNTM378+I0bN969e7ewsPDQoUMrV65csmTJ77//vm3bNrlcfvPmTUJI165dLfJBAACsCAq8Jb399tthYWEXLlwghOzZsyciImLdunUZGRm7d+/evn27ycqrV68OCQnp0qXL22+/TdP0mTNnJk+enJSU9PHHH9ddJoQsX748NDQ0JCTk3XffNVmzfg4mmx05cuSBAweMP4qOjj558qTJCq34mPHx8StWrKh9eebMmRdeeMHf3799+/aTJk3au3dv7Y8yMjL69evXs2dPe3v7n376aciQIW5ubteuXROLxTU1NUKhcPny5R988EErcgAAeOowYFF79+6Nj4+vrq7u0KFDdnZ2Y6sdP368V69epaWlSqUyMTHxk08+OX36tJubW0FBAcMwdZcPHTrUtWtXmUxWU1MTFxf37bff1v1p3Q2+8MIL9Tf7zTffTJkyhWGY27dvh4SEHDt2zGQF4xvrbuq5557rUM/Vq1frrvPgwYOOHTsal7dv3x4dHV1ZWVleXh4TE5OYmFi72meffTZu3LhnnnnG19c3KSlJqVTKZLJZs2YNHz78l19+uXjx4sKFC5/0Nw4A8HTAXfQW9uKLLy5YsGD27NmRkZEjR45sbLUjR47IZLKJEycSQsrKyhQKxZAhQ6KiompPVtcuHz16NCEhQSQSEUKSkpKOHj0aEhJSd82mN/vdd98tWLBAp9P9+OOPU6dOPXr0qMkKcXFxJhvJyspq0Uf+xz/+cfHixW7dunl4ePTt29fOzq72RyqV6sKFC6dPn27Xrl1ycvJnn322dOnS9evXG3/60ksvbdy48aefflqzZk3Pnj0/+eQTe3v7FoUGAHh6oMBbGEVR06dP//DDD3///fcmVnNxcZk1a9aCBQsIITU1NVqtNj8/39XVtXaF2mWGYSiKMi7z+XyDwVD3p4/drEgk6t+//7Fjx3bt2rVv377du3ebrJCbm2uykfj4+PrJZ2VlRUZGNhiUYZgvv/zyP//5DyFk8eLFAsGjL6Gfn9+IESNCQkIIIRMmTNizZ0/tj3755Zd+/foZDIYlS5bs379/06ZNa9aseeutt5r4pQEAPM1wDd7ycnNzXV1dg4KCmlhnxIgR27ZtKysr02g0EyZMqHtjmomhQ4fu3btXqVRqtdrvvvtu6NChLd1sYmLiypUrPTw8OnXq1Jy4P//884N6GqvuhJCbN2/26tVLpVKVlpbu2LFj8uTJDMPcuHFDr9ePHj368OHDd+7cUSqVP/zww8CBA41vYRhmzZo1c+fOValUHh4eQUFBPXv2rKioaOKjAQA85VDgLezMmTMnTpwYOHBg3aPV+qKiolJTUwcMGBAaGhoaGvrcc881tubIkSP/8Y9/9O7dOyIiol+/fsnJyS3d7JgxYy5cuDB16tQWxW2+iIiISZMmhYWFjRgxYtWqVSEhIVqtNjw8vKioKDAwcNWqVfHx8REREUFBQW+88YbxLbt27Ro7dqyDg0NwcHBcXFz37t1Xr149b968J08GAMBWYahaS6Jpul+/fikpKR07dpw/f/65c+fqnq8GAABoNZQTS9q6dStFUVOnTqUoKiIiolOnTidPnuzUqZOl8wIAAKuHI3gAAAAbhGvwAAAANggFHgAAwAahwAMAANggFHgAAAAbhAIPAABgg1DgAQAAbFDbfQ5+7dq1X375paWzALCksLCw7OxsS2fBBfR3ALP397Zb4O/duzdx4sQZM2Y0vZqClv1Xmz7eoakBWQGsUUlJyaRJkyydRXPJZDKRSERRVE5OzvXr1/v06RMREdH8tzezvwPYKjb6e9st8IQQT09P48RiTZDUiKvuVzx2NQCrU3ci3TZuw4YNq1atys/P//zzzzdt2jRo0KAPPvhg0aJFLSrYzenvALaKjf7epgs8AFiF5cuX5+fnC4XCdevW5eXleXp6lpWVDRw4sLEC//777//xxx91W86ePdunT5933nmHk3wBngrWWuBvq27kys8TQpR6uVwv21P8LSGEIrzn24135DtZOjuAp4uPj49WqyWEuLu783g8Qgifz29i/dGjRxcVFdVtyc3NVavVrCYJ8LSx1gJvz3dw4rsQQvSMgUco4zKfx1PpFSjwABxbvnz5wIEDx44dGxYWNmTIkBEjRmRmZs6dO7ex9QcOHGjS8sUXX1jRJQkAq2CtBT7QoUOgQwdCiKRG/Jvi8vPtxhNCcmXnd5d8O7sDzvLBX0ilUl9fX51OZ+lEHnFxcZHJZMaDXRsQHx9/4cKF9PR0d3f38PBwHx+fvXv3hoeHWzovgKeatRb4BtGEphna0llAm1NTU+Pt7S2RSCydyCNCoZCmaZsp8IQQd3f3qVOnWjoLAHjE6v9/cRGIuji34GkcAACAp4HVH8GLBG5OfJd5vyc78hw1dI2G1rx3fRYhxJHvtDhsFUUoSycIAABgAVZf4AkhES49DpdnLOy8okCZnyu7OCVwFiHEjrJDdQcAgKeWLRR4La3V0dpKbblI4G7Ps/ex87V0RgAAABZm9dfgCSFKg4Ii1G+KXEsnAgAA0FbYwhG8Uq8Id+n5myJ3ZLsXHPnOlk4HAADA8mzkCL67ay+FXq6n9d1cIi2dDgAAgOXZQoFX6RWuArfurr1wlh6exM9l6eXa0ta999tvv+30P4GBgRRFFRYWLlu2rFu3bkFBQStXrjRvqkAIafUfC+ApYQsFXmlQOPNde7j2RoGHJ3FVnlOmLWnde5OTk+/+z8SJEydMmHDr1q0DBw5cvnw5Nzd3/fr1ly9fNm+2TzmaMSy6kWLpLADaNJso8Hq5ltHcUl2/rrxqYAyWTgeeahkZGQcOHNi0aVNFRcWsWbMcHBy8vb3j4uLEYrGlU7MpDCHo7ABNs4Wb7FQGpYHRS2oeeNu1K1Tf6eTUxdIZQRulpbVXFZcYhqltYQhzr/oP4wjHDzXFx8qz82SXCCFedj7uQs+67/Wx9+voGNr09ouKimbMmLF//353d/eJEycaG/Pz88+ePbt27VozfxgAgCbZQoFX6uVOfGdCSDs7/3JtKQo8NKbaoMyp+pUmjyYsYAh9t/o2Q2hCiEIvv1H9u5CyI4R4Cr09hF5139vVuXvTBd5gMPzjH/946623+vfv/+fGGebf//73unXr9u3b5+bmZv7P01Zt2LBh5syZbGz5pur3zYVfGZcNjME4bCUh5LUO89HxAUywWOBpms7LyyspKdHr9QEBAVFRUWafWuMPVcEN1e9KgzxPfqlSV84Q5oz0WKWufLj3aD7V1HTU8HRyF3rO6vB2Yz/9/PZHo30TIlr7IMbSpUsdHR3nz59vfGkwGF566SV3d/dLly6JRKLWbdNaZGVllZWV1b78+OOP7e3tCSFmn37Gzz5wTsf3nPjOBsbw4Y2580M/JoTwKb7J3hgAEPYK/LFjx2bPnu3v7x8QEEAIkUgkYrF48+bNgwcPNmMUmhgUOhmPEmhpDc0YBJRArqvS0hqGMI9/M4D5HDt2bMuWLZcvX6aoPwdI3rVrl52d3ZYtWyybGDd+//33RYsWJSYmenp6EkJqamquXLlCGi/wd+/eraysrNuiUqk8PDweG+hoeZaQJxzVLsF4AR7DVgI0ga0CP2/evMzMzNDQR6c0xWJxQkLC+fPnzRglzLm7h9D7svz8EK/4Sm3Fs94jz0tPjfGdaMYQ8PRw4js78pxa997PP/9cqVTGxsbWtnTt2vXUqVP+/v7Glxs3bhw9erQZsmyT3nnnnYEDBy5cuDA5OXnYsGGHDx9OS0trYv2PP/44Pz+/bsu9e/ecnB7/y2cIgymhAZqJrQJvMBhq/2sz8vX1rXtzk7ko9XIXgatx2UvoU6krN3sIeEq83vG9Vr83KyvLjJlYo9jY2IyMjLlz52ZkZGg0mqZX/uabb0xaYmJifHx8mh+OT/FfDXqjxVkCPE3YKvApKSl9+vQZN26ccdAPiURy8ODBlBTzP7eqNChc+K4igXuAQ5Cn0LtSiwIPYBkikWjbtm0//PBDQUGBebf877uf3FYVEEJ0jJZhyC9lBwkhES6R/T3MeckPwMawVeDnzJkzZsyY7OxsiURCCAkODs7KygoKCmps/a+//vru3bt1WzIzM/v06fPYQCq9wkXg6mcfMCXwNYYwKoNSz+gElPDJPwIAtEJiYmJiYqJ5tzmnwzs6RkcIySzdwyeCke1eIIQYn3cAgMaweBe9n59fcnKyg4ODTCa7ePGiTqdrYmWRSGRyi41cLi8sLHxsFOMRvHGZIpS70FOqq8StNwC2xI5nb0fsCSFCyk5ACZz5LpbOCMAKsFXgd+zYMXv2bJFI9NZbb23YsKFz586XL1/+5JNPXn311QbXT0pKMmnZt2+fi8vju7FSr3D+3zV4QojxLD0KPICtoghl6RQArANbBf7DDz/My8vr0KFDTEzM/PnzZ8yYUV5ePmDAgMYKfKspDQp/+4Dal5523pW6sibWBwDr1d99MEXZwgDbABxgq8ALBIKQkBBCyD//+c/hw4cTQtzd3dkIpNIrXJwfjSLiKfSuwI30ADbK3yHQ0ikAWA229oUHDx48bty4M2fOpKSkdOrU6dq1a6+88srAgQPNHqjuNXjyv1P0Zo8CAABgXdg6gt+4ceP+/ftp+s8hKe7cudO3b9/XXnvN7IFMr8HbeV9V5Jg9Clg1iqKkUmnt7C9tAU3TtWPeAQCwga0Cz+PxJkyYUPuSvTG8lAZ53SN4L6EPjuDBhK+v7+7du9VqtaUTeWT69Ol8PqZLAAAWWf1sckq9wuWvR/BSXIOHv6IoyoaHiQUAaJB134+qZ/QGRm/Pc6htcea76Bl9Dd2GjtUAAAC4Z90FXqVXOPNdTZ6L9RB6SbUVlkoJAACgLbDuAq80/OX8vJGXHaacAQCAp52VF3i9wplvWuA9hd4o8AAA8JSz8gJvkNc/gsej8ADcy8nJSU9Pl8lktS0ZGRkWzKfVqnSVvylyLZ0FgBlY9130Sr2i/rQTnnbef6jMPFslgI2ZPXt2g+3r1q1rxdZWrly5fv36uLi41NTUffv29e7dmxAyY8aM4uLiJ8rSEu5U3zorPdrDtbelEwF4Ug0U+CtXrly5cmXy5MklJSVNTPDaFqgMCleByKTRU+hdgeHoAZpk3ucG16xZk5eX5+XllZ+fP2HChJycHFdX01Nrdc2bN+/69et1WwoKCjDyD4B5mRb4rVu3rly5Uq1WJyQkDBo0aNGiRdOnT7dIZs2h1CtEAjeTRk87nKIHeIxRo0aZtNA0vWjRovrtzeHq6urs7EwI6dmz5+zZs1NTUzdv3tzE+tOnTy8tLa3bkpKSIhKZ7qxzSW2oVhoUhBC5vkpDa8q0DwkhAkrgIfSyYFYAT8K0wKelpZ07dy45OdnFxeXKlSt9+/ZtywW+2qDytW9v0ugp9Jbq8JgcwON99tln//73v6VSqa+vb2lp6bPPPtu67SQlJcXGxs6aNWvmzJmpqanjx49PTEysrq5ubP3IyEiTFjc3Nzs7u9ZFN4sj5ZmnKg8TQjS0RkOrv7j9ESHEme/yYdgXmKAWrJRpgVepVA4Of44b4+TkJBQKOU+pBbSM1p5nb9Joz3MQUAKTEe4AoL7/+7//u379+vvvvz9nzhy5XP7NN9+0bjsfffRRbGxsRUUFIYSiqD179uzatcvDw8OsybJrtO9Lo31fIoRclp0/Kz2a0nGhpTMCeFKmd9FPnjx57Nixd+/e3bJly/DhwydPnmyRtJpJR2uFvAb2+jErPEBz2Nvbi0Si3r17nz9/fsCAAXl5ea3e1LBhw2qn8+Hz+UlJSa27Xw8AzMX0CH7p0qWZmZmnT58uKipasWJFXFycRdJqJh2jFVINFHgvoU+lrjzYMYT7lACsyODBg6dNm7Zw4cKXXnrp4cOHbm6md7Q8hfgUn7Ly54cBjEwLfEVFRf/+/fv371/70sur7d5joqW1do0dweM+O4DH2bRp082bN8PCwlatWpWTk9PqU/TWQsdoKcITUE09HtzTtXcnpy6cpQTAHtMv+siRI40LMpns1q1bvXr1unLlCudZNVejp+gxmB1AM1RWVnp7e1dUVERFRUVFRVk6Hdb99HC3SOA23LupRwR5FL/+szkA1si0wF+8eLF2OT8/Pzk5mdt8WqaxU/SeQm+x+h7n6QBYGevaoX9yekavp3WWzgKAI02equrZU6drfWegaTovL6+kpESv1wcEBERFRfF4Zr6y1fgpesw3A/B41rVDDwAtYlrg33zzzdrlgoKCrl27tm67x44dmz17tr+/f0BAACFEIpGIxeLNmzcPHjy41bnW18QRPAo8QIs84Q59W7btwVrjwDUPNcUCiv+b8gohpINjyEv+Uy2dGgCLTAt83dvmR40a1eqBL+bNm5eZmRkaGlrbIhaLExISzp8/37oNNqixa/AeQi+ZropmaB6Fu2EBGmWuHfo2brj3aJlOSgg5UfmLE8852j2WEOJl52PpvADY9ajAG0tvYGBg3R9funQpJiamFds1GAz+/v51W3x9fRmGaVWSjWrsFD2f4rsIXGV6KYaZBGiCuXbo27gAh+AAh2BCyG/KKyK+qJtrL0tnBMCFRwX+3//+NyFEIpGcOnUqMjLSxcXl/PnzL7744q5du1qx3ZSUlD59+owbNy4wMJCiKIlEcvDgwZSUFLMlTgghRNvIKXryv0ljUeABGmTeHXpotQMlO0OcukSK+lo6EbBBjwr8zp07CSF///vfr1+/bjxTd/fu3ZkzZ7Zuu3PmzBkzZkx2drZEIiGEBAcHZ2VlNTE33enTp01mlqysrGx6QiqaoQlh+BS/wZ962flU6spDiW2ecgR4QubdobcWPVx7O/IcLZ3FX0h1FTI9LhYAK0yvwd+/fz8sLMy43LFjR2N5bp3S0lJfX99JkybVDo+VkZHR2CSVhw4dunnzZt2WyspKH5+mvveN3WFnhEljAZpg3h16a9HNxXSSGwAbZlrgBwwYkJCQMHnyZIZhdu7cGR0d3brtrly5cv369XFxcampqfv27evduzchZMaMGSaH6bU++eQTk5aYmBhPT88mQjR2Ad7IU+htvG8WABpjxh16Dh6LbdqXd5ZMbP/PIIeOXAZttazSfdUGJSHkbvUfMn3lQ42EEBLk2CnGfZClUwPbYVrgN2zYsH79+u+//57H4/3tb39r9R79mjVr8vLyvLy88vPzJ0yYkJOT0/T59lbQ0pqmjuDtvG+ofjdvRAAbY64dem4ei21aDa3WGjSchXtCbgJ344KAEtjxHJz4LoQQV77IokmBrXlU4OfOnbts2bIPP/yQEGK8Af7mzZvz589fvXp1K7br6urq7OxMCOnZs+fs2bNTU1M3b95sppz/pGN0TR/B41F4gKaZa4e+pY/F5ufnP3z4lxNsUqnUYDAcPnzY+LJv377u7u6EkNLS0qtXr9au1kS7caH561u2vYu+h7FdUaZxdSADnx3aNvNEO2ftp06dMvtAFI8K/PDhwx0cHGqHrnxCSUlJsbGxs2bNmjlzZmpq6vjx4xMTE6urq82ycaPGHoI38rTzrtTiGjxAw8y7Q9/Sx2I3bNhQUFBQt+XBgwd2dnb/+te/jC/fe++94cOHE0JOnjz59ddfqwxKR54Tj+KZtNMMXawRM4SMeG0IE6XeV/LdrV/unfr+nEjgbhxP3mT92nBtp11SI3biO/d0faaN5IN2S7WvW7dOpVIRs6JMOmFBQcGZM2deffXVRYsWHT9+/NNPP231jLFHjhypqKgwThFtMBh27dpl/AzNfHtMTIyPj09GRkZjK9ypvvl90aZFXT5r8KcMYV67Oml1jx1NHOUDtGUPHjwYMGCAWCxmY+Pp6enx8fFHjhwxaR81alQrtrZ27drVq1fXfyx21qxZzdxC0/192a13Xg6YWX+SN4YwefJLOlpLCNlX8l2sx7O+9u0JIaHOXT2F3q34INw7XJ4Z5NCxq0t3SycCFsZGfze9C+aNN95wcXG5cOHC6dOnP/300+XLl7d608OGDTNWd0IIn89PSkpqfnVvjqZvsqMI5W3XrkxbYsaIADZj3LhxDg4OoaGhJSUlo0aN+vXXX1euXNnq+eDnzJnzyy+/hISElJeXl5WVGR+LbX51fyyZXnpeeqp+O0WoKFF0tHtstHusi8A1wiXSuGwt1Z0QMtx7FKo7sMT0JruSkpJJkyYtWLDg5ZdfjouLu3PnjkXSao6mH5MjhPja+z/UFBtHsAKA+t54441p06bV3aHPzs5u3aaCgoLM/pSd2lBNE5oQQtO0kparDEpCiCPPkdfI6BcAUJdpge/atevbb7+9a9euixcvpqWliURt965OLa1p4ho8IaSdvX+ptuGn8gCAtIeVJh4AACAASURBVO0d+mqD6v2C1w2MnhBSbai+ID2dJ7tECBnnl9jghO593QZ627fjOkuANsy0wK9Zs2b79u0HDhzw8/OrqKgwjobRNunopu6iJ4S0s/N/UHOPq3QArE9b3qF34jsP8Yov1jwghOTJL7kJ3Ts6diaE1NA1Da4f7zOO0/wA2jzTa/A+Pj5Dhw7Nz8/X6XSzZs3q3LmzRdJqDpVB+ZviShMrGE/Rc5YPgNVZs2ZNQEBAm92h7+4a1ddtYF+3gUJK6C30NS53dcYVa4BmMS3wW7dunTRp0kcffaTRaAYNGrRp0yaLpNUcNXS1lm5qXAtfe3/j+FAA0KA2vkPfxTnCeNOcHc++nb2fcbmLc4Sl8wKwDqYFPi0t7dy5c7169XJxcbly5cqnn35qkbSaQ8foSJPTz3rZtZMbqgoUv3GVEYCVsZYd+t6imNqh3wCgmUyvwatUKgcHB+Oyk5OTUCjkPKXHuFt962Tlf40Leka/7cFaY/tY30kmk8NShLKnHK8qL4W79rBAogBtnnGHPjk52bhD37dv3+nTp1s6qQb0csNsqgAtZlrgJ0+ePHbs2OLi4i1btmzdunXy5MkWSasJ7kJP4702ZZoSHo9nXOZTfCe+c/2VnfnOCr2C6xQBrETb36E36un6jKVTALA+pgV+6dKlmZmZp0+fLioqWrFiRauHsWOPh9Drb14jCCH31bfvVN8yLpu4r76t0isJIRTFK9E8uKbII4S4CT3wTDxAXW1/hx4AWs20wBNCRo0aVTtcpUKhMPsscOaipbWEohr80cmK/z7UFhNClHq5XF+VVbaPENLFOQIFHqCutr9DDwCt9qjAX758eeHChZWVlc8///zcuXMXLlxYWFhonODZgvk1gSFMiKPp2NRGUwJfMy6svfcvcc39+SFLOcwLwJpYyw49ALTUo7voX3nllQEDBqSlpUkkksjIyICAgIULF547d86CyTXNwOgHe/296XWc+C4KvYybfACsyOXLl+Pj46OjoxcvXlxeXj5jxoz4+PguXRreY26OnJyc9PR0mexRd2tipigA4MCjAi+VSpcsWRIXF/fpp5/q9folS5YMGTKkY8eOlsvtMbS0Vkg95p4gkdBdS9doaS03KQFYC/Pu0K9cuXL8+PG7du2KiorKzc01Ns6YMcN8+QJAiz06Rc/n/zl/g5eXl7NzA3ektzU6RmvHs296nRd8E3Oqfi3TluDqO0Bdxh16QkhERER6erpxudXWrFmTl5fn5eWVn58/YcKEnJwcnOoHsDjTgW6siI7WNj3ZDCGER/ExYC1AfebdoXd1dTVupGfPnrNnz05NTX3S/ADgiT06gr9//37tTrdKpapdVija6HPkOkb32FP0hBBf+/YlmgeExHCQEsDTKSkpKTY2dtasWTNnzkxNTR0/fnxiYmJ1dXVj67/66qt5eXl1WwoKCrp3xyDzAOb0qMCr1WrzbpqmaeNN+Hq9PiAgICoqiscz5wkDLa157Cl6QkiwY6cr8otmjAtgA8y7Q//RRx/FxsZWVFQQQiiK2rNnz65duzw8PBpb/4MPPpBKpXVbpk6d2sT6ANAKjwq8vf3ji2XzHTt2bPbs2f7+/gEBAYQQiUQiFos3b948ePBgc4Vozil6QkgHx9D0hz+aKyiAbTD7Dv2wYcNql/l8flJSUlJSUmMrh4SEmLQ4OzsLBA0MywEArcZWj5o3b15mZmZoaGhti1gsTkhIOH/+vLlCaJnH30VPCPF3CFToZdUGVYNj2QI8ncy7Q29iyJAhx48fZ2/7ANAcbN1kZzAY/P3967b4+voyTJOzv7WQjn78XfSEEIpQgQ4dCtV3zRgaAJpgMBgsnQIAsHYEn5KS0qdPn3HjxgUGBlIUJZFIDh48mJKSYsYQOkYrpB5/ip4Q0sEx5L76drgL5pQD4EJycrKlUwAA1gr8nDlzxowZk52dLZFICCHBwcFZWVlBQUGNrV9WVmZyd09NTQ1N042tzxBGR+uEvGZNfhXsGHJNebXZuQPAE8EQNwBtAYt3tQQFBc2cObOZK7/++us5OTl1WyQSiZ1dowfoekbHpwQUaXiyGRMdHEOzy/Y3MxMAAAAb0FZuW921a5dJS0xMjI+PT2Pr62idXTNuoTcKcAiq0JZp6Bp7nkPrUwQAALAebBX49evXnzhxon77zp07zbL9Zj4jZ8Sj+AEOwYXqu12cI8wSHQAAoI1jq8BPmTLl/Pnzcrl8zpw5bGxf2+w77Iw6OIbcV99BgQcAgKcEWwXe2dn5zTff/OGHH+oOf2FGOlrb/FP0hJBgx5A71TfZyAQAAKANYnGymcjIyBUrVrC08ZYewXd0DL1X/QdLyQAAl766u6KoptDSWQC0ddY6m1wrjuCrdJUynfTxqwJA26YyKKoNKktnAdDWWXGBb/5NdoQQHsWLcI38TZHLXkoAAABtR1t5TK6ltIzWriWn6AkhPV2f+U2RG+s5lKWUAIA9Wlqzq3irgTEQQkq1xdml+88KjxFCeomio0TRls4OoC2y1gLf0iN4QkhP0TM/Sr6hGZpHWet5C4CnFs3QbgIPkcCdEHJdme9vH9DO3p8Q4mvv/7i3AjylrLbAt/AmO0KIm8DD0877bvWtUOeuLGUFACy5q751Q/X7/JClhJCz0mNRbv3w1CtA06y1wGtbeJOdUU/XPvmKyyjwAJZ17tw5sVhct6WystLNza2JtzCEELNORwlg86y1wOvoFh/BE0J6uj6zq3jrC35JbKQEAM2Unp5++/btui2VlZXe3t7NfHsP195edo2OYw0ARlZb4JkWX4MnhHRxDi/VFMv1MpGgqWMFAGDVypUrTVpiYmK8vLzqr5n+8If0kh9qX07Le4EQ4sh3GtnuhaZD/PRwlzPfdaj3c0+cLIC1stoCT+sqdWUtfReP4vcU9blQdWq492g2sgIA8xrnmzjON5EQck15NevhnvmhHzfzjRq6hk9Z6/9vAGZhrfeTaxnNBekZPaNr6RuHej13uDyTIbiYBwAAtsxa93B1tLZ5c8Gb6uwc7sx3yZdfjhT1MXdSAMCWopp7zVltT/G3ZdqHhJAHNff4RHhffZsQEujQYYzvRFbTA2iDrK/AqwxKQki1QUUYojIoBZSQT/EdeI7N38JQr+eOlGeiwANYi2qD6mDJj805Px8p6msckVrP6Owpx95u/QghuCMPnk5WVuDvVt9adWcpIaSGrqaJYVFBCiEUIWROx3cjXCKbuZEYj0F7ireXaIr87ANYzBUAzIQhDCFUB8fQx64Z5tzNuHBffduJ7xLtHstyagBtl5UV+E5OXVb32EEI+ehGalHN/bTuWwWUsKUbEVDCQV7Dfyn7KTnwNWMLzdBXFTkY8BIAAGyGlRX4Wgaib9Ul+D+N8B770c03YlXPGge9Uehl2x6sjer2zWPfePDhj2HO3cNdejxBcABolivyi1/dXV770viMnIASLA5bFeAQ3PR7OztHOPAc2M0PoG2zsgJ/pvJoseYBIUSmq2IoZl/JdzzCcxd6Dfce1aLtuAhckwNf2yz+aklYWotGxHuokfjY+bYsaQBolShR9JZeBwghKoNywfXXjGfvmv9e1vICsA5W9pics8DFie/ixHchFOMrDHThi5z4Lm4C91ZsKkrUL9Cxw96S7SqDstqgYgijMiiNy2ZPGwAAgGNWdgQfJeoXJepHCDlcnhnq3Pn5duNbuoWfy9IHeAwxjmQX6Rr9zYOvTlb8l0/4aqZ6wfXXCCF8ir+w80pf+/Z133WodL/SoCCEFKrvVBtURTWFhJAuzt16ifqa5XMBQBOo1j0UC/B0s7ICX4tm9BTFb8UbL1SdDnPuZizwcZ7Phjp3+fL2khE+Y7PL9qc1fg3ejufgRBhCCJ8S2PMcnPguhBBMOwvADSe+8xudFlk6CwArY60FvtqgcuI5P/l2/O0D54d+/PntD2totZ7RNXZPfu2I1kU193u49h7gMeTJQ7fILdX1n8vSUzou4DguQBvR2Tnc0ikAWBlrPQZlCAl37dnslZk8+aWLVWcuVp1RGZTXFHnG5UpdOSHEz779G50WMQz94Y03rsovtc1RbLW0RkPXWDoLgEbJZDKGYQghOTk5O3bsuH79uqUzAnjasXgET9N0Xl5eSUmJXq8PCAiIiori8cy2P8EQRtDsmSRqDOpz0hM0oQkhKr3iuiq/sOYuIYRH8TzdvAkhwY6dFoetKtM83FuyfUfRhliPoZGiPsGOIfx6VwECHDpgVCwAExs2bFi1alV+fv7nn3++adOmQYMGffDBB4sWLZoxY4alUwN4elHGnW6zO3bs2OzZs/39/QMCAgghEolELBZv3rx58ODBzdxCTEyMj49PRkZGbYuGrtkqXmOs05eqzoS59DBeSu/rNrD541Utu/XOywEzOzl1aWyFQvWds9Lj1xRXy7QlgQ4dfO3bt7P3cxN4iARuTnwXe76DM9+FIpQj34kQwiM84wJLzkqP58kvEkLk+qrimqKuLt0JIXxKMC1obvP3b8BKPXjwYMCAAWKx2NKJPF6HDh3y8/NFIlFQUFBeXp6np2dZWdnAgQNv3brVzC3U7+8ATxU2+jtbRWLevHmZmZmhoY+GlhSLxQkJCefPn29w/ZEjR164cKFui0Kh6NPnL8PF2/Hs+3v8TUtrGMLkyM5FuET62wcQQkKcwsyYebBjSLBjCCFEbagW19wr1RSXaR/eqb6p0MurDSoNXVNtUNKErjGoCSE0odWG6uZvnCKUE78Ftw4whDYwNCGEYWg9o78qzzFu5E35P1v0oaAtm9fpgy7OEZbO4on4+PhotVpCiLu7u/FEHZ/fmntgAcCM2CrwBoPB39+/bouvr28TZwvS09Orq/9SKZcsWRIYGFi3hSJU7WNpR8ozI1x6tuK/RXuegx3PvjlrOvKdwpy71Q5tbRYMYVr3nH2BMv9IedbrHd8zYzLQFrB9Eogby5cvHzhw4NixY8PCwoYMGTJixIjMzMy5c+c2tn5ycvK1a9fqthQUFPTogQEiAcyJrQKfkpLSp0+fcePGBQYGUhQlkUgOHjyYkpLS2Pr29vb29vYmLWwk9lbIYgue3KYI5cx3acUbHXiOfIrfuvcCsC0+Pv7ChQvp6enu7u7h4eE+Pj579+4ND2/0vvfly5eXlpbWbVm1alXXrl3ZzxTgKcJWqZszZ86YMWOys7MlEgkhJDg4OCsrKygoyFzb7+M2oJ2dXyveaKWXrv3sA3qLYiydBUCj3N3dp06d2syVg4KCTP43CAgIcHRswaTPAPBYLFa7oKCgmTNnPskW9u7d+8cffzT4o4MHD3p4eDzJxltKqVTy+Xwu/w+iaVoqlXp5edVpe5vtoDKZzNHR0c6uBePzPyG9Xq9QKDj+a0qlUpFIxOV1Yq1W6+joOHDgwOa/RaWy1lGThwwZcvz48Za+y+L9nWGYiooKb29vDgJVVlb+tWuzgqbpqqoqT09PtgMZDAa5XM7B30iv1yuVSnf31gxP3iI6na66utrNzY3tQBqNxsXFpX///mz097Z7ODtjxozOnTs3+COapjdu3Dh8+HAu87l06ZKLi0sTZx3NTqVS/fLLLyNGjOAsIiHk5MmTAQEBwcGPmarLjMrKyi5fvmxyQyXbDh061KlTJx8f7p54vH//fkVFRUs/ZnJyMkv5sMpgMLT0LW2hv2u12v3798fHx7MdqLq6+tChQxx0bZlMduLECQ46V0VFxYULFzgIVFJS8ttvv3EQ6MGDB3/88QcHge7evSuXy42BzN/fGSuk1+v5fD7HQVNTU9PS0riMeP/+/eDgYC4jMgyTkJCwe/duLiOeOXNm4MCBXEZkGKZ///6//vorlxF//PHHiRMnchnRgjZs2GDGrXHW36uqqtzc3DgIJBaLAwMDOQh0/fr18PBwDgJduHAhOjqag0BHjhwZOnQoB4HS09PHjh3LQaAdO3ZMnjyZpY1b60h2ANBmYXwbgLYABR4AAMAGocADAADYIBR4AAAAG8RfsmSJpXNoMYqiKioqnnvuOS6DKpXK0NDQjh07chaRz+dXVVVx/LCAVCp95pln2rVrx1lEiqK0Wm1cXBxnEQkh5eXlgwcPdnV15SwiTdN2dnYcPyxgGzjr73w+v6KigoO76Pl8vlQq5aBr8/l8uVz+7LPPsh2Ix+PV1NQMGjSI7UAURRkMhgEDBrAdiGEYHo/Xr18/tgMZDAZHR8fevXuzsXG2JpsBAAAAC8IpegAAABuEAg8AAGCDUOABAABsEAo8AACADUKBBwAAsEEo8AAAADYIBR4AAMAGWV+BX7t2bbdu3SIjI48dO8Z2rGXLlnXr1i0oKGjlypVcRv/888+zsrI4i/jzzz9HR0d36NBhw4YN3ARdsGBBp06dQkNDd+zYwUFElUq1fv362pf1Y5k9uklES32RbADbvyiO/zQcdG3OujMHvZiznstZhzUJZMTit4KlWepYUlhYGB4erlKpbt261bVrV4PBwF6sw4cPP/PMM2q1uqysLDg4OCcnh5vop0+ftrOz27p1K8PJ55VKpV26dCkrK5PJZF27dpXJZGwHPXHiRFRUVE1NTXFxsZubm1KpZDXi1atXp02blpiYaHxZP5bZo5tEtNQXyQaw/Yvi+E/DQdfmrDtz0Is567mcdViTQEasfius7Ag+Ozt73LhxTk5OnTt39vPzy8vLYy9WRUXFrFmzHBwcvL294+LixGIxB9HlcvnChQsTExONLzmIaJz22NvbWyQS5efnu7q6sh2Uz+fb2dkJhUIHBweBQEBY/pg7d+6sqqqqfVk/ltmjm0S0yBfJNrD9i+LyT8NN1+asO3PQiznruZx1WJNAhP1vhZUV+OLi4sDAQONyYGBgSUkJe7EmTpw4c+ZMQkh+fv7Zs2eHDBnCQfS5c+cuXrzY09PT+JKDiGKx+N69ez179gwODv7iiy8oimI7aGxsbFhYWPv27YOCgpYsWeLs7MxqxBUrVsyZM6f2Zf1YZo9uEtEiXyTbwPYviss/DTddm7PuzEEv5qznctZhTQIR9r8VVlbgaZqmKKr2pV6vZzUcwzBpaWkTJkzYt2+fm5sb29F//PFHT0/PYcOG1bZw8HnVavWtW7dOnjx55cqVjRs35uTksB306NGjt27dOn78eFZWVlpamkQi4fLPWj8WB9E5/iLZDJv503DWtTnrztz3Yi57rs18KwRP+H6OtW/fvrCw0LgskUjat2/PXiyDwfDSSy+5u7tfunRJJBJxEH3r1q23b98+fPhwcXHx7t27NRoNB5/Xx8cnPj7ew8ODEDJo0KCCggK2g2ZlZb388svh4eHh4eH9+/c/efIkl3/W+rHYjs79F8lm2MyfhrOuzVl35r4Xc9Zzbepb8YTX8Dl2//79Hj16aDQasVjcuXNnVu9O+v777ydNmmSR6KmpqcZ7LjiIeP369e7du1dUVJSWlgYHB1+7do3toBs2bHj++edr72HJzc1lO+Lhw4drb2ypH4uN6HUjWvCLZO3Y/kVx/6dhu2tz1p256cWc9VzOOmzdQLXY+1ZY2RF8cHDw66+/bpw7fNOmTTwei5cYTp06lZ2d7e/vb3y5cePG0aNHcxbdiIPPGx4ePnPmzJiYGIZh3n///YiICEIIq0GnTZuWm5vbrVs3Qsg777wTFRXFdsS66v9K2f4lt4UvkpWy4T8NSx+Ns+7MfS/mrOfa0rcC88EDAADYIBw6AAAA2CAUeAAAABuEAg8AAGCDUOABAABsEAo8AACADUKBBwAAsEEo8AAAADYIBR4AAMAGocADAADYIBR4AAAAG4QCDwAAYINQ4AEAAGwQCjwAAIANQoEHAACwQSjwAAAANggFHgAAwAahwAMAANggFHgAAAAbhAIPAABgg1Dg25Y7d+688MILU6dO/frrry2dCwAAWDGKYRhL5wCPvPvuu0lJSb179x48ePDJkyctnQ4AAFgrgaUTgL9YunSpg4PDgwcPvL29LZ0LAABYMZyib1scHR337NmTkpKydu1aVgOdOHHixRdf5PKNMpmsFe+qTyqV3rt3zyybAgCwYSjwbUtmZua5c+f279/v5+dn6VzMSS6XDx48uPblxo0bw8PD/fz8pkyZolar66753nvvBQUFtW/ffv78+YSQ+/fvDxgwIDw8/KuvvjKusGjRIo1Gw2XyAADWCAW+bdm/f/+NGzcmTpw4ZcqUZr5Fp9M1uNz0mlxasWJFTEyMXC43vszJyVmxYsXx48dv3rxZWVn52Wef1a75yy+/HD169ObNm9euXcvIyPj5559Xrly5ZMmS33//fdu2bXK5/ObNm4SQrl27WuSDAABYERR4S3r77bfDwsIuXLhACNmzZ09ERMS6desyMjJ27969fft2k5VXr/7/9u47IIprawD4ne3Lsh0ERFBApQliF8Fe0ICaKFEUg8ZYPjUaY0yi0SR2TTThvWfI8xmjWKMksSL2iiY2BCX2AoggCsuyC9t3Z74/NkFcFSk7s4Xz+2t3GOacXeZypty5d62/v3+bNm0++eQTHMfPnz+fmJg4ZsyYJUuW1HyNEFq+fHlAQIC/v/9nn31msebLOVhsdvDgwXv37jX/qEuXLmfPnrVYoQEfMyYmZsWKFdVvHz16NGPGDE9PT4FAMHLkyPv371f/yNXVdenSpVwuVyQStWvXTq1WC4XCmzdvFhYWarVaJpO5fPnyBQsWNCAHAABocghgU7///ntMTIxarW7ZsuWhQ4det9rp06fbt2//7NmzqqqqhISEZcuWnTt3TigU3r59myCImq8PHz4cGBioUCi0Wm10dPSWLVtq/rTmBt9+++2XN7tp06b33nuPIIgHDx74+/ufOnXKYgXzL9bc1JAhQ1q+5Pr16zXXefz4catWrSw+lEwm69Kly549e17+Tt566624uDitVqtQKKZOnTpgwICjR49evnx5/vz59f6KAQCgSYJe9Db2zjvvzJs3b9q0aeHh4YMHD37daidOnFAoFKNGjUIIlZaWVlZW9unTJyIiovpidfXrkydPxsfHCwQChNCYMWNOnjzp7+9fc83aN7t9+/Z58+YZDIZdu3aNHz/+5MmTFitER0dbbCQjI6MBH3z79u2LFi1atGjR22+/bfGjyMhIHMeXLVt26tSpwYMHr1u3zrz83Xff/emnnw4cOJCSkhIWFrZs2TI2m92A0AAA0BRAgbcxDMMmTZr05Zdf3rhxo5bVXF1dp06dOm/ePISQVqvV6/W5ubl8Pr96herXBEFgGGZ+TafTTSZTzZ++cbMCgaB79+6nTp1KS0vbvXv3r7/+arFCdna2xUZiYmJeTj4jIyM8PPyVQXEcnzBhgkKhyMzMtOhLaO5dGBkZGR8f/+jRo3379lUf9Bw9erRr164mk2nRokV79uzZsGFDSkrKnDlzavnSAACgKYN78LaXnZ3N5/N9fHxqWWfQoEGbN28uLS3V6XQjR448f/7869bs16/f77//XlVVpdfrt2/f3q9fv/puNiEhYeXKlWKx2M/Pry5xjxw58vglr6vuCKF9+/YVFxfv3bu3uroTBHHnzh2j0VhUVLRgwQKlUqlUKjMyMoKDg6tXSElJmTlzpkqlEovFPj4+YWFhMpmslo8GAABNHBR4Gzt//vyZM2d69Ojx22+/1bJaRETE7NmzIyMjAwICAgIChgwZ8ro1Bw8ePHbs2A4dOgQHB3ft2jUpKam+mx06dOilS5fGjx9fr7h1l5mZmZmZKRAI+Hw+n8+fMGGCXq8PCgoqKiqaPn16eHh4eHh4aGhoUFDQ9OnTzb+SlpY2bNgwDofj6+sbHR0dGhq6du3aWbNmNT4ZAABwVjBUrS3hON61a9cPP/ywVatWc+fOvXDhAoMBN00AAABYAZQTW0pNTcUwbPz48RiGBQcH+/n5nT171s/Pz9Z5AQAAcHhwBg8AAAA4IbgHDwAAADghKPAAAACAE4ICDwAAADghKPAAAACAE4ICDwAAADghKPAAAACAM7LpVDe1Wb58ua2/GwBsrF27drZuiBSB9g6A1du7/Q50U1FR8e2333766adW2RpOoHyF0V/09+et1BNqA+7Bo1tl4wCQ4fHjx5GRkbbOgiLWbe8AOBwy2ntTuURfqcfH7pcdeqhFCFVo8bH7ZcfydbZOCgAAACCL/Z7BW5eQTdsUK5mQXq7Q4jtvqXt4s8aFutg6KQAAAIAsTeUMHiEULGX+MEj09TmFG5c2P1Jg63QAAAAAEjWVM3iEkEKHL/+jcmgbbuYj3f57mmFtuLbOCFDEZDKlpKRoNBpbJ/KcSCSaOnWqrbMAADizplLgNUZi3IHynj7sz7rxb8kM7x8s57NofVuybZ0XoMKzZ88WLFgwY8YMWyfy3BdffPHBBx/A7MAAAPI0lf8vGEKT2/PMZ+3BUubWOGmVAabRa0L4fP6qVatsncVz3333na1TAAA4uaZS4P8qM+QrTEodLmDTEEJtJE3lgwMAAGianL+TXXGVacZR+cfHKx4pjW/9Wnb6ETwdBwAAwPk5f4FfcEbRgk8/luC+pp/ou36irzMVe+7aUWcrAAAAdm7HTfWfRXrza6UO/ypTYXKEe7xOXuAfKU03ygxzuvI5DAwh1K05a8MQyfI/lPkKo61TAwAA4BjaShizjsvPP9YpdXhSejmLjtExW+dUB/ZS4Pv06YO9aPXq1bt3727kZrf+pRod7MKu8adoI2FM6+j66SnHOP4CAABgc509WetixLOPV4zcU9bZi7Wwh2OMpGIvBf706dMWo+R37dpVKpU2ZpsaI7HnriYh2HLEuvfDeBw6tj6nqjEbBwAA0HS0lTAlXFpxFd7X12Ger7aXAm91N8sMB+5rOnqwvPl0I47ulj+/Jk/D0Mo+wg3XVKVq3IYZAuCs1q9fb+sUALCmSj0xPl3W04e9JU7y8YmK848do7O20z4ttupC5S2ZYU0/kRFHM4/J+Szs276i6p+24NNHtuX+kFW5uKfQhkkCxDEElgAAIABJREFUO6HQ4UL23we7JhxpTQSPWb87bFu2bPn666/Nrw0GQ1FRUV5e3rJly86cOWMymZYuXZqYmGjlpO1JRkZGaWlp9dslS5aw2WyE0Pjx422XFABW890lZWdP1hc9BAihlEHij09UnElsZv+34Z23wPcR9t1RmnFf+8tNtd5E/LuXyGKF6R1dB+wsfT+c10rotF8CqAuNkRi4s3RNP1EvH7beREw/Kg9zZ37UmV+vjSQlJSUlJZlfz5kz59GjR1lZWc+ePbt7925eXl779u1HjRrFZDJJSN8u3LhxY+HChQkJCRKJBCGk1WpzcnLQ6wv8+PHjb9y4UXPJ/fv3O3fuDNPFAvu0KPr5qWAXL9a5cc1smEzdOW1tu1du7OzJOlmgZTOw4wnurJeOtUQc2sRw3veXKv8zUGyTDIGd4DKw/w0WTzksX95b+PttDYeOzehYv+peU3p6+t69e69evZqbm/v5559jGNayZUuBQGAymZy4wH/66ac9evSYP39+UlJS//79jx8/npycXMv6S5curXnGjxCaMGECi8UiOU0AmhanLfDXSw1P1aYQd6beSCw5r1zWS0h76XLK++G8/r+U3iwzhLg57X9eUFNRpWnWMbnxVQ9QiDjY9CNyLgPzEzFG7il7eYXevuw5Xd5Q+IuKiiZPnrxnzx6RSNSzZ0+EUEpKyqZNm+bMmcPhcKzxCexXVFRUenr6zJkz09PTdbo33KH09fX19fWtucTFxYVGc9ouQQDYhNMW+J231O4u9A1DxDojmnCw/NsLynkvTRHLZWAftOf9N7tqLZzENw2ervQlvYTESwXeiBPfXqzkMbACJf5OG24Xr1ecSrq5vKH8mEymsWPHzpkzp3v37tUL33rrLRaLtWbNmkmTJgmFTt7hQyAQbN68eefOnbdv37Z1LgAA5y3wOiPxr/5CJg1jstDmOMnVEv0rVxsb4rI+u+puubEtjE7fBNAxFPrS1RoTgf7vcLmEQ9saJ71eqp96WB4kFUd61/ty8eLFi7lc7ty5c81v09PT/f39Q0JCJk+evHfv3suXLw8YMMAKn8HuJSQkJCQk2DoLAICTPiZXpsEJArX8p/ccj4n19Hn1k4tcBjYhnPc/eCa+CTPiRIQH698DxHQa6uDB+mmIpEpf7+cnT506tXHjxi1btmDY37eCHjx4sHLlSqPRWFZWdvXqVT8/P2snDgAAtSHrtNVkMtHpdPPr7Ozsmzdvdu7cOTAwkKRwFnKfGcKb1fU5p/dCeX1/eZavMEJ3+qaJTcdmdHStftu+GROhevfJWL16dVVVVVRUVPWSjIyMrKys4OBggiCWLl0aEBBgnXQBAKBuyCppnp6e5l6yycnJa9as6dev34IFC5YsWVL9KBGpbpQZXr4S+zquLGxcqMtPOarlvZ38FikgT0ZGxssLt2zZQn0mAABgRvol+u++++7KlStbt269fPnysmXLyA5nlltqCGtWpwKvNhCVemJ8GO9wnvapylSqxmGMegAAAE6A9ALv5ubm5eVlflF90Z5suaWGMPc6FfizhbqRu8v0JvROW+7qi5Vxv5YWwERzAACncF9uNNboT3JbZrBdLsAGyCrwTCYzNDR0yJAhSqVy8+bNBEFMmjQpOjqapHA1lapxvYlo7lqng4nB/px3g13G7pOFuzP33tMs6CHwF8GdeACAM0jNVU0/IjfgBEJo1QXlwrNKW2cEKEVWMSsuLtZoNAUFBfn5+Z6engRB+Pv7z5kz53Xr5+XllZeX11yiUqnE4oY8nl7303ezye15T1WmuScVnTxZhZWmBkQEAAA7tLincM6JiqmH5UFSxvnH+q1xEltnVCclKhOfRaueD+JBhdFfxLD7cd/tEYlnqzdv3nz8+HGfPn3M43ssWLAgPT09Li7ulSsvXrz4r7/+qrkkPz/fxcVypte6eFhhbC2ux+e6WWZIv695O5B74bEu9bpqYjiPC/sSAMDx0TH0fX/RW2llWSXqo6PdRRzHeC764ANt+n3N5liJgE3be1fz7cXK4wnuLvWc/wkg8i7Rr1y5csSIEWlpaREREdnZ2eaFkydPft36qampV14UGhrarFlDBvQvqjS14Nf1Zv+dcuP7B8uX9RJ+20c4JtRFZyK25KoaEBQAAOzQvy5XMumohzd7/hmFzkG6EH8Qzov0ZiceKE+7rf72YmVqrASqe8OQdQafkpJy7do1qVSam5s7cuTIrKwsPr/hE3jUy+NKY1SLug5DZsSJ7/uLolqwEULTOrgaTGjzX+qJ7XnMl0euB47MZDI9fPjQ1lk8R7w8Xi4A1vafK1WnH+m2DZW4Mmmzjsk/Pl7xY4xjDMv9WTf+gwrjl2eVO4ZJYJjRBiPri+Pz+TweDyEUFhY2bdq02bNn//zzzyTFsvC4PmfwFo/Lz+rsevmJ/sA97YhALgmpAdvg8XhisXjgwIG2TuS50NBQmFsFkK2TJ3N8mIuQTUMI/Weg+GSB1tYZ1dXBB5rcZ4Z3ArmLzim3DpWI2NBYGoKsAj9mzJioqKipU6dOmTJl9uzZI0aMSEhIUKvVJIWrqajS5M1v+Oea1pG3KFP5dlsunMM7DYFAANOfgCbIfG3SjEFDg/wcY0rDI3na5X9UbomTBIgZy84r3z9Yvmu49OUpv8EbkVXgv/rqq6ioKJlMhhDCMOy3335LS0trWK/4eqnQ4gwaxmc1fFfo4c12ZWFH87SD/R2jMQBgD7Kysmp2qkUI1dKpFoBaSLm07UMlfiIGQmhhlGDfXQ0Gxb1BSLzu0b9//1GjRplf0+n0MWPG/Pe//yUp1qJzihV/KBFCjytNYg6t57ZneCNucc7o5JpytQrukQJQR/XtVNsYmYW6KYfkWiOBEFIbiQkHy6+8Zq5I4KA6e7L8/hmPBEPo7bZc6BTVME7SeeGTroIJ6bJlfyjFHFpxlWnjW5LG7A/9W3LWXqk6VaDr1/LVc9AB4OimTZv2yuUNOwqvb6fa/Px88+W9aiqVSiKp7SntrzMVCSEuwVJmjxbszbmqgTtL98e7fXhM7sNndPR4oVNt/B7Z+DCXoa25CKEdN9XH8rSbYh3j+W8ArOsVBT4nJycnJycxMbGkpMTHx4f6nBqAz8JS46Tv7inLU5gGtmI3YDJvC9M6uv77SmXflmw4bgROyboXz+vbqXbRokX1Hfci0ps98WD5xliJCUfXSw0BYkb/X0r7t+Ss6C20OJr/pq/wvQPlJhxpTcS67KptQ6UN/2AAODLLAp+amrpy5UqNRhMfH9+zZ8+FCxdOmjTJJpnV191yg0yLuzCwcm29J/N+2SA/zr8vV2YW6nq9ZiJ5ABxabGysxRIcxxcuXPjy8rqob6fa1NRUiyXdunVzd3evJcRgfw6GocT95TQMLekp2HZTLeLQnqpNehPBeXFkqgARY+tQSfweGR1Du0e4+QoomgIDAHtjeQ8+OTn5woUL7du3d3V1zcnJWbVqlU3Sqq/LT/TTjsj/M0DcwYP5VGUy349vDAyhGZ1c/3OlyirpAWCfvv322+bNm3O53FatWrm6ul67dq1h2/nqq6++/fZbkUiE/ulUO3z48LFjx1o1WeTDpxOIMODEumyVL59xZLS7lEubevjv+/E1XX6i5zAQnYZdhdvzoAmzLPAqlYrD+bv3uIuLC5NZj0Hdbeie3Lh2oDjSm1WiMn3TR6g1EY3pZGc2xJ+rNRJnHumskSAA9uiHH364devWxIkTDx48eOLECW9v7wZviuxOtTfLDBMzylf3FU1u7/qgwjg2xIVJQ2v6iVoJ6XfLX5gBcsdN9Y9Xq9KGu22Nk3xzQZl+X2PFNABwIJYFPjExcdiwYXl5eRs3bhwwYEBiYqJN0qqvsSEu3ZqzEEJFVaZAKWtJT8vbcg1Aw9DMzq7fX66E7vTAWbHZbIFA0KFDh4sXL0ZGRjb4DJ4Cqy9WLuslHNCK82En1zX9RClXqxBCdAwt7ikMb/bCeUih0rR9qNRHQG8rYWyJkz6sgBmkQBNleQ9+8eLFBw8ePHfuXFFR0YoVK6iZ4NVaKnQ4DWvUQ/AWBvlx1mVXHc/TDnSQASIAqJdevXpNnDhx/vz577777tOnT6sfYbdDNXvCD/HnDHn9MBWfd3/egb+NhNFG4kpuZgDYK8sCL5PJunfv3r179+q3UqnD9EGt1zQzdYEhNKsTf/VFZf9WHHgOEzifDRs23L17t23btt9//31WVtamTZtsnREAwGosC/zgwYPNLxQKxb1799q3b5+Tk0NBHgcPHiwqKqq55NmzZ+YHb+quqNLU3NXKPWb7tmT/mE07cF8zvA2MTg+cTXl5uZubm0wmi4iIiIiIsHU6AABrsizwly9frn6dm5ublJRETR43btx48OBBzSVqtVqvr18P2MJKkw8Jj8R8ESmYfVw+xJ8DgyEDJ2OrA3oAAAVqG8kuLCzMYDBQk8dnn31msSQnJ8f81E3dFVWafKx6id6sgwczSMrcdkM9Mbx+VxQAsHO2OqAHAFDAssB//PHH1a9v374dGBhIbT6NUlRpimze2DHsXunz7vzR+2QjArkwayFwVlQe0AMAKGBZ4Gt2m4+Nje3bty+1+TRKcZWpOQln8AghfxFjYCvO/7JVNTvoAuDoHPqAHgBQu+cF/uLFiwihFi1a1PzxlStXunXrRnVSDVWqNrm7kDUs5Zyu/CFppaOCuNXTHAHg6Bz6gB4AULvntepf//oXQqi4uDgzMzM8PNzV1fXixYvvvPNOWlqa7dKrB5xAFVpCyiHrEroblzYpnLfqQuX/BpM+qz0AZHOCA3oAQO2eF/hffvkFITRw4MBbt26Zr9Tl5eVNmTLFZqnVU7kWF7IxOmm3yIf/Xrakp+DX25qzhbpCpeluuWFxT/sdFQSA2jn6AT0A4I0srzYXFBS0bdvW/LpVq1bFxcWUp9RAZWqTG2nX5xFCH3fhTzkknxzh+vmpCiYd2w5zUAJH5ugH9ACAN7Is8JGRkfHx8YmJiQRB/PLLL126dLFJWg3wTI03cyGxi3sfX/Y3fUUfHZMbcDQhjEvGA/cAUMxxD+gBAG9kWeDXr1+/bt26HTt20Gi03r17O9ARfZkadyOzwCOESlQmNgOj4+iXW5oxITyo8cDRWfGAHsfxa9eulZSUGI1Gb2/viIgIGg2eKQXAlp63wJkzZ1ZUVMydO/f+/fteXl4eHh53796dO3duw7ZrMj2fwSk7O3v79u137txpbLK1IrULPUJo2w31f69W/T7C7Zs+QiNOzDlRQV4sAKixfv36Xr167dixY9euXb179/7f//7XsO2cOnUqJCRkzpw527dv37Vr19y5cwMDA8+ePWvdbAEA9fL8DH7AgAEcDqd66MpG8vT0LC0tRQglJyevWbOmX79+CxYsWLJkCXlDZZVpcE9rD0RfU2ahbtswqQ+f7sOnr+knWnhWceihtpYprQCwZzNnzly6dOmXX36JEPLy8kIImQ/o165d24CtzZo16+DBgwEBAdVLCgsL4+PjzX31X/bjjz8+evSo5pIHDx4UFxfPmzfP/Hb06NEdOnRACGVmZh48eLB6NVgOy511+c6dO6uqqpBVPS/ww4cPRwgFBAScP3/+gw8+WLhw4enTp1etWtXIAN99992VK1e8vLxKS0ujoqLIK/ClatxiWmjrqvl0XIwfx4NHn3qovKsXS8qF65DA8Vj3gN5kMpmPEqp5eHgQBPG69Zs1a1ZZWVlzCZPJZLPZYvHfrax64lqxWFy9EJbDcideLhQK6XRrn6MSLxo0aNDOnTsvXrzYu3fvzMzMwYMHEw3i5uZmftG+fXvzCxzHg4KC6r6Frl27xsbG1n39sfvL/nysq/v6jbfiD8WsY3IqI4ImpbCwsEWLFqSGuHXr1oYNGwiCWLBgQVRUVGZmZsO2k5KSEhQU9Pnnn69du/aHH3744osv2rVrt27durpvob7t3Ql8ckJeqDSaX59/rP335Urb5gNsi4z2bnn2WVJSMnr06N27d48bNy46Ovrhw4cNO25gMpmhoaFDhgxRKpWbN28mCGLSpEk1h82yOgo62Vn4pCv/tsxw+KGWyqAAWNFHH33k6up66dKlc+fOrVq1avny5Q3bzvTp048ePerv719WVlZaWurr65uRkTF16lTrZutk2jdjJe6XPa40nX+s++h4RaQ3KfNogKbMshd9YGDgJ598kpaWdvny5eTkZIFA0LDtFhcXazSagoKC/Px8T09PgiD8/f3nzJnzuvW/+eYbi4OJ/Px8BqMeg8JSX+BZdGxFb+HMYxWdvVhucKEeOCDzAf28efMaeUCPEPLx8XGgh25eKV9hbOZCd2H+PSv0zTJDiBuJd/3ea+fCpKORe8pMOEoZJO7iBQUeWJllBU1JSdm6devevXs9PT1lMpl5NIyG4XK5QUFBQUFB5rcLFiyoZeXw8PCadyYQQseOHeNyuXWMpTcRGiMhpHyqt06erPgg7qcnKzbGSmCueOBwrHVA7xzS72tPFWhT46R8Fva/7Kpfb2uOJLjTyWzY/iKGxkhwGVhzMjsIgybLssC7u7v369cvJycnPDx86tSpPj4+1OQxZMgQiyU///wzh1PXPuqlatzNhWaTEvtRJ/7ofWXb/lK/187FFvEBaDgrHtA7gRmdXGUafNwB2cBWnD13NTuGSUit7pee6D88Kv9psCRPYUw8INs+VApDawDrsizwqampK1eu1Gg08fHxPXv2XLhw4aRJkxqw3XXr1p05c+bl5ST9BynT4La6SE6noe/6ieL3yro2ZwVKYKI54EhsdUBvnzCEvooWjN4rS7ladSBe6sEjt9yu/FNpvjLfrTnLiKOUq1Wr+sD0FsCaLAtScnLyhQsXkpKSXF1dc3JyOnfu3LAC/9577128eFGpVE6fPt0aeb5WSlaVXId3a85yd6HflxtnHJXvHuHGY1J6Mt9SyPg6SjD9iHzvSDc+Cy7VA4dhrQN6+3ev3NhawjA3ThOB8iqMrcWvOBz/+ZpKpsGHteZ+clKxJU5C6l2/PSPcql+PC4Xrf8D6LHdflUpVfWHcxcWFyWxgHxMej/fxxx8HBgb2f1Gjkn2VpDBezlP9z9dUbAYan17+fx1cKa7uZnGtuT28WXNPVrz2yV8A7I/5gL59+/bmA/rGj3tBqko9kXhAVqbBzW83XFNtvK6q4+8uOa9cel5JIGQi0NyTFSlXXzGiyLYb6l9uqncMk6zqK+zgwfwgoxyH9gwcmWWBT0xMHDZsWF5e3saNGwcMGJCYmNjgTYeHh69YsaJx6b0Zn4VtipXmK4xHHmo/6cZ/p21d++VZ3cIoQYnKtDm3rv9xALA5ax3QU4PPwvr4cMbskz1VmTZcU+28qY4NqGs3nR9jxNef6ReeVXx2qkKuxVf2fsXF8BApY+dwqQePjiH0dbRwaoQrBtfjgCOzvEi1ePHigwcPnjt3rqioaMWKFaQ+uW4tpWqTSk8I2LSsEv07bbm2apJsOpYySPzunrI2YkZUC7aNsgCgHswH9E+ePNm4cWNqampjDuipMTmChxAa+luZCwPb9XY9bpPzWdiGt6QDdz5l0WnHE9w5jFf8n+jo+fxBNQyhgX4wEDVwbK+4CxUbGxsbG2t+XVlZyefzqU2pfu7LjUnp5f5ixsRw3pZc1ZJzyq+iBbaq8S349LWDxB8ele8cLm0lhA53wN45xAH940qTiE1z/ad3yzONiSAwrJ4n1yYCLT2vCJKwVAZ82R/KZb2EcHIOnN7zS/RXr16NiYnp0qXL119/XVZWNnny5JiYmDZt2tgwubrIKtHP685n0TEfPj01Tlqpx9UGW9436+zJmt2ZP/WwXKHDbZgGAHUUGxu7cuXKL7/8Mjo62mJ8eDtx+KF27H6ZuUFNP1qx5br693ckY0Ncxh0of6oyvfHXzeafrijX4BveEm+Ok94uM6y+aI+fFADrel7g33///cjIyOTk5OLi4vDwcG9v7/nz51+4cMGGydXF6GCXYW245rli+SxsTT+RTTrZ1ZQQ4tLLhz3tiFxvgi46wE450AH9pPa8Xj7scQfK12WrThdod74t9RUyJkfw4gO56Q/qOlB0H1/OusFiNh3js7DNcdKOHnbd2wAAq3he4OVy+aJFi6Kjo1etWmU0GhctWtSnT59WrVrZLrd6oH6c2tp90UPg5Uqfe7ICeuEC++RYB/Rzu/GbudD+daUy7W1ph39q89QOrh+E87RGYsdNdXU7u1lm+LNI//IW3grgsP8ZtsaVhQ1oBffXgfN7XhSrJ6qTSqU8Hs9G+TRElZ6g0xD3Vb1mbAVDaEVvYakaX/aH0ta5APAKVj+gz8rK2rdvn0KhqF6Snp5uhUQRQgjtuqW+W24cFegy/4yiQvvCzS8ahnbf0XydqSAQuv7MMOFguREOqwFACL38mJwjeqY2NXOxuyEe2XTspyGSqyX67y/B3T5gd6x7QL9y5coRI0akpaVFRERkZ2ebF06ePLmRmzXbe1fzQ1bV9mHSJb0EPbxZSenlhholnEXHtgyV3Jcbpx+RTzlcvqK3sKcPPMMCAEI1e9EXFBRUd5hXqVTVr+2z301NZRpcapeTubmysE2xkjH7ZVwmNq2Dq63TAYAsKSkp165dk0qlubm5I0eOzMrKsuLTN34ixs7hUm8+HSE0P1JwJE9LQy9crnNhYJ9044/bL+vqxe7XEq69A/C353VRo9GU/aPmaxsmV0elaryZPd2Ar0nMoW2Jk+6+o3nlyFkA2Ir5gN6s5uuGbY3P55svA4SFhU2bNm327NlWTLV9M6a5upvF+HHoLzb3G2WGGUfka/qJDDix8KwCrtADYPb8DJ7NtuV1rfHjx9+4caPmktu3bwcHB9fyK6cKdHqciPHjPFOZ3Hn07y5Vvh/Gk9jfqXwzF9rO4dL3DshUBuKzbnY9qABoOjQajRW3NmbMmKioqKlTp06ZMmX27NkjRoxISEhQq9WvW3/IkCEXL16suaSysjI8PLwBoZU6/P2D5av6iPq1ZPf24Uw4WL4pV/VBuCP1IgKAJPYyGMvSpUtLS0trLpkwYYK7u3stv+IjoCellxtMRKkGv1ikd3eh8dl21M+uJimXtnWodHx6ucFEfNHDZuPwAFDNugf0X331VVRUlEwmQwhhGPbbb7+lpaWJxeLXrb97926t9oUn3AYMGODp6dmA0AI2bX+8myePjhByZWHbhkq0RjiFBwAhhDCCsNPG0K1bN3d399o74j6oMCYdKDfghBuXvmeklE3q7M2NptThUw7LvVzpq/uKGHZ3oQHYncePH0dGRhYWFto6ESrUpb0D4MTIaO+OXWdaCRmh7ky5Fo9qwbLz6o4QErBpqbEStYGYdKi8Um+nx1UANF6fPn1snQIAwJELvIlAn52qMJiIlkLG/nuavXeteU+RJBwG9mOMOMSN+fbvZXkVRlunAwApTKa6jiALACCPAxf47TdUci2+brBYqcPXDhJ/e7GypM4DU9sQHUOfdeN/0J43Zr/s/GOdrdMBwPqSkpJsnQIAwG462TVAYggvMZSHCKTUEZ09WafHurPs/ip9tbEhLm3EjI+OV4wO5s7sxKc5TOIAvJm1hrgBADSGA5/B02mIjqEyjUnCpdEw5EDV3ayLF2v3CGlmoX7aEbnF6JsAAABAIzlwgTcrVePu9jrKzRt58ug7h0v9RPS438ouFr9ihgwAAACgYRz4Er2ZQxd4hBCDhuZ1F/TwZn98oiI2gPNJVz7HnmbNAQAA4KAcuDSa2edMM/XVy4ed8a5bqRof9ltZVgmcygMAAGgshy/w9jYTfIOJOLR/DRB90o0/61jFl2cVSh3clQcAANBwDl8an6lN7lyHP4OvFuPHOTzanYahQbtK026rYdoMAAAADUNWga850kV2dvb27dvv3LlDRqBSNd6M5/CHKTXxWdjinsINQyS/3taM2F126QlcsQcAAFBvZJXG6nkjkpOT4+LiDh8+HBMTs2XLFqsHeqbG3e1vBrnGa+fOTHtb+kF73vQj8rd/k90pNyKEKnT4xycqdCY4rwcAAPAGpJfG77777sqVK1u3br18+fKyZcusvv0ytcmd5zyX6GvCEBramrslTpKvNIzZW/Z/R8pH7ZF58GjkjbqvNhBTDsnLNX/f/t95S705V0VSLPDrbbXxn44WT6pMpwqcZ1jD9evX2zoFAAD5j8m5ubl5eXmZX9Dpr63Effv2PX36tMXCjh07vnH7pRrnPIOvFuLG/H2EW+L+8kvFBgNO5FWYsp8aOngwyYjlwsRC3RkJ+2Tbh0lPFGh/vFq1faiUjEAAIXQ8X3eqQPefgeJnatPY/bKpEa62zqjhMjIyak73vGTJEvN0tOPHj7ddUgA0dWQVeCaTGRoa6uvrq1QqN2/enJSUNGnSpOjo6Netf+rUKYsl5ukja4+i1OEsOub0D467c2liDva4kviqh0CHo4+Pyz149AnhvEGtOHRrH9t81JmPEBr6Wymdhu0cJvUROOfVEXvwwyDRh0crphwqz1MYJ4TxxoS42Dqjhrtx48bChQsTEhIkEglCSKvV5uTkICjwANgUWQW+uLhYo9EUFBTk5+d7enoSBOHv7z9nzhzrRnH603eEkFKHJ6WX92zBHh3i8t6B8s+68U+MbXY8X5t6XbXsvHJ0sMvoYK6HVW9SePLoWiMh4dKc/sjJtpg0bGEP/pC0spZC+rhQnq3TaZRPP/20R48e8+fPT0pK6t+///Hjx5OTk22dFABNHYmX6LlcblBQUFBQkPntggULrB6iVOUkD8HXYsN1VVQL9qfd+Aih1FjJzGPyIQGcGD9OjB/ntszwy031W2llHTyZIwNd+rdkN35A/l231ClXq/bHu+++ox53QLZtqNShBwq0Z0+qTEnp5R93db1UbJh1TP6fgWKGI3/TUVFR6enpM2fOTE9P1+ne0J8gJyenrKys5hKFQiEUCslMEIAmx7GHqi3VOMMwdrWb04Vf/bqthHFk9PO1IYIBAAAPWklEQVTbFkFS5uKewnmRgsMPtTtuqBeeVQxqxYlrzenu3cB+eGoDse2GevswqQ+fPrsLn4ZhabfUMzo55L3hM490XbxYLkwMIaQzEece6/q35Ng6qReMO1A+Pow3IYyX1I6YfkS+4k/lV1ECWyfVKAKBYPPmzTt37rx9+3bta27ZsiU3N7fmErlc3rZtWzKzA6DJcewC/8zBB6K3Ci4De6ct95223KcqU/oD7ZpLlYXKioGtOAP82FHe7HpdZndhYgfi3arfzurskKXdLPOx7serVZtiJXQa+r/Dcg8e3d4KfGqcxIdPRwgxadh/YyRlGtMbf8UhJCQkJCQk1L7O999/b7Hks88+e2OfGwBAvTh2dSx1ioHorcWDR/8gnLdnhNu+kW5tJIyN11XdtjydfKh82w31I6WTFI+6W9BDEOzGSEovn3ZEzmdhy3vZ3eVfc3U3Y9CQpxM97dmnTx9bpwAAcPAz+Dsy45gQlq2zsDvefPrEcN7EcJ5Ch597rD9bqPvxahWLhiK92d29Wd2as5yplrwOhtD8SMGgnc/yFejkmGZWf9wA1KLmQJYAAFtx4AJvwImrT/X/GiCydSL2S8imxQZwYgM4CKH7cuMfRbpjedoVfyg5DKyjJ6uDBzOiGSvYjcGkOWFveb2JmHFU3r4ZS8yhfXCoPDVWwmM64ce0T0lJSbZOAQDgyAU++6nBX8QQsuHUrE5aixmtxYykdjyE0MMKY/ZTw9Wn+l9va/IrjK0ljBA3Zjs3ZrAbs62Y4eIUhXDuSYWARVvdT0TD0FdnFR8elW+Kldg6qaZi8uTJtk4BAODIBf78Y10Pb7ats3BI/iKGv4gxMpCLENIYiVtlhr/KDNeeGXbeUj+QG91daG0lzDZiRoCYESBitBLSBQ54FDWzk6u/mGF+mmBJL+HdcqOtMwIAAEo5cIH/o0j/cRcH7uZtJ7gMrKMnq6Pn310ZTAR6pDDelRvvy42ZhbrNuao8hZFJw1oJGb4Cuq+A7iNgeLvSvfl0T1eaPV/bbyN5vm9jCAVKHHhXBwCABnDI/3qPK00yNX5HZujowUIIXSnRB0qYfJb9FhsHQseQn4jhJ2LE+D1fWKbBCxTGR0rTI6XxzyJdcZWpqNL0VIWLOZinK92DR/fi0d24NE9XupRLc3ehuXFpYg6t8aPuAAAAaDASCzyO49euXSspKTEajd7e3hERETSada70PqkyTTkkDxAxOQws44F26XnF7yPc+Czn7xluK25cmhuX1cnzhYU4gUrVpicq/JnKVKIylanxi8U6mQZ/psZlGlymMXEZmJRLF3NoYg4m4tDEbJqQTRNyaAIWJmTT+CyMz6Lx2Zgrk+YKB2cAAGBtZBX4U6dOTZs2zcvLy9vbGyFUXFxcWFj4888/9+rVq/Eb7+LF6tacdb5I999s1ZbcqtQ4aXNXqO5Uo2HIg0f34NERevXUdgodLtPgci0u1+IKHSHX4god/rTcoNARSh1eqScq9XilnqjS4yoDIWDTXBiYCxNzYWICFo3DQBwGJmDTWDSMy8RcGBiThgRsGoYhAYuGEBKyMYQQn0WjYYjLxFg0jE5D5n7yLkzMnu8dAAAANcgq8LNmzTp48GBAQED1ksLCwvj4+IsXL75y/Zs3bxYXF9dcolAoxGLx67ZfoDTGBnC/v6Rc018Mt1ftk5BNq/szDgodrjYQaiOhMRBKPa4xElojqtThepzQGAiVkVAbUWGlgSCQUo8TBFLqCYRQpR7HCaQxEHqcMOJIbSAQQmoDYcAJhBCPiTFoGEKIy8CYdIQQYmAY75+rBSw6xv1nmD8ahixu8QhYNPTiQQKfhdEwy+MGHhN73VCBTDrmUodhBDEMxfhzRA7YjREAYOfIKo0mk8k8DXw1Dw8PgiBet35KSsrdu3drLpHJZK1atXrd+iI27fQj7eKewhXnFS0F9IhmpMyPDigjZNOE1n4kQmUgjDiBENIYCYMJIYSMOKEy/L0T6k2Exvj3a5xAlfoXdk6lHkcv7q2VegJ/aQdWGQjVa3ZqgwlXG1+7w9fUyZMFBR4AYHVkFfgPP/ywU6dOw4cPb9GiBYZhxcXF+/fv//DDD1+3fkpKisWSWsam/rNIn68wbo6TBkoYnjz61EPlv41wqznwJwAIma/YYwghqx86AACA/SOrwE+fPn3o0KGHDh0yX3j39fXNyMjw8fGxysb9RfS0t918BHSEUL+W7E2xEikHToAAAACA50i8e+3j4zNlypTGbKG8vPzhw4ev/FFeXt5DxvPkSxsTpm50Oh2dTmcwKL3fr1KpeDwelRHVajWXy8VeutlMKuo/JvURcRzncrnNmjWr+6+UlJSQl48dqr29U9P0KNsxIJCdByIIQqPRuLi4kB0Ix3Eej+fm5kZGe7ff7ml+fn5r1qxJS0t75U8fPnzIZFJ6391kMmEYZq0n/eqCIAiTyUTxIUUT+ZhGo5FOp1N5HIPjOJ1Ob9GiRb1+Kzw8nKR87I09tHeCIIxGIzWBqNnnnS8QjuM4jjtZIAaDYX7czPrtnXBA5v/OFAedPXt2cnIylRELCgp8fX2pjEgQRHx8/K+//kplxPPnz/fo0YPKiARBdO/e/c8//6Qy4q5du0aNGkVlRKdBWXuvqKgQCoUUBCosLGzRogUFgW7duhUUFERBoEuXLnXp0oWCQCdOnOjXrx8Fgfbt2zds2DAKAm3bti0xMZGkjcOtawAAAMAJQYEHAAAAnBAUeAAAAMAJQYEHAAAAnJBDFngMw1gsFsVBqX9GjsFg0OlUj94DH5MkNvmYzoGy9k7ZXkHZzgCBmnggjHj98LH2rKioyPxcAWXkcjmLxaL44WnqP+bTp08lEgmVjyASBPHkyZPmzZtTFhEhVFxc7OXlReVjcgaDQS6X1+s5eFCNsoYAgRqGslaM4/jTp08txkEng9FolMlkHh4eZAfS6/UKheJ1w7Y2kqMWeAAAAADUwiEv0QMAAACgdlDgAQAAACcEBR4AAABwQlDgAQAAACcEBR4AAABwQlDgAQAAACfkeAX+xx9/DAkJCQ8PP3XqFNmxli5dGhIS4uPjs3LlSiqjr169OiMjg7KIR44c6dKlS8uWLdevX09N0Hnz5vn5+QUEBGzbto2CiCqVat26ddVvX45l9egWEW21IzkBsr8oiv80FDRtypozBa2YspZLWYO1CGRG4l5B0ix1JHn06FFQUJBKpbp3715gYKDJZCIv1vHjxzt27KjRaEpLS319fbOysqiJfu7cORaLlZqaSlDyeeVyeZs2bUpLSxUKRWBgoEKhIDvomTNnIiIitFrtkydPhEJhVVUVqRGvX78+ceLEhIQE89uXY1k9ukVEW+1IToDsL4riPw0FTZuy5kxBK6as5VLWYC0CmZG6VzjYGfyhQ4eGDx/u4uLSunVrT0/Pa9eukRdLJpNNnTqVw+G4ublFR0cXFhZSEF2pVM6fPz8hIcH8loKI5mmP3dzcBAJBbm4un88nOyidTmexWEwmk8PhmEcGJTXiL7/8UlFRUf325VhWj24R0SY7knMg+4ui8k9DTdOmrDlT0Iopa7mUNViLQIj8vcLBCvyTJ09atGhhft2iRYuSkhLyYo0aNWrKlCkIodzc3D/++KNPnz4URJ85c+bXX38tkUjMbymIWFhYmJ+fHxYW5uvru2bNGgzDyA4aFRXVtm3b5s2b+/j4LFq0iMfjkRpxxYoV06dPr377ciyrR7eIaJMdyTmQ/UVR+aehpmlT1pwpaMWUtVzKGqxFIET+XuFgBR7H8ZrjhxuNRlLDEQSRnJw8cuTI3bt3C4VCsqPv2rVLIpH079+/egkFn1ej0dy7d+/s2bM5OTk//fRTVlYW2UFPnjx5796906dPZ2RkJCcnFxcXU/lnfTkWBdEp3pGchtP8aShr2pQ1Z+pbMZUt12n2Ckon1Gq85s2bP3r0yPy6uLiY1LkNTCbTu+++KxKJrly5IhAIKIiempr64MGD48ePP3ny5Ndff9XpdBR8Xnd395iYGLFYjBDq2bPn7du3yQ6akZExbty4oKCgoKCg7t27nz17lso/68uxyI5O/Y7kNJzmT0NZ06asOVPfiilruU61VzTyHj7FCgoK2rVrp9PpCgsLW7duTWrvpB07dowePdom0WfPnm3uc0FBxFu3boWGhspksmfPnvn6+t68eZPsoOvXr3/rrbeq+7BkZ2eTHfH48ePVHVtejkVG9JoRbbgjOTqyvyjq/zRkN23KmjM1rZiylktZg60ZqBp5e4WDncH7+vrOmDEjOjoaIbRhwwYajcRbDJmZmYcOHaqel/Cnn36Ki4ujLLoZBZ83KChoypQp3bp1Iwjiiy++CA4ORgiRGnTixInZ2dkhISEIoU8//TQiIoLsiDW9/JWS/SXbw47koJz4T0PSR6OsOVPfiilruc60V8B0sQAAAIATglMHAAAAwAlBgQcAAACcEBR4AAAAwAlBgQcAAACcEBR4AAAAwAlBgQcAAACcEBR4AAAAwAlBgQcAAACcEBR4AAAAwAlBgQcAAACcEBR4AAAAwAlBgQcAAACcEBR4AAAAwAlBgQcAAACcEBR4AAAAwAlBgQcAAACcEBR4AAAAwAkxbJ0AsJkRI0ZkZ2drtdqKigpPT0+E0JgxYyorK9euXWvr1AAAVgbtvQnCCIKwdQ7Als6cObNs2bJjx44hhLRarVarFYlEtk4KAEAKaO9NClyiB8+dOHHiyy+/PHHixIABA8LCwnx8fFavXj1s2LDg4OClS5ea11m0aFHr1q0jIiJSU1NtmiwAoFGgvTs9uEQPXuHOnTs3b968detW9+7d//rrr5YtWwYEBHzxxRdHjhw5d+7c9evXdTpd9+7dIyIiIiIibJ0sAKBRoL07Kyjw4BX69u3L5/MDAwPbtm0bEhKCEBKLxUajMTMzs6SkZPTo0QghjUaTnZ0NDR4ARwft3VlBgQevQKfTzS+YTGbN5Vwu95NPPnn//fcRQlVVVWw22wbJAQCsCtq7s4J78KAe+vbtu2nTJr1er1Ao2rVrV1JSYuuMAABkgfbu6OAMHtRDz549Bw4cGB4ebjAYFixY4OPjY+uMAABkgfbu6OAxOQAAAMAJwSV6AAAAwAlBgQcAAACcEBR4AAAAwAlBgQcAAACcEBR4AAAAwAlBgQcAAACcEBR4AAAAwAlBgQcAAACcEBR4AAAAwAlBgQcAAACcEBR4AAAAwAlBgQcAAACcEBR4AAAAwAlBgQcAAACcEBR4AAAAwAn9P//Qv/wSoZOgAAAAAElFTkSuQmCC" 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" /><!-- --></p> <pre class="r"><code>summary(m.Z.FOCUS, data = FALSE)$bpar</code></pre> <pre><code>## Estimate se_notrans t value Pr(>t) Lower Upper ## Z0_0 96.838822 1.994274 48.5584 4.0280e-42 92.826981 100.850664 @@ -1733,12 +1772,21 @@ FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)</code></pre> ## Z1 1.44917 4.8141 ## Z2 1.53478 5.0984 ## Z3 11.80986 39.2315</code></pre> -<p>This fit corresponds to the final result chosen in Appendix 7 of the FOCUS report. Confidence intervals returned by mkin are based on internally transformed parameters, however.</p> +<p>This fit corresponds to the final result chosen in Appendix 7 of the +FOCUS report. Confidence intervals returned by mkin are based on +internally transformed parameters, however.</p> </div> <div id="using-the-sforb-model" class="section level1"> <h1>Using the SFORB model</h1> -<p>As the FOCUS report states, there is a certain tailing of the time course of metabolite Z3. Also, the time course of the parent compound is not fitted very well using the SFO model, as residues at a certain low level remain.</p> -<p>Therefore, an additional model is offered here, using the single first-order reversible binding (SFORB) model for metabolite Z3. As expected, the <span class="math inline">\(\chi^2\)</span> error level is lower for metabolite Z3 using this model and the graphical fit for Z3 is improved. However, the covariance matrix is not returned.</p> +<p>As the FOCUS report states, there is a certain tailing of the time +course of metabolite Z3. Also, the time course of the parent compound is +not fitted very well using the SFO model, as residues at a certain low +level remain.</p> +<p>Therefore, an additional model is offered here, using the single +first-order reversible binding (SFORB) model for metabolite Z3. As +expected, the <span class="math inline">\(\chi^2\)</span> error level is +lower for metabolite Z3 using this model and the graphical fit for Z3 is +improved. However, the covariance matrix is not returned.</p> <pre class="r"><code>Z.mkin.1 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE), Z1 = mkinsub("SFO", "Z2", sink = FALSE), Z2 = mkinsub("SFO", "Z3"), @@ -1748,10 +1796,13 @@ FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)</code></pre> <pre><code>## Warning in mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data</code></pre> <pre class="r"><code>plot_sep(m.Z.mkin.1)</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> <pre class="r"><code>summary(m.Z.mkin.1, data = FALSE)$cov.unscaled</code></pre> <pre><code>## NULL</code></pre> -<p>Therefore, a further stepwise model building is performed starting from the stage of parent and two metabolites, starting from the assumption that the model fit for the parent compound can be improved by using the SFORB model.</p> +<p>Therefore, a further stepwise model building is performed starting +from the stage of parent and two metabolites, starting from the +assumption that the model fit for the parent compound can be improved by +using the SFORB model.</p> <pre class="r"><code>Z.mkin.3 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE), Z1 = mkinsub("SFO", "Z2", sink = FALSE), Z2 = mkinsub("SFO"))</code></pre> @@ -1760,9 +1811,12 @@ FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)</code></pre> <pre><code>## Warning in mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with ## value of zero were removed from the data</code></pre> <pre class="r"><code>plot_sep(m.Z.mkin.3)</code></pre> -<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAMACAIAAAB0NULIAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdd1yT1/4H8BOSsPcQEIgKKqgVcYKCaNWKFHFccVAUqtdRKFBHndiK1tVatS21ehUHWosDF7K0RYrjKi5EinsgI6DIChsyfn/kd2MahCrkyZOEz/vlH08OJ+d8A3n8nmedwxCJRAQAAADUiwbdAQAAAID8IcEDAACoISR4AAAANYQEDwAAoIaQ4AEAANQQEjwAAIAaQoIHAABQQ0jwAAAAaggJHgAAQA0hwQMAAKghJHgAAAA1hAQPAACghpDgAQAA1BASPAAAgBpCggcAAFBDSPAAAABqCAkeAABADSHBAwAAqCEkeAAAADWEBA8AAKCGkOABAADUEBI8AACAGkKCBwAAUENI8AAAAGoICR4AAEANIcEDAACoISR4AAAANYQEDwAAoIaQ4AEAANQQEjwAAIAaQoJXLs+ePZs0aVJQUNB//vMfumMBAAAVxhCJRHTHAG8sW7bM39+/f//+np6eFy9epDscAABQVTiCVy5r1651cXEpKCgwNzentKP09PTJkycr8o2VlZUyJXw+//Xr1+/bTnl5eW5ubhsCAADoUJDglYuOjk5cXFxoaOgvv/xCdyzyxOPxPD09ZQojIiIWLVokXfLNN9+w/4fFYg0cOPDFixdDhw51cnL66aefxHVWr17d0NCgoLgBAFQWErxySUxMvHbt2qlTp6ysrN7xLU1NTW/dbr2mIm3cuNHV1ZXH40kXpqen79u3T6bmV1991fQ/ixcv/uKLLzZt2hQZGZmTkxMTE8Pj8R49ekQIcXR0VFz0AACqCQmeTkuWLOnZs+f169cJIXFxcb169Tpx4sTDhw+nTZs2a9YsmcpRUVH29vY9evRYsmSJUCi8cuVKQECAv7//unXrpLcJIRs2bHBwcLC3t1+2bJlMzeYxyDQ7bty406dPi380ePDgixcvylRow8f08vLauHGjdElFRcXSpUu//vrrlt5y8+bNBw8eBAYGGhkZ3bt3Lz8/v76+ns1mb9iwISIiog0xAAB0OCKg1YkTJ7y8vGpra7t06ZKcnNxStT///LNfv36vXr2qrq6eMWPG+vXrL1++bGRk9ODBA5FIJL2dkpLi6OhYWVlZX1/v4eFx8OBB6Z9KNzhp0qTmze7fv3/WrFkikejp06f29vZpaWkyFcRvlG7K29u7SzN3796VrlNQUNC1a1fJS39///j4+CNHjsycOfOtn3f48OE5OTkikaiysnLBggVjxow5f/78jRs3Vq5c+b6/YQCAjolF9wCjo5s8efKKFSuCg4OdnZ3HjRvXUrXU1NTKyspp06YRQkpKSqqqqkaOHOni4iI5WS3ZvnDhgp+fn6GhISHE39//woUL9vb20jVbb/bw4cMrVqxoamo6evRoUFDQhQsXZCp4eHjINJKUlPReH/nw4cP6+vq+vr5Hjx59a4Xk5OROnTr17t2bEGJoaLhr1y5x+dSpU/fs2XP27NkdO3b07dt3/fr1Wlpa79U1AEDHgQRPMwaDMXfu3K+++ionJ6eVavr6+gsWLFixYgUhpL6+vrGxMTs728DAQFJBsi0SiRgMhnibyWQKBALpn/5js4aGhm5ubmlpaceOHTt58uTx48dlKmRmZso04uXl1Tz4pKQkZ2fnt3YaFxeXkZHRrVu3mpqa2tpaHx+fxMRE6Qq//vrrlClTZN51/vz5IUOGCASCyMjIU6dORUdH79ixY/HixS39xgAAOjhcg6dfZmamgYGBnZ1dK3XGjh0bExNTUlLS0NAwZcqUK1eutFRz1KhRJ06cqK6ubmxsPHz48KhRo9632RkzZmzatMnExKRbt27v0u+5c+cKmmkpuxNCTp06xeVynz9/HhUVNXny5MTERJFI9PDhQz6fTwhpaGhISUmROZkhEol27NgRFhZWU1NjYmJiZ2fXt2/f0tLSVj4aAEAHhwRPsytXrqSnpw8bNiwuLq6Vai4uLgsXLhw6dKiDg4ODg4O3t3dLNceNG/fJJ5/079+/V69eQ4YMCQwMfN9mfX19r1+/HhQU9F79tkdjY6OTk1NhYSEh5Pz58/369TMxMZGucOzYsQkTJmhra3M4HA8Pjz59+kRFRYWHh1MRDACAesBMdnQSCoVDhgwJDQ3t2rXrl19+ee3aNRYLF00AAEAOkE7odODAAQaDERQUxGAwevXq1a1bt4sXL3br1o3uuAAAQOXhCB4AAEAN4Ro8AACAGkKCBwAAUENI8AAAAGoICR4A2ks8n5JYZmbm4cOHHz58SGM8AEAoTfBCoTAzMzM5Ofns2bO3b99u2zolAKD8JIsfbt++ffz48SkpKV5eXgcPHqQ3KoAOjqq76NPS0oKDg62trW1sbAghXC43Pz9/7969zRcFBwBVZ2FhUVJSQgixtbW9ceOGtbV1SUmJu7u7eHlfAKAFVc/Bh4eHJyYmOjg4SEry8/P9/PwyMjLesYUrV66cPXuWmugAVAOHwwkJCaE7ivdgbm5ubW0t3mAyme/+RuzvAHLf36lK8AKBQLyfS1haWr7X2YIzZ87k5OTgiB86rMrKyk2bNqlEgmez2X369OFwODweLyYmJjAwcO7cuc0XHmwF9nfo4KjY36lK8KGhoQMHDpw4caKtrS2DweByufHx8aGhoe/VyMiRI5cuXUpRhABKrqCg4NChQ3RH8U64XG5dXd2LFy9yc3OtrKxEIpG9vX0ra/35+PhcvXpVuqS6unro0KHLly+nPlgAZUTF/k5Vgg8JCfH19U1OTuZyuYQQDoeTlJTU+oJpAKC6dHR0nJycnJycxC8jIiJaqXz8+PGGhgbpkjFjxujr61MYH0DHQ+Fc9DY2NoMHDy4uLubz+TY2NuK77eTi0qVLf/31l7Ozs7u7u7zaBACF0dXV1dXVlS5hsVgMBoOueADUElUJnrq76OfNm3f16lVPT8+oqKgPP/xwx44d8ogXANpu165d6enpzctjY2MVHwwAiCnLXfR1dXX19fXSJbW1tc0fnb99+3Z6enp2draWllZdXV3v3r1zcnL69Okj9/gB4N3NmjUrIyODx+OpxC2BAB2EstxF/69//Usm91dVVQ0YMEDmppsnT570799fS0uLEKKjo9O/f//Hjx8jwQPQS09Pb9GiRUeOHBk9ejTdsQDA/1OWu+iTk5NlSlxdXS0sLGQK+/Xrt2TJktevX5ubm798+fLatWtbt26Vf/QA8J6cnZ2dnZ3pjgIA3lCxu+gdHR3DwsL69OnzwQcf/PXXXytWrOjWrZs84gV1VlNTM3bsWJnbtullaGiYmpqK28oAgDoU3kVvZ2c3f/58uTe7bNmyTz755NGjR05OTp07d5Z7+6B+eDzeo0ePUlJS6A7kDVdXV4FAwGJRuAMCQAenkv+/2NjYTJ8+/cqVK3QHAiqDzWYPHDiQ7ijewLE7AFCNqgRP6WMzDAbj6tWrIpEI/0sCAAC8FVUJnurHZlgsFp/PZ7PZVDQOAACg6qhK8FQ/NoMEDwAA0AoKr8FT+tiMOMFT1DgAAICq06A7gDZisVhNTU10RwEAAKCkVDXBs9lsHMEDAAC0RFUTPE7Rg1I5ePBgt/8Rz96Yl5f3yy+/9O7d29nZOS0tje4AVdiVK1c8PDwsLS3HjRt3//59usMBUBkq+Rw8QYIHebt9+3ZaWpqZmdm0adNkVjJ9F4GBgYGBgeLtxYsX5+XlMRiMqKioW7ducbnc8ePH37t3T0NDVcfTNOJyuX5+fjt37hw2bNjp06fHjx+fk5Ojra1Nd1wAKkBV/8dhs9m4Bg/y8p///Gfy5MlFRUUJCQkDBgyorKxsc1MJCQmnT5+Ojo5OTk6eOHGirq5u9+7drayssrKy5Bhwx5GWljZmzJhJkyZ16tRp/vz5lpaWmZmZdAcFoBpwBA8dS25urswyxCKRaMWKFWfOnLG1tSWErFq1avPmzfPmzZN5o6mpqbGxceuNFxYWzps379SpU8bGxkVFReIGCSG2trbFxcXy+xAdiLa2dnV1teRlTU0NDt8B3hESPHQgz549Gzt2rMyyxUKhkMfjzZ49W/ySx+M1NjYeO3ZM5r0+Pj4//fRTK40LBIJPPvlk8eLFbm5u4malZ1rE17VtxowZs3Tp0g0bNgwfPjwuLk5bW7tv3750BwWgGlQ1weMUPbSBvb39kydPmpc7Ojpu3779448/rq+v9/b2njNnzqxZs9638bVr1+ro6Hz55Zfil507d87LyxNvc7lcLIzUNkZGRmlpaRs2bDh//vzgwYOTkpKwQg/AO1LVXQVH8CBHMTEx06ZNMzMzKyoq+vjjjwMCAt63hbS0tH379t2+fVty1O7t7e3j47NmzZpXr17l5+f3799f3lF3FF26dNm9ezfdUQCoHiR4AOLm5vb48eP79++bm5tLLpy/ly1btlRXV7u7u0tKUlNTP//8cw8PD0JIdHS02t9CLxQKs7KyiouL+Xy+jY2Ni4uL2n9kACWnqgkep+hBvrS0tFxcXNr89qSkpOaFn3322WeffdaOoFRGWlpacHCwtbW1jY0NIYTL5ebn5+/du9fT05Pu0AA6LlVN8DiCB1Ae4eHhiYmJDg4OkpL8/Hw/P7+MjAwaowLo4FT1HBoSPIDyEAgE1tbW0iWWlpYyTysAgIKp8BE8TtEDKInQ0NCBAwdOnDhRPE0vl8uNj48PDQ2lOy6ADk1VEzwWmwFQHiEhIb6+vsnJyVwulxDC4XCSkpLs7Oxaqn/16tWCggLpkrKyMiMjI8oDBehIVDXB4xQ9gFKxs7ObP3/+O1Y+e/aszIQEZWVl5ubmFMQF0HEhwQOAom3cuFGmxNXV1czMjJZgANSVqiZ4PCYHoDx27dqVnp7evDw2NlbxwQCAGIUJnqKJL16+fLlu3brz58/n5ua6urp27969/W2C2quurv7222/pjuINmQVvVN2sWbMyMjJ4PF5ISAjdsQDA/6MqwVM08UVTU5O3t/fYsWPd3Nx0dHRGjx59584dExMTOUUN6snc3HzRokXl5eV0B/LGunXr1GlOdT09vUWLFh05cmT06NF0xwIA/4+q/2Iomvji7t27hJDNmzcHBwf369ePz+enpqb6+fm1N1xQa2w2e+3atXRHoeacnZ2dnZ3pjgIA3qBqohuKJr5oamrS0tIi/7vJTltbu7GxsZ1tAgAAqB+qjuApmvjCxcXl9evXP/74Y319/Y0bN9LS0rZt2yaXgAEAANQJVQn+fSe+WLVqlcxzsU+ePGl+U562tnZSUtLSpUtTU1OtrKzOnDljaWlJRfwAAAAqjcLbfExNTQMCAvT09Lhc7qVLl8rKylpJ8L6+vjIzW2VmZhoYGDSv2aNHj9OnT69atcrAwGDw4MHyjxsAAED1UZXgDx06FBYWxmKxIiMjt2/fPnTo0GXLli1durSls/RDhw6VKfn+++81NTVbah8T3QAAALSCqgQfGRn54MEDbW1tOzu7lJQUd3f3169fDxkyRF7rTyDBAwAAtIKqBC8UCk1NTVkslp6enniKaX19fQaDIa/2WSxWQ0ODvFoDAABQM1Ql+GnTprm6umpqan700UeffvppQEBAcnLyuHHj5NU+juABAABaQVWC//bbb318fJhMpru7e2pqanJy8qRJk+bMmSOv9jEXPQAAQCsovIteMivt6NGj5T6BJY7gAQAAWkHVTHZUQ4IHAABohaomeJyiBwAAaIWqJngcwQMAALQCCR4AAEANqWqCxyl6AACAVqhkgr969Wp8fPyTJ09qa2vpjgUAAEAZqV6C/+GHH2bOnNnY2FhUVDRo0CAej0d3RAAAAEqHwufgqcDn8yMjI+/du5eZmVlbW2thYXHgwIHw8HC64wIAAFAuKnYEX1paqqOj07lzZzabzefznZ2dX7x4QXdQAAAASkfFErylpaWOjs65c+fEi83ExcU1X2cWAAAAVOwUPSHk119/nTFjBovFKigomDdv3pQpU+iOCKCjEwgETCZTvJ2ZmXnv3r1BgwY5OjrSGxVAB6diR/CEkGHDhj158mTdunX9+vXbsWOHHJegBYC2sbKyEm9s3759/PjxKSkpXl5eBw8epDcqgA5O9Y7gCSGampqOjo5I7QDKZuvWrTdv3rS2ti4pKXF3dw8MDHxrtYULF+bk5EiXPHz4UEND9Y43AJSZSiZ4gpnsAJSSubm5tbW1eENy0r652bNnl5SUSJeEhoYaGBhQHh9AR6KqCR4z2QEoDzab3adPHw6Hw+PxYmJiAgMD586d6+Hh0VL9fv36yZQYGRlpampSHCZAx6KqCR5H8ADKg8vl1tXVvXjxIjc318rKSiQS2dvbL168mO64ADo0JHgAkAMdHR0nJycnJyfxy4iICHrjAQBVvasFp+gBAABaoaoJHkfwAAAAraDwFL1QKMzKyiouLubz+TY2Ni4uLnJ8DAYJHgAAoBVUJfi0tLTg4GBra2sbGxtCCJfLzc/P37t3r6enp1zaZ7FYOEUPAADQEqoSfHh4eGJiooODg6QkPz/fz88vIyNDLu2LF5uRS1MAHVBwcPBby3fu3KngSACAIm9J8Hfu3Llz505AQEBxcbGdnV3b2hUIBOL5LiQsLS1FIlHbWmsOp+gB2mP8+PF0hwAA1JJN8AcOHNi0aVNdXZ2fn9/w4cNXr149d+7cNrQbGho6cODAiRMn2traMhgMLpcbHx8fGhoqj5gJQYIHaB8fHx+ZEqFQuHr16ubltGtqaioqKrK2tmaz2XTHAqBKZBP89u3br127FhgYqK+vf+fOnUGDBrUtwYeEhPj6+iYnJ3O5XEIIh8NJSkpq5XzA1atXCwoKpEvKysqMjIxaqo/H5ADa77vvvvvhhx/Ky8stLS1fvXr14Ycf0h2RrN9+++2LL77Q1dWtq6vbsWPH1KlT3+VdWVlZx44dYzKZAQEBWNQOOizZBF9TU6OtrS3e1tXVbc+Q2dDQcN68eQwG49atW/fv36+urm6l8tmzZ588eSJdUlZWZm5u3lJ9BoOhoaEhvUglALyvn3/++f79+6tWrQoJCeHxePv376c7or/Jy8tbsmTJ5cuXHR0dc3JyRo8e7eHhIXPtr7lz587Nnj07ODi4sbHR09Pz9OnTQ4cOVUzAAEpFNsEHBARMmDChqKho3759Bw4cCAgIaFu7u3fv3rZtW3Z29pYtW6Kjo4cPHx4REbF69ep58+a9tf7GjRtlSlxdXc3MzFoLncXi8/lI8ABtpqWlZWho2L9//4yMjDlz5ixcuJDuiP7m+vXrHh4e4kPwPn36uLq63rhxY8KECa2/a+PGjXv37vX29iaEODo6btmy5eTJk4oIF0DJyCb4tWvXJiYmXr58ubCwcOPGja0sF9G6DRs2ZGdns9nsnTt3ZmVlmZqalpSUDBs2rKUE3wbiBK+lpSWvBgE6Gk9Pzzlz5qxcuXLq1KkvX75s5aIYLaytrZ88eSIUCsWn654+fdq5c+d/fFdxcXH37t3F2927dy8qKqI4zLarrq6+du2ajo6Om5sbjlVA7mQTfGlpqZubm5ubm+Rl64fRLbGwsGhsbCSEGBsbi+e3kfvXF5fhAdopOjr60aNHPXv23LZt261bt5TtFP3QoUM7derk4+Pz0UcfpaSkdOvWbeDAgf/4rhEjRvz888/btm0TCoU7d+4cMWKEAkJtg6ysrPHjxzs6OpaXlzMYjNTUVGUbYIGqk03w48aNE29UVlY+fvy4X79+d+7caUO7GzZsGDZs2IQJE3r27Dly5MixY8cmJiaGhYW1N14puJEeoJ3Ed7qUlpa6uLi4uLjQHY4sDQ2NhISEw4cPZ2dnz5o1y9/fn8Fg/OO7vv322xkzZnTq1EkoFH744YdK+2R/eHj4d9995+/vTwgJDQ3dvHnzpk2b6A4K1Ipsgr9x44ZkOzs7OzAwsG3tenl5Xb9+/cyZM8bGxk5OThYWFidOnJCsNCUXSPAA7SSvAT112Gz2p59++l5vMTExOXfuXGlpKZPJNDY2piYuObh79+7HH38s3vbx8fn555+p6CU5OXn37t11dXVTp06dM2fOu4yQQG20NpNd375923MO3NjYOCgoqM1v/0c4RQ/QTvIa0Cuhtl1bVCRHR8dLly6JZxy6dOkSFY/zJScnBwcHb9682dDQMDIysqysbOnSpXLvBZSWbIJftGiRZPvBgwfK/AgpjuAB5KidA3p4X1u3bp0yZcqHH35YUVHx5MmTK1euyL2L/fv3f/vtt9OnTyeEdOnSZcqUKUjwHYpsgpe+bd7Hx0cJJ76QQIIHaCdVGdDX1NQQQvT09OgORJ7c3d2zsrIuXLigo6Mzbtw4yQQkclRXV6evry/eNjAwqKurk3sXoMzeJHjxMjC2trbSP75586arq6uig3o3WFAOoJ2Uf0BfXV0dFBR0/vx5QoiXl1dMTIw6pXlLS0vxTXYUmTx58tq1a7t166avr79kyZLJkydT1xcooTcJ/ocffiCEcLncS5cuOTs76+vrZ2RkTJ48+dixY/SF1xosKAfQZqoyoP/qq6+MjY1LS0sJIfPnz//666+3bt1Kd1AqY86cOaWlpb6+vo2NjVOmTMFd+h3NmwQfGxtLCPnoo4/u378vPlP3/Pnz+fPn0xZay4RC4d27dxsbG+vr6+mOBUAlqcqAPi0t7eDBg5qamoSQhQsXzpkzh+6IVMzSpUtx3b3D0pB5/eLFi549e4q3u3btKl4qRqmUlZW5uroGBATk5eVNnz49Ly+P7ogAVE9sbGxsbKympub9+/fv3Llz+fLlR48elZeX0x2XrE6dOuXm5oq3nz9/3qlTJ1rDAVAlsjfZDR061M/PLyAgQCQSxcbGDh48mJawWrFmzZoRI0Z8//33Hh4e/fr1W7JkyfHjx+kOCkAlyXFALxQKs7KyiouL+Xy+jY2Ni4uLeArLdlq6dGlQUFBOTg4hJCoq6tChQ4SQZ8+erV279v79+3379l2zZg2Hw2l/RwDqRzbB7969e9euXb/99puGhsaIESOU8BT9rVu3tm3bRghhsVjDhw9ftWoV3REBqCp5DejT0tKCg4Otra1tbGwIIVwuNz8/f+/evZ6enu2M8KOPPkpISIiNjWUwGElJSS4uLlVVVR999NGCBQtCQ0NTUlLGjh17/fr1AwcO/Pnnn5aWlosWLZIMWQA6uDcJPiws7Jtvvvnqq68IIeIFGR89evTll19GRUXRFt3b2Nvb37hxw83NjcVi3bt3z8HBge6IAFSVvAb04eHhiYmJ0jtjfn6+n5+f+Fa+5kpLSysrK6VL6uvra2pqnj17Jn5pZ2cnXqu6oaHB2Ng4ODhYXN7U1HT58uWePXt+8cUXhYWFAQEBZ8+enTRpEpvN/uyzzx49euTp6Xn8+HHxOEOmncLCQkl3KEe5spXn5eUJhUIiX6L/OX36dF1dXUIzIpoMGTLEx8enefmjR4+sra0DAgJsbW2NjY0zMjIUHxuAAuTn59va2lLUeGhoaHl5eWgzbWutV69eNTU10iUNDQ2DBw9uqf6//vUv+79jsVhaWlqSl6dOnRLXjI6Olq526tSp+Ph4X19fSbmOjg6DwTh69Ki4/tixY01NTd+lnbeWR0VFhYaGzpo1a/78+e9SH+Uol1c5h8PR0dERyRVDJBJJ5/sHDx5cuXLl3//+9+rVq//888/Nmze3ecXYdnJ1dbWwsEhISGj+o7KystOnT//4449LlixRp8k1AaQVFBQMHTo0Pz+fisbPnDnj5eWVmpoqU+7j49OG1n755ZeoqKiJEyfa2toyGAwulxsfHx8aGrpgwYJ3bKGV/V2isLAwPj6+tLT0hx9+CA0NnT17dmJi4ubNm+vq6kpKSsR1Nm3aVF1dvWHDhjZ8ivv373/44YeLFy+2srKKiooaP378mjVr2tAOQBtQsb/L3gXzxRdf6OvrX79+/fLly5s3b27bfkI1U1PTOXPm2NvbGxoa0h0LgEqaOHGitra2g4NDcXGxj4/P1atXN23a1OblSkNCQs6fP29vb//69euSkhIOh5OUlPTu2f1d3Lx5c8CAAfHx8Rs3buTxeN99913v3r3PnTt3/vx5BweHzZs3NzQ0ZGdn79mzR7KCy/uKjo5euHDhsmXLAgMDz5w5Q9HqLwAKI3uTXXFx8fTp01esWDFz5kwPDw/JJTElhMVmANrpiy++mDNnjvSAPjk5uW1N2dnZUXpP7po1a7Zt27Z06dKLFy9mZWUlJiZWV1dPmTLFycnp6NGjwcHBkZGR1tbWa9ascXd3b1sXFRUVH3zwgXjb3Ny8rq6uqalJfIkUFEwoFF64cKGkpMTd3R1PSbSZ7BG8o6PjkiVLDh8+PH78+O3btyvzITLmogdoJ/GA/uTJk8o/oH/+/Ln4P/pBgwYNGDAgNzf3448/Fi9u26VLl6SkpPr6+ufPn7/v2rLSvLy8fv7554KCgsbGxnXr1o0YMQLZnRYNDQ0jRoyIiIg4e/bs4MGD4+Li6I6IEEJ++uknDodjaGg4Y8aM169f0x3OO5FN8Dt27LCxsTl9+rSVlVVpaal4ejvlhAQP0E4qNKAfPHjw+fPnGxoaHj9+HBsbO2TIkMuXL7/76ji1tbXz5s0zNDQ0MTFZsmTJW//rmDZt2pQpU5ydnU1MTO7cuRMdHS3XTwDvat++fZaWlhkZGb/99ltqaurnn38uc6+Y4p0+fTo6Ovr8+fMFBQVWVlZK+AD5W8kmeAsLi1GjRmVnZzc1NS1YsKB79+60hPUusNgMQDup0ID+22+/TUpK0tLS6t279/79++/cuVNUVBQUFPSOb1+9enVNTU1eXt7Dhw9zcnLEc2k0t2rVqrKysoqKioSEBPHTwqB49+/fHzFihHj7gw8+EAqF4sUIaHT27NlFixY5OTkZGhpu3rz5999/V4nDS9kEf+DAgenTp3/99dcNDQ3Dhw9X5jEsFpsBaCcVGtBbWVnduHHj3Llz8fHx33333Zo1ay5evPjuS6wmJSVFRkYaGxt36tRp1apViYmJrVTGmb5XXLEAACAASURBVHl69e3b9/fffxc/FH7r1i1NTU1zc3N6Q9LX1y8rKxNv83g8TU1NJpNJb0jvQvYmu+3bt1+7di0wMFBfX//OnTuDBg2aO3cuLZH9I5yiB2inAwcObNq0qa6uzs/Pb/jw4atXr1ba/Z0QoqGh0bdv3759+7bhvYaGhq9fvxZPcvf69es2Py8ACvDpp5+eOHHC2dnZ3t4+IyNj3759dEdEZs+e7e3traOj07lz5++///6zzz5jMBh0B/XPZBN8TU2NZFCsq6urzCNZJHiAdlKhAX07ff7557Nnz169enVDQ8O6det2795Nd0TQIjabnZKSkpGR8erVq71791pYWNAdEXFxcUlISPjpp594PF5QUJCqrGoom+ADAgImTJhQVFS0b9++AwcOBAQE0BLWu8BjcgDtpEID+nYKCgoyNTWNjY1ls9mHDh2SXOIFpeXq6kp3CH8zcODAmJgYuqN4P7IJfu3atYmJiZcvXy4sLNy4cWN7prGjaHUpsYqKipKSEj09PXk1CNABqdCAvv18fX19fX3pjgJAcWQTPCHEx8dHMl1lVVWVgYFBG9qlbnUpQsjevXtXrFihqalZUVFhZmYWHh7e/jYBOiA5DugpIhAIxCvFde7cOSwszNbWlu6IAFTGmwR/+/btlStXlpWVffzxx2FhYStXrszLyxMfgreh3fddXerdFRYWRkRE3Lx5c//+/dXV1d999924ceOwQCRA28hlQE+dzz///OHDh//+978fPHjg5uZ28+ZNKysruoMCUA1vEvzs2bMnT548ZsyYmJgYZ2fn+fPnBwQEdO3atW3tCgQCmadILS0t5TJZwZ07dwYPHtylSxcWi6Wjo+Pp6Xnr1i0keID3It8BPUVqamqOHDnC5XJ1dXUJIeXl5bGxsYsWLaI7LgDV8CbBl5eXR0ZGEkJ69ep15swZ8XabhYaGDhw4sPnqUu2LlhBCunbteu/evYaGBjabzePxsrOzw8LC2t8sQIci3wE9RXg8np6enji7E0I6d+4seRYZAP7RmwQveWzfzMys/TevhYSE+Pr6Jicnc7lcQoh4dSk7O7uW6nt7e8ucva+qqurXr1/zmn369BkxYsSwYcOMjIwePHjg7u7u5ubWzmgBOhr5DugpYm1tbWZmtnPnzgULFjx//nz//v379++nOygAlfGWm+zkRXp1qd27d7eS3QkhJ0+erK+vly4ZM2aMpaXlWyvv27cvJSXll19+GTx48LFjx1RiwgEApSLfAT114uLiZs+evWTJEn19/XXr1g0fPpzuiABUxpsE/+LFC8n9NTU1NZLtqqqqNrSblJRUUlIieblu3TotLS1CSEtzR+vo6Ojo6PwtMharlcw9bty4p0+f3rt3D9kdQI317NnzypUrDQ0N4v9AAODdvUnwdXV1cmw3Jydn9erVM2bMMDU1JYTU19eLF3Z898Uh/hHmogdoM/kO6KmG7A7QBm8SvHx3oaVLlw4bNmzlypWBgYGjR4/+448/tm/fLsf2CaaqBWgH+Q7oAUAJUXgN3t3dPSEhISwsLCEhoaGhQe7tI8EDtBmOiQHUntzmjn0rQ0PDmJgYV1dX6Rlv5AVz0QMAALSE2gQvNmPGjOTkZLk3iyN4AACAligiwVMECR5ASQgEAsl2Zmbm4cOHHz58SGM8AEBUOsHjFD2AkpDMD799+/bx48enpKR4eXkdPHiQ3qgAOjgKb7KjGo7gAZTN1q1bb968aW1tXVJS4u7uHhgYSHdEAB2XCh/BI8EDKBtzc3PxKlPm5uaSyfIAgBYqfASPU/QASoLNZvfp04fD4fB4vJiYmMDAwLlz57ayuvzu3bufPXsmXVJQUKCtrU19pAAdiAoneBzBAygJLpdbV1f34sWL3NxcKysrkUhkb2+/ePHilurr6+ubmJhIl2hoaGDaaQD5QoIHADnQ0dFxcnJycnISv4yIiGil8ieffCJTcvLkSX19faqCA+iQVPsaPE7RAyihkSNH0h0CAKhygsdiMwDKSfqxeLm4devWzJkzvb29t27d2tjYKN/GAdSVCid4nKIHUE7yfTru3r1748ePHz58eHh4eGpqanh4uBwbB1BjuAYPAHI2b948ObZ2+PDhkJCQBQsWEEKGDx/euXPnqKgoNpstxy4A1JIKH8HjMTmAjqC2tlayXL2Ojg6DwcBZeoB3ocIJHkfwAB3BxIkTf/7555s3b75+/Xr58uXu7u56enp0BwWgApDgAUCpjRw58uuvv545c2avXr24XO6BAwfojghANajwNXicogdQe3/99deiRYuysrJ69Oixd+9ed3d3uiMCUBk4ggcAJVVbW+vr6+vv75+Tk7N06VI/P7+ioiK6gwJQGUjwAKCkMjMzbW1t58yZY2FhMWnSpFGjRqWlpdEdFIDKUOEEj1P0AOpNW1u7urpa8rKqqkpHR4fGeABUiwpfg8cRPIB669evH5vNDg8Pnzx58qVLl3JyckaPHk13UAAqg8IjeKFQmJmZmZycfPbs2du3bwuFQvm2z2QyhUKhSCSSb7MAoCRYLFZycrKmpua6deuKi4v//PNPQ0NDuoMCUBlUHcGnpaUFBwdbW1vb2NgQQrhcbn5+/t69ez09PeXYi/ggHnNaAagrMzOz77//nu4oAFQSVQk+PDw8MTHRwcFBUpKfn+/n55eRkSHHXsQLyiHBAwAAyKDqFL1AILC2tpYusbS0lPvpdCwoBwAA8FZUHcGHhoYOHDhw4sSJtra2DAaDy+XGx8eHhobKtxfcZwcAAPBWVCX4kJAQX1/f5ORkLpdLCOFwOElJSXZ2di3V37lz54sXL6RL8vPztbS0Wu8FCR4AAOCtKHxM7tWrV5aWltOnTzcyMhKXJCQkjB8//q2VTU1NeTze3yJjsZhMZutd4FF4AACAt6IqwW/atGnXrl0eHh4LFy48efJk//79CSHz5s1raabJ6dOny5ScPHnyH9eMwhE8gJIQCoVZWVnFxcV8Pt/GxsbFxUVDQ4Xn0QJQA1Ql+B07dmRlZZmZmWVnZ0+ZMuXWrVuSFZ3lCAkeQBko5rFYAHgvVCV4AwMD8fF33759g4ODFy5cuHfvXrn3glP0AMpAMY/FAsB7oeocmr+/v7u7++7duwkhCxcuLCsrmzFjRm1trXx7wRE8gDJQzGOxAPBeqDqC//rrr93d3UtLSwkhDAYjLi7u2LFjJiYm8u0FCR5AGSjmsVgAeC8U3kUvvSwEk8n09/f39/eXbxc4RQ+gDN73sVgAUAAVXk2O4AgeQGnY2dnNnz+f7igA4A0keABQtI8//vjatWvSJVVVVf369aMrHgC1pPIJHqfoAWi3a9eu9PT05uWxsbFvrR8XF9fQ0CBdMmbMGEtLS0qCA+ioVDvBY7EZAGUwa9asjIwMHo8XEhLyLvV1dXV1dXWlS1gsFoPBoCY6gA5KtRM8TtEDKAM9Pb1FixYdOXJE+tZaAKAXEjwAyIGzs7OzszPdUQDAG6o9WTQekwMAAHgr1U7wOIIHUEIjR46kOwQAQIIHAHkTCAR0hwAAKp7gcYoeQAkFBgbSHQIAqH6Cl3maFgBoN2/ePLpDAABVTvBlZWXW1tYpKSnv/pa0tLSvv/56586d1dXV1AUGAABAO1VN8Js3b+7Ro8fJkyfPnDkTExPzLm9Zv379559/zmazL1++PGjQIB6PR3WQAAAAdFHJ5+Bv3bq1Z8+ehw8fmpubjxkzJiwsbNKkSUZGRq28pbGxccuWLU+ePLGwsCCEBAYGHjp06PPPP1dUyO3V2NhYXV1tampKdyAAAKAaVPII/vr1615eXubm5oSQzz77jMlkZmdnt/6WV69eGRoairM7IaRPnz75+fmUByonERERZmZmPXr0GDRo0KNHj+gOBwAAVIBKJvguXbpkZ2eLRKJTp05FRERUVFQsX748MjKyW7duJiYmAQEBZWVlhJDc3NyoqKhdu3a9fPnSxsaGxWIlJSURQiorK48dO+bh4UH353gnx48f//3333Nzc0tLS+fMmUP1/ckikejw4cNz5sxZsWJFXl4epX0BAAB1VDLBe3l5aWlpDR06NCgoSCgUcjicnJycb775ZuTIkXfv3jU1NV2wYMGlS5dcXV0fPHhw48YNFxeXhw8fxsbGhoSEODo6duvW7aOPPho/fvz79isQCB49esTlcqn4UC1JS0ubPXu2mZkZISQ4OPjevXs1NTXUdbd8+fKff/55+PDhLBbLzc2toKCAur4AAIA6KnkNnslknjt3LigoSEtLy9/f/6uvvqqpqWEymRcvXvTy8vrkk0/27NmTlZXl5+fn6empoaHBZrNDQ0MjIiKOHTtWWFhoa2trZmb24sULQ0NDQoiBgUFFRUV+fn6PHj309fVb6vTx48eTJk1qamqqrKz09PT87bff2Gy2Aj5sp06dnj9/Lt5++fIlk8nU0dGhqK+mpqZdu3bl5eUZGxsTQurr62NiYiIiIijqDgAAqKOSCZ4QwmQyhw4dqq+vX1BQ8MUXXzx58uTYsWOffvrphg0bCgsLNTQ0ioqKKioqTp06JRQKCwsL7927t27duqqqKkJIVVUVn8/n8/nil5WVlUKhULxUpa6urpmZGYvFYrFYBgYGhBADAwMWi6WhoXHnzh1bW9uBAwdqaWn98ccfXl5eQ4YMkVTT1tYW511xfUKIiYkJIYTNZosHDZIKOjo62trahBB9ff13GSLMmzdvyJAhAoGgS5cue/bsWbp0qYYGVeddampqWCyWOLsTQmxtbXGWHgBARalqgieE/Otf/9q4caOjoyOHwyksLBSJRBYWFkwm8+7du1988cXTp08/+OCDlStXikSisLAwNze3rVu3Nm/kxo0b/v7+169f19fXv3Xr1oQJE5KTk7W1tZuamsTPyvN4PIFAIBAIJk6cuG3bNoFAUF9fz2KxLl++bGJiwufzy8vLCSF1dXX19fXkf6MHkUhUUVFBCJG0U19fX1dX17wmIcTQ0JDJZBJCjIyMxMlbUiLecHBwSEpKEggElpaWT548WbBgASFEV1dXS0uLEKKlpSVeWlsy2iCEGBsbi4cskgGHZKgh3ZFk2MFgMIyNjY2NjXv06PHjjz+Gh4cXFhZGR0dv2bKFqr8fAABQSYUTvLW1dXp6+vLly48ePTpt2rTdu3evW7fO0NAwMDBw7ty5RUVFPj4+hw4damhosLW1jY+Pf2sj169f9/b2Fj9+NnTo0B49elRWVvbu3bt5zS5dujg6Og4aNIgQsnbt2jFjxixfvlwuH0Q8hiD/O5fw1g3JaECyUVtbK57Fr6GhQTzIkAwmCCHiEun6jY2Nkov3FRUVIpGISA07JCMSQsjNmzcXLVpECNHW1g4NDRUXampq6unpibclgwYNDQ3J04lMJlN8yYNIjTmI1BiCSA1KZMqlG5cepki3L92vzNtb8vLlyzNnzgiFQl9fXxsbm9YrAwCoGRVO8ISQ7t27nzhx4ty5cxs3bty0aZO3t/e6devEZ5htbW1v3759//59TU3NHj16iA9nm+vSpcvRo0dFIhGDwaiurn769GmXLl3eWnP9+vWTJk0KDAwsKSlJSkq6evWqvD6FJC+Kz+rTrra2trKyUktLi8FgSAYK0uMDyaBBKBRWVlaKCwUCgWTuoIaGhtraWvF2fX29pJHCwkLJ1MKSsYVM49LDFOk2idRISObtMuMA8TCisbGxqKhIvB0WFta9e3c9PT3p0xjk7wOO5i+lxxOEED09PU1NTclLmRGG9BhFTHIS5a31xe2npaWlpqZaWVkFBwf36tWLAADID4UJXigUZmVlFRcX8/l8GxsbFxcXii4ee3l5eXl5NS9nMpkffPBB6+/19vb+4YcfxowZM2TIkISEhJkzZ3bu3PmtNadOndqzZ8/ExERra+vNmzeLb2tXS7q6upLjbyUZc7ROepxB/nduIywsLDg4eMaMGeXl5efOnUtISIiKipIePRCpsyBiNTU1jY2NkpeScYzkp9LPL0gutYhJj1HEJKdJ3lqfEJKXl1dZWWlkZOTn5zdy5Mjff//d2dm5bb8B2gkEAvFFJUJIZmbmvXv3Bg0a5OjoSG9UAB0cVQk+LS0tODjY2tpafGqUy+Xm5+fv3bvX09OToh7bRnxD/smTJ58+ffrjjz+OGjWqlcr9+vXr16+fwmKDd6ShoSE9EBFvv3r1auzYsfb29oQQQ0PDmJiYgQMH0hbi23A4nP/+97/du3cnhPTo0WPXrl2//PIL3UG1kZWVVUlJCSFk+/bt33///ahRoyIiItatW4dl5QBoRFWCDw8PT0xMdHBwkJTk5+f7+fllZGS8tf6rV69kFoCpr68XX36mGpPJnDp1qgI6AkUaNGjQsWPHxPdMHDlyZPDgwXRH9Dfimx6sra3FL21sbFraNVTL1q1bb968aW1tXVJS4u7ujgQPQCOqErxAIJD85yVmaWkpfcZSRlhY2M2bN6VLiouLxZPRArTB+vXrvb29e/XqJX7KMTk5me6I/obBYIwZM+abb7755ptvKioqfvrpp3//+990ByUH5ubm4h3f3NxcctIeAGhBVYIPDQ0dOHDgxIkTbW1tGQwGl8uNj4+X3JLd3NGjR2VKli1bJpk6HuB9mZmZXbt27a+//hIKhX379lXCZLNz5845c+YYGxszmczQ0NBPP/2U7ojajs1m9+nTh8Ph8Hi8mJgY8ZMsqjIbNIC6oirBh4SE+Pr6Jicniyd25XA4SUlJdnZ2FHUH0JyGhoYy37ZmaWmZmJjY0NDAZrOpm7xIMbhcbl1d3YsXL3Jzc62srEQikb29/eLFi+mOC6BDo/Auejs7u/nz51PXPoAakH4wT6Xp6Og4OTk5OTmJX2KGYwDaqfZz8ACgiq5cuSKzaFNZWZn0ZAYA0H5I8ADQXrt27UpPT29eHhsb+9b6SUlJjx8/li6pq6ujbhUlgI5JeRM8g8H49ddfb9y48dafnjt3TjIBnGLU1dUxmUzpucyoJhKJqqqqFPwxa2pqtLS0pGdwo5pAIKitrZVMT6sYVVVVenp6irzyzefztbW13+tpvbq6upZmYFQ2s2bNysjI4PF4ISEh71J/w4YNMiXLly9PSUmZNm3aW+srbH8XTz1EdS8K27UV1pFQKKypqVHAXiwQCOrq6lpZ9lNe+Hx+Q0ODzPSUVGhqatLV1R00aBAV+7vyJvjQ0FDxQ8zNCYXCuLg4BT9WlJ6ebmBgMGDAAIX1WFVVdfToUQU/o5+QkODo6NijRw+F9cjlci9duqTgj3nkyJERI0ZYWVkprMdHjx5xudz3/ZgcDoeieORLT09v0aJFR44cGT16dNtaUIb9vbGxMTo6WgFfxerq6iNHjiigo7KysrNnzyqgo5cvX6ampiqgo/z8/IyMDD8/P6o7evbs2V9//TVhwgSqO3rw4MHr16/Fvzr57+8iFcTn85lMpoI7Xbhw4fbt2xXZ44sXLzgcjiJ7FIlEfn5+x48fV2SPV65cGTZsmCJ7FIlEbm5uV69eVWSP4iWRFNmj2lDY/l5RUWFkZKSAjvLz821tbRXQ0f37952cnBTQ0fXr1wcPHqyAjlJTU0eNGqWAjs6cOTNhwgQFdPTrr78GBARQ1LhqP5wDAAAAb4UEDwByNnLkSLpDAAAkeACQN8mqvgBAIyR4AJAzrDEDoAyYkZGRdMfw3hgMhlAo/PDDDxXZqUAgcHR0tLW1VViPbDZbKBQqeELvpqam/v37K3KZHzabzWazFbzaW0NDg7u7uwIetpFgMplGRkZ9+/ZVWI80ku/KvArb31ksFp/PHzFiBNUdsdlsgUAwfPhwBXRECBk2bBjVHWlqajKZTFdXV6o7YrPZWlpaClj6mcVi6enpubi4UN0Rk8k0MTH54IMPqGicIWp5hTcAAABQUThFDwAAoIaQ4AEAANQQEjwAAIAaQoIHAABQQ0jwAAAAaggJHgAAQA0hwQMAAKgh1Uvwv/zyS+/evZ2dndPS0qju65tvvundu7ednd2mTZsU2fuWLVuSkpIU1uO5c+cGDx7cpUuX3bt3K6bTFStWdOvWzcHB4ddff1VAjzU1Nbt27ZK8bN6X3HuX6ZGuL5IaoPoXpeA/jQJ2bYXtzgrYixW25ypsh5XpSIzCbwVFq9RRJC8vz8nJqaam5vHjx46OjgKBgLq+/vjjjwEDBtTV1ZWUlHA4nFu3bimm98uXL2tqah44cECkkM9bXl7eo0ePkpKSyspKR0fHyspKqjtNT093cXGpr68vKioyMjKqrq6mtMe7d+/OmTNnxowZ4pfN+5J77zI90vVFUgNU/6IU/KdRwK6tsN1ZAXuxwvZche2wMh2JUfqtULEj+OTk5IkTJ+rq6nbv3t3KyiorK4u6vkpLSxcsWKCtrW1ubu7h4ZGfn6+A3nk83sqVK2fMmCF+qYAexcsem5ubGxoaZmdnGxgYUN0pk8nU1NRks9na2tosFotQ/DFjY2MrKiokL5v3JffeZXqk5YukHqj+RSnyT6OYXVthu7MC9mKF7bkK22FlOiLUfytULMEXFRVJZoO3tbUtLi6mrq9p06bNnz+fEJKdnf3f//535MiRCug9LCxszZo1pqam4pcK6DE/Pz83N7dv374cDuf7779nMBhUd+ru7t6zZ8/OnTvb2dlFRkbq6elR2uPGjRtDQkIkL5v3JffeZXqk5YukHqj+RSnyT6OYXVthu7MC9mKF7bkK22FlOiLUfytULMELhUIGgyF5yefzKe1OJBJt3759ypQpJ0+eNDIyorr3o0ePmpqajh49WlKigM9bV1f3+PHjixcv3rlzZ8+ePbdu3aK60wsXLjx+/PjPP/9MSkravn07l8tV5J+1eV8K6F3BXyS1oTZ/GoXt2grbnRW/Fytyz1WbbwWrne9XsM6dO+fl5Ym3uVxu586dqetLIBBMnTrV2Nj45s2bhoaGCuj9wIEDT58+/eOPP4qKio4fP97Q0KCAz2thYeHl5WViYkIIGT58+IMHD6juNCkpaebMmU5OTk5OTm5ubhcvXlTkn7V5X1T3rvgvktpQmz+NwnZthe3Oit+LFbbnqtW3op3X8BXsxYsXH3zwQUNDQ35+fvfu3Sm9O+m3336bPn06Lb0vXLhQfM+FAnq8f/9+nz59SktLX716xeFw7t27R3Wnu3fv/vjjjyX3sGRmZlLd4x9//CG5saV5X1T0Lt0jjV8kVUf1L0rxfxqqd22F7c6K2YsVtucqbIeV7kiCum+Fih3Bcziczz//XLxEenR0tIYGhZcYLl26lJycbG1tLX65Z8+e8ePHK6x3MQV8Xicnp/nz57u6uopEolWrVvXq1YsQQmmnc+bMyczM7N27NyFk6dKl4hWXFfaLbf4rpfqXrAxfJBWlxn8aij6awnZnxe/FCttz1elbgfXgAQAA1BAOHQAAANQQEjwAAIAaQoIHAABQQ0jwAAAAaggJHgAAQA0hwQMAAKghJHgAAAA1hAQPAACghpDgAQAA1BASPAAAgBpCggcAAFBDSPAAAABqCAkeAABADSHBAwAAqCEkeAAAADWEBA8AAKCGkOABAADUEBI8AACAGkKCBwAAUENI8Mrl2bNnkyZNCgoK+s9//kN3LAAAoMIYIpGI7hjgjWXLlvn7+/fv39/T0/PixYt0hwMAAKqKRXcA8Ddr167V1tYuKCgwNzenOxYAAFBhOEWvXHR0dOLi4kJDQ3/55RdKO0pPT588ebIi31hZWSlTwufzX79+/dbKrfyovLw8Nze3DQEAAHQoSPDKJTEx8dq1a6dOnbKysqI7Fnni8Xienp4yhREREYsWLXprfcmPXrx4MXToUCcnp59++kn8o9WrVzc0NFAaLQCAGkCCVy6nTp16+PDhtGnTZs2a9Y5vaWpqeut26zUVaePGja6urjweT7owPT193759b60v/aNNmzZFRkbm5OTExMTweLxHjx4RQhwdHamOGQBA1SHB02nJkiU9e/a8fv06ISQuLq5Xr147d+5MSEg4fvz4oUOHZCpHRUXZ29v36NFjyZIlQqHwypUrAQEB/v7+69atk94mhGzYsMHBwcHe3n7ZsmUyNZvHINPsuHHjTp8+Lf7R4MGDL168KFOhDR/Ty8tr48aN0iUVFRVLly79+uuvm1eW+ZGRkdG9e/fy8/Pr6+vZbPaGDRsiIiLaEAMAQIcjAlqdOHHCy8urtra2S5cuycnJLVX7888/+/Xr9+rVq+rq6hkzZqxfv/7y5ctGRkYPHjwQiUTS2ykpKY6OjpWVlfX19R4eHgcPHpT+qXSDkyZNat7s/v37Z82aJRKJnj59am9vn5aWJlNB/Ebppry9vbs0c/fuXek6BQUFXbt2lbz09/ePj48/cuTIzJkzZT6pzI8qKysXLFgwZsyY8+fP37hxY+XKlW37PQMAdDS4i55mkydPXrFiRXBwsLOz87hx41qqlpqaWllZOW3aNEJISUlJVVXVyJEjXVxcJCerJdsXLlzw8/MzNDQkhPj7+1+4cMHe3l66ZuvNHj58eMWKFU1NTUePHg0KCrpw4YJMBQ8PD5lGkpKS3usjHz58WF9f39fX9+jRo//4I0NDw127dom3p06dumfPnrNnz+7YsaNv377r16/X0tJ6r64BADoOJHiaMRiMuXPnfvXVVzk5Oa1U09fXX7BgwYoVKwgh9fX1jY2N2dnZBgYGkgqSbZFIxGAwxNtMJlMgEEj/9B+bNTQ0dHNzS0tLO3bs2MmTJ48fPy5TITMzU6YRLy+v5sEnJSU5Ozu/tdO4uLiMjIxu3brV1NTU1tb6+PgkJib+44/Onz8/ZMgQgUAQGRl56tSp6OjoHTt2LF68uJVfGgBAR4Zr8PTLzMw0MDCws7Nrpc7YsWNjYmJKSkoaGhqmTJly5cqVlmqOGjXqxIkT1dXVjY2Nhw8fHjVq1Ps2O2PGjE2bNpmYmHTr1u1d+j137lxBMy1ld0LIqVOnuFzu8+fPo6KiJk+enJiYKBKJ/WfxugAAIABJREFUHj58yOfzm/9I/BaRSLRjx46wsLCamhoTExM7O7u+ffuWlpa28tEAADo4JHiaXblyJT09fdiwYXFxca1Uc3FxWbhw4dChQx0cHBwcHLy9vVuqOW7cuE8++aR///69evUaMmRIYGDg+zbr6+t7/fr1oKCg9+q3PRobG52cnAoLC1uqcOzYsQkTJmhra3M4HA8Pjz59+kRFRYWHh1MRDACAesBUtXQSCoVDhgwJDQ3t2rXrl19+ee3aNRYLF00AAEAOkE7odODAAQaDERQUxGAwevXq1a1bt4sXL3br1o3uuAAAQOXhCB4AAEAN4Ro8AACAGkKCBwAAUENI8AAAAGoICR4AAEANIcEDAACoIeV9TI7P51dVVdEdBQCdtLW1dXR06I5CEbC/A8h9f1feBL9+/frvvvtOW1ub7kAA6CEUCi0tLR8+fEh3IIqA/R06OCr2d+VN8LW1tWvXrl26dCndgQDQo6CgYOjQoXRHoSDY36GDo2J/V+Vr8CJRY24B3UEAAAAoIxVO8DWXb3GXfdeY2+IKJQAAAB2WqiZ4UUNj+eEz+iNdy/a3tggbAABAx6SqCb7y1O9avbqbB38irGuouZpJdzgAAADKRSUTPP91edW5SyYBEwiDYTbHrzzmpKihke6gAAAAlIhKJvjymJMGPiNY5iaEEC0ne60eXSvPptIdFAAAgBJR3sfkWtKYx625mqlZ+LL2era4RFhbW3s7x8h3NENLk97YQDmJRKLDhw/X1tbSHcgbJiYmU6dOpTsKAFBnqpfg2TZWnb9bJlvKZCK7Q0tevny5YMGCmTNn0h3IG9HR0ZMnT2axVG8HBABVoXr/vzCYGpr2HLqjAFUiEomMjIz+85//0B3IG/v27aM7BABQcyp5DR4AAABap3pH8ACghIRCYVZWVnFxMZ/Pt7GxcXFx0dDA8QMAnZDgAaC90tLSgoODra2tbWxsCCFcLjc/P3/v3r2enp50hwbQcSHBA0B7hYeHJyYmOjg4SEry8/P9/PwyMjJojAqgg6MwweOUHUAHIRAIrK2tpUssLS1FIhFd8QAAoS7B45QdQMcRGho6cODAiRMn2traMhgMLpcbHx8fGhoql8aFtXUvv9lhsWg2q5OZXBoE6CCoSvA4ZQcqp/7BU80uNho62nQHonpCQkJ8fX2Tk5O5XC4hhMPhJCUl2dnZtVS/tra2oaFBpkQoFL61ckVcCv91efmh0xZL/i3fsAHUG1UJHqfsQLUIyitfbdipP8rNdLZfG95+8ODBNWvWiLebmpoKCwtfvHjB4XBqamoOHTr02WefyTVYZWRnZzd//vx3rDxlyhSZsX51dfWAAQOWL18uU7OpuKQmLcP626XFkT/V33us3buHfMIF6ACoSvCUnrIDkLvyw/F6HgNrLt8y+MiDbWv1vm8PDAwMDAwUby9evDgvL4/D4WRnZ//www+1tbUdIcG/l+TkZJkSV1dXc3Pz5jXL9p8wmvwRq5OZycyJZftOdP5uGcGtPADvhqoE/76n7CIiIh4/fixd8t///nfw4MEUhQcgrfFZXl3WA5ufvmLbWpftO275dVibm0pISDh9+vTt27cJIbGxsRUVFZqa6j+J8q5du9LT05uXx8bGtqfZursP+YUvDb6cSwjRGzagKuVSVepVg4/c29MmQMdB4V30hoaG8+bNYzAYt27dun//fnV1dSuVvb29XVxcpEsyMzNbfwvA++KXVpTu+o0IBH8rFYkan+YzzYxLtuwRiUjj41zu0k1MA32Z92o7OxlN+qj19gsLC+fNm3fq1CljY2NCyMaNG1NTU6Ojo+X6IZTRrFmzMjIyeDxeSEiIvNoUCYRl+45r9uhS/ef/n8zX7NK54kiCnvsADV0defUCoMaoSvC7d+/etm1bdnb2li1boqOjhw8fHhERsXr16nnz5r21voeHh0zJ999/r6Wl1XovgrIKho427oqCd8Q0MjCaMFrmVpC6uw+aXleYfDqFMBiEkIZn+VVJacb+ExkspnQ1lplx640LBIJPPvlk8eLFbm5u8g5c2enp6S1atOjIkSOjR4+WW6N8vnafnkQoaHyWJynTHdJPVN9IkOAB3gFVCX7Dhg3Z2dlsNnvnzp1ZWVmmpqYlJSXDhg1rKcG3TcXxZE17O4OPZAcHAG/FYDG1+zrKFJbtOy6sqi7dfURSIijniWpqdIe/3xWitWvX6ujofPnll3IIVAU5Ozs7OzvLsUGGlqbZvGlybBCgo6EqwVtYWDQ2NhJCjI2NxfPbMJnMf3rTexPxBaK6hn+uB9Ayq8gvRA2y3yKmmcl7NZKWlrZv377bt28zGAz5hQYA0HYUHsEPGzZswoQJPXv2HDly5NixYxMTE8PC2n7v0tsJBMKGRjm3CR0M09iAEIN2NrJly5bq6mp39zf3f6WmpnI4WNcYAGhDVYL38vK6fv36mTNnjI2NnZycLCwsTpw44eTkJN9eRAJB82MvAMVLSkp6a/no0aPleVkaAOCdUXgXvbGxcVBQEHXtE0JEAqGwHkfwAAAAslR4yoj6e4+JQCjCKXoAAIBmVDXB1919WPz1j/yyClE9TtEDAADIUskELxIIyw+cMPQZ2ZRfLESCBwAAaEYlE3zV+UsaRvqms/0YOppNha/oDgcAAEDpqF6CF9bUVp5IES/5xbYwE5SWCcoq6Q4KAABAuahegq84kqg3dIAmpzMhhGhoMPR0yn+LpzsoAAAA5ULhY3JUEPCqeecvsS0tuMu+I4TwC4tFfH51+nXjaR+zOpnRHR0oKV1dXR6Pp1RzzJmZmWlg2dO2EQqL1/xoNGWcjksvukMBUGoqluCZhvqdt6wgfL74ZckPBwQVVdbfLGRZmNIbGCgzIyMjrEyoNqpS/8svrSjbF9d5+yoGBRNgA6gN1TuG0OR01rTniP8xWCxRE1+ziw1RpoMzAKCIsKau4mhSp2XzWVbmVUlvWYEeACRUL8FLEwkEoqYmIhTSHQgAKELF8STdIc6aXW1M5/hVnjwvqODRHRGA8lLtBE8EQkKIsLGJ7jgAgHJNhS9rLt4wnjGeEMK2stAbOaTiaCLdQQEoLwqvwQuFwqysrOLiYj6fb2Nj4+LiIve7ikRCISFEVN9ItLXk2zIAKJuymJOaXW1qM7LEL5lGhuVJ8QZenppdbegNDEA5UZXg09LSgoODra2tbWxsCCFcLjc/P3/v3r2enp7y7EYgYLBZovqG9i/3CQBKTsuBI6jgNT7Lk5QYjBpKYzwASo6qBB8eHp6YmOjg4CApyc/P9/Pzy8jIkGMvIoFAQ1dH2Ij1ZgDUn/F0H7pDAFAlVF2DFwgE1tbW0iWWlpYikUi+vYgEQg1dbaw3A6DeGh7nippwqw3A+6HqCD40NHTgwIETJ060tbVlMBhcLjc+Pj40NFTO3QiEGro6WDEWQI3xX5cXr/nRaPJY46nedMcCoEqoSvAhISG+vr7JyclcLpcQwuFwkpKS7OzsWqqfm5tbWloqXVJTU2Nq+g/T14gEAoaujrAeCR5AbZUfOqU/fHBVUrr+h24scxO6wwFQGVQleIFAYGdnN3/+fEJIZmbmvXv3amtrW6kfGRn5119/SZfk5ubq6ur+YzcaejqiBpyiB1BPDQ+fNTx8bvPjV0xTo/LDZyy++JTuiABUBlXX4K2srMQb27dvHz9+fEpKipeX18GDB1uqf+DAgZt/16dPn06dOrXei0gg1NDVEeIUPYBaEonK9sWZzJrM0NI0mjy24cGz+vtP6Y4JQGVQPtHN1q1bb968eejQoRs3bqxfv16eTYtEhBANHS0RTtEDqKOq1P8SFktvWH9CCEOTbfLJhLJ9x4m879UFUFeULzZjbm4uvp3e3NycKdeVIUQCIYOpwdDSwil6ALVUGZdCCOEu3/K/AlFjbmHt7RzdgR/QGBWAqqAqwbPZ7D59+nA4HB6PFxMTExgYOHfuXA8PD3n2IRAQJlNDSxM32QGoJcvIcFFtnUwhm4N56wDeCVUJnsvl1tXVvXjxIjc318rKSiQS2dvbL168WF7tV6dd45dVMJhMhpamiFclr2YB/o+9O4+Lqt7/B/45szEMMDNsArKk4IIb4ZYLaqaW5kYmpkRhmcKF0Kysm1ezrNSWW/T9ea2uW3qza+7LZSlT0dSbqChIKe7KyLDJMiyzMeec3x/jRRx2nJkzM7yeD/848+Hw+bxRju/P55zP+XygA2iarr8/Z5xUO2TIkN69ez9itUJf70cODaDzsuAzeGdn59DQ0EmTJhlXoV+2bJmzs7NZaqarayq2Haj6z1FCUZRYhGfwANxq76RaALACiz+Dt4TKn1JdRg2mRKKqlKM8JyfMogewEcZJtX5+fqWlpREREbGxsU2e9sEHH1y+fLlhyfXr1wUCu/zvCMBm2d8VVacoVJ/O9v96Oa3WVh08XFdUgkl2ADaijZNqx48f37//QxPlLly4YK47fABgZH8JvnzLHvkLk3luLoxGy3N1qT5yWujrxXVQAJ1aeyfVNt5V8u9//7tYLLZwmACdi50leHVmju6mQjp1nObiFUO5iuLzWIYxlJS1/p0AYDGWnlQLAB1gZwmerqpx6h5Q9Z8jhBBWp6drNaLHuhpKyrmOC6CzM06qDQ0NNX5ctmwZt/EAgJ0leLenI9yejjAe6/OV977e4r3olaKP/8FtVAAAALbG4kvVWhDNED6PEouwXSwAAIAJO07wLE3fX+gGCR4AAOBhdpzgCU0THo/nJGJ0euw/AeDYak9lKd/9jDAM14EA2A07TvAszVACPuHxKD6fNRi4DgcALIXV6St+2M/q9NWH/8t1LAB2w44TvPEZPCEEq9UCODbVvkPivj26LJlfuSOVqa7lOhwA+2DHCZ5laIrHJ4Tcv0sPAI7IcK+i+peT8henCwP9JMPDK3encx0RgH2w4GtyDMPk5OQUFRUZDAZ/f3/jljPmbMBAEwGfEEKJnVgtVqsFcEwVW/e6TRkr8HInhLhHTyt442PX8SNFQV25jgvA1lkqwWdkZCQkJPj5+fn7+xNClEqlQqHYtGlT4yUqO4xlGIrHIxjBAzgubd6N2swceYCvat8hQlGEEIGvd8W2Az5/S+A6NABbZ6kEv2jRotTU1JCQkPoShUIRFRWVmZlptjbqn8E7OWG/GQCHxJM4y6aPV2fmEIpyHtiXECLuE8L3lHMdF4AdsFSCp2nauKlUPR8fH9asL7OxNEPxeYQQwudhkh1AuyQkND0C/vbbb60cSctEQV3Z4eE1v50lhJWMGOgUEsR1RAB2o4kEn52dnZ2dHRMTU1RUFBgY2LF6k5KSBg8eHBkZGRAQQFGUUqk8ePBgUlLSo0X7EONCN7UnzuouXTeMGmzGmgEc3tSpU7kOoW1YtmLLXvcXpxGGKd+0y2/VW8Yb9QDQKtMEv2XLljVr1mg0mqioqNGjRy9fvnz+/PkdqDcxMXHatGnp6elKpZIQEhQUlJaW1kJ34c033/zjjz8ally5cqWVSXk0TQhV8eNBnsyt9vgZt/EjOxAnQOc0ZcoUkxKGYZYvX964nFs1v51l9HWuTz5BCKk+dLL2ZJbL6CFcBwVgH0wTfHJy8unTp2NjY11dXbOzs4cMGdKxBE8ICQwMjIuLMx6vX7++5ZsBc+fOvXfvXsOSpKQkNze3Fr6FZZi6fKW4bw8iEql/P6+7kY/bdwDt8vnnn3/99dcVFRU+Pj4lJSVPPfUU1xE9hNXpK7f/x2vxK8ZRu8e8qNKvNkueCKOcRFyHBmAHTBN8bW2tWCw2HkskEqFQ2LF609LSSktL6z9+9NFHTk5OhJC5c+c2eX54eLhJiUwmE4lauoyZymp9QbH3e/HV6cfF/XuXb97t98mbuH0H0Hb/+Mc/Ll++/Le//S0xMbGqqur777/nOqKHqA4cZg205twfmnP3b++xBoPq4BH5rGe5DQzALpjeA4+JiZk+ffqtW7c2b948YcKEmJiYjtX7559/xsXFHT161PhEX6vVGg8eOeAHan+/IOruL/Byp5xEoiA/YjDU/n7BjPUDODwnJyepVDpw4MDMzMwRI0bk5ORwHdFDnEJDpFOf4rlK6v9Ip44ThwZzHReAfTAdwa9cuTI1NfXkyZMFBQWrV68eNWpUx+p95513Ro4cuXTp0tjY2PHjxx8+fDg5OfmRo31Ad+22/vZdQRev0i831SlL2Lo6SiSq+GG/y4iBGMQDtNGYMWPmzZu3dOnSWbNmFRcXy2QyriN6iHNYb+ew3maskNXXER6PEvDNWCeAzTJN8GVlZcOHDx8+fHj9R09Pz45VHRERkZKSsnDhwpSUFJ2531MXdu3iMmYoq9FJRg7SZl823Ct3nRDBcxIhuwO03caNG69evdqrV6+vvvoqKyvL1m7Rm13pl5t4MjevxA7emASwL6YJftKkScYDlUp17dq1xx9//FHuq0ul0q1bt/700095eXkdj7EpPBeJKMCX0epdRgxk9XrmrJpRVbtMMtsyeQCdQXl5uZeXV1lZWXh4eON5MA5Gk325TlnM3FTob+aLgjEhFxyfaYI/e/Zs/XFubm5sbOyjtzFnzpw5c+Y8ej0m6he64Tk5aa/erj170alXd1FwB1/cB+iEzNuh5xZbZ6CEza7cxdJM+da9Hq/MpCuryzbifXroFFp60XzAgAF1dXVWC6XdGIbweISQusJSpqbWY+7Msk27iFkXywNwbGf/5+rVqxcvXqTsNuex+rqCNz6uPXGuuROq048J3KXOg/u7jhtOGKb2VJY1wwPghGmH980336w/zsvL693bnDNczIulaZ5QSBim+sh/hd6e0mfH1B7PrP3vBZeIQVyHBmB/bL1D3yLVwcN8N5eKbfubfEueqVGr9v3q++EiQgihqPvv0w/F+/Tg4EwTfMNp81OmTLG1hS8eQjOEz6s+/F++WMwS9v51m/y9ZEh/XLcAbWFHHfoW0OWV1anH/T57p2J7imrfIfkc01V4K3emET6/Ku1YfQmj1VWlZMhmTmxcm+FeRV2+0nlQP4vGDGAFDxK8cZ+3gICAhl8+d+7csGHDrB1U27A0Q2i6ckeqx6szK348yNSqhQG+osf8VQcOy1+YzHV0AHbAnjr0zSv/Yb/bpNGCLp4eL0cq3/7UddwIQZeH3v2RjBgoDHxo7ytRcFBz83XKN+/S5OQFrF3B98CedWDfHiT4r7/+mhCiVCpPnDgRFhbm6uqamZk5Y8aMnTt3chdeixi6rriMNdBl63cyas3dxA+NxYaKKiR4gJbZXYe+Obqrt3SXb3j95UVCCN9D7jblyYof9nu//VrDc8R9QsR9Qpqp4CHa3Kv6O0rpxNEV2w54LWp62U3zoqtqeGInStTBNUMBWvAgwW/fvp0Q8vTTT1++fNl4p+7WrVv1i8nbINZAO4UEeSW8SBjmdvTioC2fYVosQBvZX4e+GeXf7+F7ulfuSjd+ZPV1taezpXk3nTqw4B3DlH+/2+OV553DQgve+Fibd0Mc2qZuQccxTPFH/3Dq2c0z3vzvGQGYzqK/c+dOr169jMfdunUz7gVno2iGGPeD5/F4Tk6MWst1QAB2Y/v27du3bxeJRJcvX87Ozj558uTVq1crKiq4jqvd3J4ZJRk6oH4tW76HzD1mOt+zI3fXq34+wZO5GiffyV+cXr55t6Xfyqk+8jslEqrPXtTfLrBoQ9A5mU6yGzFiRFRUVExMDMuy27dvHzp0KCdhtQXLMNT/9pPluTgztWqeizO3IQHYFzN26BmGycnJKSoqMhgM/v7+4eHhrWz3bCauTw1vsrzyp1Sei7N02rg21sPUqlV7f/ZZsfB+taOH1Bw6WXMss7n6Hx2j0VbuSvN5L153Pb98407fjxfjHiSYl2mCX79+/Xfffffvf/+bx+M9+eSTNn6Lvn5NaZ6LM6PWcBsPgN0xV4c+IyMjISHBz8/P39+fEKJUKhUKxaZNm8aM4WZxybqC4upDJwhLJCMHCdo2mq89dZ6urFa+tbphIUvTlkvwlTvSJIMHiIKDRN0Cqg+dqD2d7TJioIXags7pQYJfuHDhxx9//P777xNC/Pz8CCFXr15dsmTJ2rVrOYuuZQxNeP9L8BIJU4sED9A+5urQL1q0KDU1NSTkwRNrhUIRFRVlnMrX2K1bt8rLyxuWVFVV8fn8rKz768+Ehoa6uLgQQiorK2/cuFF/WhvLyzfvcgrrzmi0zOadj70T16Z6PJ3J0lcblyuzsh49nsblpVevn9170Putebezsggh3WKmVfxzh2RQP5VGbZb6UW535bm5uQaDgZjVgwQ/YcIEsVhcv3Sl7atfqpbcv0WPBA/QVubt0NM0baykno+PD9v8A+yVK1f+8ccfDUtu3bqlUCji4+ONHz/66KPJkycTQlJTUxtuRNmW8i9XrabLKoUBvoSQRb69X5g6XtwnpAP1WLR898rPvj19iPfyMWP5Xyc+N1LDq/71ZKqq0KbiRLnVyj/77LOqqipiVpTJRZiXl3fq1KnXXntt+fLlx44d+/TTTzu8Y+wjPpMbNmyYt7d3SkpKcyeUfLbeddxwydAwQsi9ddvEfXtY7mYagPXdvXt3xIgRCoXCEpUfOHBg4sSJR44cMSmfMmVKB2r75ptv1q5dGxkZGRAQQFGUUqk8ePBgUlJSfcJuVavXexuxBlr51mqPV2c6D+xLCKk9cVb1n6NdP3vX1h5v627kG0rKTAqdQoJM3uCHzsMS17vpM/g33nhj3rx5Z86cOXny5Keffrpq1ar09PQO1GuFZ3IsTVP8Bs/ga9XmqhnA4UVGRhJCQkJCTDr0HastMTFx2rRp6enpxml6QUFBaWlpgYEc7PxU/fNxRqsz3Kuo/vUUIYQQ1lBcVnP8jOtY23q/3ykkyCkEO9qBZZkm+KKiotmzZ7/33nsvvfTSqFGjbt682bF62/tMriPqX5MjhCdxxmtyAO1lrg49ISQwMNAW5uTynJ0lg/vpb+bXl7iMHNjCLnMADsz09753795vv/32zp07z549m5ycLJVKO1Zve5/JtQ/LFq1cy9SqqQeT7JwNpab3uwCgZebq0NsO1/EjXMeP4DoKAJtgmuDXrVv3ww8/7N+/39fXt6yszLi8XQckJSUNHjy48TO5Rw6YEEKqj/5uKCyha9Ts/x6r8VycmduYZAfQPubq0AOADTJN8N7e3uPGjcvOzg4LC4uPj+/wU7T2PpO7efOmySpatbW17u7ujc9kNNrKHald/hpf/NFaTVauc/9ehBCeqwTP4AHay1wdegCwQaYJfsuWLWvWrNFoNFFRUaNHj16+fPn8+fM7VrWvr29sbKxYLFapVGfPnm15q+nVq1dnZ2c3LLl9+7bxBUETlbvSJYP6O4UECTzdazIyZc9P5Lu54jU5gA4wV4ceAGyQ6XtrycnJp0+ffvzxx11dXbOzszs8q3bbtm0eHh4hISFffvnlE088kZycHBERsWnTpubO37hx47mH9evXz9vb2+Q0Q9G92oxM+ZwphBDC5zmH96nckUaw0A1Ah2zZsmX27NkrVqzQ6XSjR4/euHEj1xEBgNmYJvja2lqxWGw8lkgkQmEHNzF8//33c3Jy8vPzt2/fvmTJktTU1Nzc3A53F+qVb9kjfKyr+mxu9a+nGFUNX+ZW/etJfb4Sr8kBdIC5OvQAYINMb9HHxMRMnz69sLBw8+bNW7ZsiYmJ6WC9AkFwcDAh5JVXXpkwYQIhRC7vyP5OJoSPdWWqaozvwDA6HV1R5TZuBDHQuEUP0AHm6tADgA0yTfArV65MTU09efJkQUHB6tWrO7yM3ZgxYyIjI999913jzPlLly6tWbNm5MiRjxiue/S0+mNt7lX5i9OEvt6EEMKyjF7fcPFaAGiVuTr0AGCDmlj/YcqUKfXLVVZXV7u5uXWg3g0bNuzbt49hGOPHmzdvDhky5C9/+UuHA22s4XaxhKJ4zmJWraHcmpiXBwBNMleHHgBs0IMEf/78+aVLl5aXl0+ePHnhwoVLly7Nz883LibfgXp5PN7MmTPrP06dOtUMwT6s4XaxhBCei4Sp1fCQ4AHawywdegCwQQ9uaL/66qsjRoxITk5WKpVhYWH+/v5Lly49ffo0h8G1osF2seT+lvCYZwfQJufPn584ceLQoUM/+OCDe/fuLViwYOLEiT179uQ6LgAwmwcj+IqKig8//JAQ0qdPnwMHDhiPbZnJE3fMswNou1dffXXGjBkTJkzYunVrWFhYXFxcTExMt27duI4LAMzmQYLn/29nNk9PzyZXmLE5DTabIXgVHqA97K5DDwDtZcdzzhtuF0swggdoD/vr0D/MUFJWkLSSLldxHQiA7XqQ4O/cueP2Pw2POQyuFTRNGiZ4VwmNtW4AOoeKH/azhK3490GuAwGwXQ9u0Ws0djb8NX0GLxGzajv7EQC4YuzEG49ra2vrj6urq7kLqq20eTd01277ffau8p3PdNduO/XsxnVEALboQYJ3cnLiMI52M24tT1H1BTyJxFBYwlk8AHbF7jr0dHUN382VEEJYtnzzHve5z/Nlbu4vTiv/frffqrcb/lcAAEb2+gyeNdAmi9ZhOXqAtnNqBtdxNU37x9W7ccvrCksJIdWHT1EigcvwcEKI65NPEIatOXGO6wABbJG9JnjCMA0fwJP7z+DtbFACAK1jmPIte8R9e1Rs3ctotJU/pQp9vFQHDqv2/6o6cFjg41Wx7QCr03MdJYDNsdcEbzKFnmAED+Cgqn89xXN16bI0oU5ZrM2+7DpuBN9dxtSojX8E3h6uY4ex/1sVGwDqNbEWvX14+CV4QghPgtfkABwNU6uu3JXus/x1SsB3f3lGxY8Hun651KRzDwBNcqQRPBa6AXA0lTvTJMMeF3XzJ4RIhg4QeHtU/3KC66AA7IMFR/AMwxj3qjEYDP7+/uHh4Tye2foTjXeGxS16AAdjKCmr/vmEU98epV9uMpYwterKnWlu40dSTiJuYwOwfZZK8BkZGQnwvMBgAAAgAElEQVQJCX5+fv7+/oQQpVKpUCg2bdo0ZswY8zTw8Co3hBCe2InQtMkWcwBgv/gyN6/Fr5gUUjweJRJyEQ6AnbFUgl+0aFFqampISEh9iUKhiIqKyszMNEv9D20G/z88iTNTq+bLbHj1PQBoM8pJ5DJiINdRANgrSz2Dp2naz8+vYYmPjw9rXJ3GLAw0aTRSx3L0AAAARpYawSclJQ0ePDgyMjIgIICiKKVSefDgwaSkJHPV3/QI3kWCx/AA1kfTdP3uNRcuXLh06dKQIUN69+7NbVQAnZylEnxiYuK0adPS09OVSiUhJCgoKC0tLTAwsLnzFy5cmJeX17AkLy+Pan75SYOyxOQ1OUII30OG3aUArM/X17e0tJQQkpyc/Pe//33cuHHLli376KOPYmNjuQ4NoPOy4Cz6kpISHx+f2bNny2QyY0lKSsrUqVObPDkuLq64uLhhSVJSUv03mtDfLij5cpPQ19ukXODpbiirfOTAAaCDvvzyy3Pnzvn5+ZWWlkZERCDBA3DIUgl+zZo133333ahRoxYvXrx3796BAwcSQhYsWFBYWNjk+QMGDBgwYEDDEplMJhQ2PVe2/PvdzgN6afNumsyZ53vK6fIK8/0QANA+Xl5exsk3Xl5efCxHA8ApSyX4devW5eTkeHp65ubmzpw5Mysry1xby9f+foGuqvF4bZb+y01VqRmyyAn1XxJ4ytV3CszSCgC0nVAo7NevX1BQUFVV1datW2NjY+fPnz9q1Kjmzn/99devXr3asMTkCR0APDpLJXg3NzcXFxdCyIABAxISEhYvXrxp06ZHr5atq6v4Yb9nfDTFEkHXLlX7D7uOGcp3v38nn+8pp3GLHsDqlEqlRqO5c+fO7du3fX19WZYNDg5+6623mjv/9ddfN87OqZeUlOTu7t5qQ4xaQyiK5yw2Q9AAjs5SCT46OjoiIiI+Pj4uLm7x4sXPP//8nDlz1OpHneKuOnDEKSTI+fFQTfZlnljsOm54xU8pXgkxxq8KPN0N93CLHoADzs7OoaGhoaGhxo/Lli1r4eS+ffv27du3YYlMJhMIWv/vqDT5e0ok7PLOgkcJFaCTsFSCX7FiRURERFlZGSGEoqjdu3fv3LmzLT30FjAabdX+XwW+XsUfrWWqaurKVYxGo7t2R/bcM0I/b0II30NOV6gIy5Lmp98DgKWNHTv22LFjZq9Wc/5PQ3EZS9Pa3KviAb3MXj+Ag7HgLPrx48fXH/P5/Ojo6Ojo6EepkCd26vK3BNZAE0J0V26S7Mvy2VMoigi6eBhPoIQCnsSZVtXw5VjMDoAzNE2bvU6Wpsu37PV4dSar15dt2tn1y79RjV6UBYCG7Gq7WIoS9+1hPGTVGv2dAuewh1bSqP39Amug6fIKJHgADlni7bjq1GMCP2/ngX0JIdWHTtX8etJtkpk2tgBwUPbaBW68XSyr01ds3cvW6dWZF7mKCgAIIQsWmPkZOV1Vo9r/q8fcGcaPHq/OrNyVzlTXmrcVAAdjVyP4BhpvF6vad0jctwet0VX9elIWNYkS2uuPBgAmVHt+JoRUbk+pL2EZRnXgsPtLkdwFBWDr7DYLPrxdrOFeRfUvJ/2++Gvtb2frFIXV6cel08e38N0AYEdcx41wCg1pWCIZOUjo78NVPAB2wV4TvMlmMxVb97pNGSvwcud7uQsD/FT7fnUZM5Qvl3IYIQCYi+gxf9Fj/lxHAWBn7PUZfMPtYrWXrqvPXOQ5i6t/PaW/dddQUMT39qjckcptgAAAAByy2xF8w2fwLOs6bnjd3UJCCKPRGlRVrv17Cny8uIwPAACAU/aa4AlDE979Eby4X09xv57GY7auLj/2Xc+4OVjrBgAAOjN7vUXPGkxn0RtRQiFP7ETj/RkAAOjc7DXBE+ahWfQN8b3kNFakBwCAzs1eE3zj9+DrCTzdDdhTDgAAOjcLPoNnGCYnJ6eoqMhgMPj7+4eHh/N45utP0AwRNF0b31NOl2EEDwAAnZqlEnxGRkZCQoKfn5+/vz8hRKlUKhSKTZs2jRljnuWjWYbm8YRNfkngKTeUYwQPAACdmqUS/KJFi1JTU0NCHiw+pVAooqKiMjMzzdMAzZDmbtF7e6jP5ZqnFQAAAPtkqWfwNE37+fk1LPHx8WFZ1lz1t/AMXviYv/7WXXM1BAAAYI8sNYJPSkoaPHhwZGRkQEAARVFKpfLgwYNJSUlma4Budha9KMCXLlcxag1P4my25gAAAOyKpRJ8YmLitGnT0tPTlUolISQoKCgtLS0wMLC58y9evFhSUtKwRKVSyeXy5s5vvF3sAzyeMMhPf7ugfvN4AACAzsaCs+gDAwPj4uLaePKGDRvy8vIalhQWFraQ4Ft4Bk8IEQUH6W8qkOABAKDTspWlateuXWtSMmzYMC+vZteTZ2maav6lO6fugdpL18wWHAAAgL2xVIL/7rvvjh8/3rh8+/btZqmfpRlK0MwtekJEwYGqlKNmaQgAAMAeWSrBv/zyy5mZmVVVVYmJiRZpoMFmM40JA/0MJWWsTk85iSzSOgAAgG2zVIJ3cXF58803f/rpp/Hjx1ui/hZekyOEUAK+0N9Xf6fAqVd3S7QOAABg4yz4DD4sLCwsLMxStTf/mpyRU3Cg/qYCCR7AOiy7NDUAtJ+tTLJrr5ZH8IQQUfcA3c18V52e8ChK2PSitgBgFpZemhoAOsBeE3zLr8kRQkQhQdWHTxV/+k+Bp9wr6WWrxQXQCVl8aWoAaD97vYfGMjTV/CQ7QogoqKvhbjFdodJcvKK7fsdqgQF0QpZemhoAOsBuR/AGmjT/mhwhhPB4LI+SPBEm7OpTvnmX36q3CUVZKziAzsXiS1MDQPvZ7Qi+tWfwVQePCAN86xSFrk8+QRi25sQ5q8UG0NkkJiYeOnQoODj43r17paWlxqWp4+PjuY4LoFOz2xE8w5Dm5+jSldVV/znaZUVS8Qf/x9RqPOZFlX65SfJEGE/sZM0YATqPdi1NDQBWYK8JvqXNZgip3JFK+Lyqvb/wnJ2KP/6HoIsno9ZWpWTIoyZZM0gAaNKpU6eM21DVKy8vl0qlXMUD4JDsNcG3PIve7emR4rDehBCBt4c6+7Jk5CDJyEGiQL/mzgeAR9HepanT0tKuXXtotwiNRuPi4mKR4AA6K3tN8C2P4EXBQaLgIEKIZEj/6qO/i3sH8z1kVowOoHNp79LUq1atMil59913vb29LRAaQOdlrwm+1ffgjSihUDI0rCbjtGzmRCsEBdA5WXppagDoAPudRd/SdrENyV+YXJWaYSgpa+kkhtHk5LV0AgC0KCwsbPXq1VxHAQAP2G+Cb2m72IYE3h7SKU+Vbdhh/MjUqhufU33k9+JP1mE9HAAAcBj2muBb3i7WhDRygqG0XH32IqPRFrzxSc3R3x+qqVZTuSNVOvWp8s27CdbeAnhkY8eO5ToEALDbBN/qQjcNUQK+Z9zssg07yjfvFnb1qdiewmi09V+t3JUuGRrmETuDMEyr6+Hc+39bazJOdzzuR8DWGThpF6C9aJrmOgQAsNsE3/pStQ8T9+0pj3q25limdOpTzgP7qvb8bCyvKyqtPX5GPnsyoSiPeVGVPx5gtLrmKtHmXtH8cbXix4NMreZR42+n6sOnlO9+xuL/TbAHsbGxXIcAAHab4FmGaeMku3qa83+6TRhZ9s/tfLm0+sjvdcoSQkj5pl2ymc/w5VJCiFOv7uJ+Pav2/9r09zNM+fd7POfPlgwNq9yV/sg/QTswtZrKn1IpPr/6lxPWbBegYxYsWMB1CABgydfkGIbJyckpKioyGAz+/v7h4eG8dqbkltB0C0vVNqbNvaK/W+j/9mvSyAmV21NIHV3y2XfyWZM1Fy7VKQqr0n8znsbqdIxaK3t+IiUy3UK+6pcTPJmr5Ikwcd8eBW987DZ+hNBaK+cYHyJIp4wt+uD/XEYP4bu5WqddAACwX5ZK8BkZGQkJCX5+fv7+/oQQpVKpUCg2bdo0ZswYs9Tf9ln0RuXf7+G7upRv3kUI4UnE4rBe6rMXK37Yx5e7CXy8hH5dBJ5ynsyN7+ZCScR0hYoncSY8ihI7GZfTYWrVqj0/+6xYSAjhuUpkM54u37LH531rbJZlfIjQNflvfLnUJWJw5U9pngtesEK7AABg1yyV4BctWpSamhoSElJfolAooqKiMjMzmzz/tddey8nJaViSl5c3YMCApmtnWUrAb9f2r/LZk+mq2oYlzoP6SYYMYPV1+jvKuruFhnsV9E0FrapmtTpGp2dq1YQlrFbX8LG38u01lPD+yJ7V6++89HYLq+mZC6PVUXx+wRufEEIIyzIabe1vZ9p196JdeC7O2FfXmig+3yvpJade3bkOBAAcjaUSPE3Tfn4P3cH28fFhm38JbdmyZRUVFQ1Lvvjii549ezZ9NkX5r/2gXfFIhoU39yVBF08ytJmexP9UpRytKygmLNsw30uGhIn7NxOhmWj/uFry5WbXp4bV367Q/nmd7yrxfme+hVpkajV4V9DKBN4eXIcAAA7IUgk+KSlp8ODBkZGRAQEBFEUplcqDBw8mJTV7Tzs4ONikJCgoqIXdpQRe7maLtQ2kU8dZs7l6wgA/9xenNSxxGTWY7y7juUgs1KLlagYAAGuyVIJPTEycNm1aenq6cVPIoKCgtLS0wMBACzXnqIT+PjL/p7mOAgAA7I8FZ9EHBgbGxcVZrn4AAABojr2+Bw8AAAAtsOntYi9evLhr164mv5SamurhYdWpSSqVSigUSiRWfURdVFTk6+trzRbLysrc3NxEIpHVWqRpuqysrEuXLlZrkRBSUlLi5eVlzoUZWqPX6yUSydChQ9v+LWVlLW6B6HBs4XovLCw0mR1sIda5tFmWLS4utkJDDMPcu3fPClexwWCoqKjw9va2dEN1dXUqlcrLy8vSDel0OqlUOmjQIEtc77ab4J988smtW7c2ecGzLLtnzx7jG/ZWo1Kp+Hy+q6v1FpmhabqkpMQ6/93UKysrk0gkzs7OVmtRr9erVCorXLENlZSUyOVya/ZjNBoNIeT27dvt+q558+ZZJBrbYwvXO8uyhYWFXbt2tXRDVru0DQZDWVmZj4+PpRuqq6urqKiwQoLX6XRVVVVW+O9Cq9XW1tZ6enpauiG1Ws3j8Z566iliieudtUMGg4HP51u50cWLFycnJ1uzxTt37gQFBVmzRZZlo6Kidu3aZc0WT506NXLkSGu2yLLs8OHDf//9d2u2uGPHjhdeeMGaLToMq13vlZWVMpnMCg0pFIqAgAArNHT58uXQ0FArNHTmzJmhQ4daoaEjR46MGzfOCg0dOHBg+vTpVmho27ZtMTExFqocz+ABAAAcEBI8AACAA0KCBwAAcEBI8AAAAA7ILhM8RVHdunWzcqNyudzd3arr40okEitMfzVh/R9TJpNZYaqqCU9PzxYWQrYEd3d3uVxuzRYdhtWud5FIZJ13cyQSiXVef3V1dbXO+ylSqdQ6V7FMJrPOC5NW+5/Qov8zUCx2FgEAAHA4djmCBwAAgJYhwQMAADggJHgAAAAHhAQPAADggJDgAQAAHBASPAAAgANCggcAAHBA9pfgv/nmm759+4aFhWVkZFi6rY8//rhv376BgYFr1qyxZutffPFFWlqa1Vr85Zdfhg4d+thjj61fv946jb733nvdu3cPCQnZtm2bFVqsra397rvv6j82bsvsrZu0yNUvkgOw9F+Ulf9prHBpW+1ytsJVbLUr12oXrElDRhb8rbDQLnUWkp+fHxoaWltbe+3atd69e9M0bbm2Dh8+PGjQII1GU1paGhQUlJWVZZ3WT548KRKJtmzZwlrl562oqOjZs2dpaalKperdu7dKpbJ0o8ePHw8PD9dqtYWFhTKZrKamxqItXrx4cd68eXPmzDF+bNyW2Vs3aZGrXyQHYOm/KCv/01jh0rba5WyFq9hqV67VLliThows+lthZyP49PT0yMhIiUTSo0cPX1/fnJwcy7VVVlYWHx8vFou9vLxGjRqlUCis0HpVVdXSpUvnzJlj/GiFFo3bHnt5eUml0tzcXDc3N0s3yufzRSKRUCgUi8UCgYBY+Mfcvn17ZWVl/cfGbZm9dZMWOflFcgyW/ouy5j+NdS5tq13OVriKrXblWu2CNWmIWP63ws4SfGFhYUBAgPE4ICCgqKjIcm298MILcXFxhJDc3Nz//ve/Y8eOtULrCxcu/OCDD+oXW7ZCiwqF4vbt2wMGDAgKCvr73/9OUZSlG42IiOjVq1fXrl0DAwM//PBDFxcXi7a4evXqxMTE+o+N2zJ76yYtcvKL5Bgs/RdlzX8a61zaVrucrXAVW+3KtdoFa9IQsfxvhZ0leIZhKIqq/2gwGCzaHMuyycnJM2fO3Lt3r0wms3TrO3bs8PDwGD9+fH2JFX5ejUZz7dq13377LTs7e8OGDVlZWZZu9OjRo9euXTt27FhaWlpycrJSqbTmP2vjtqzQupV/kRyGw/zTWO3SttrlbP2r2JpXrsP8Vgge8futrGvXrvn5+cZjpVLZtWtXy7VF0/SsWbPkcvm5c+eMO49ZuvUtW7bcuHHj8OHDhYWFu3bt0ul0Vvh5vb29J06caNw3afTo0Xl5eZZuNC0t7aWXXgoNDQ0NDR0+fPhvv/1mzX/Wxm1ZunXr/yI5DIf5p7HapW21y9n6V7HVrlyH+q14xGf4Vnbnzp3+/fvrdDqFQtGjRw+Lzk7697//PXv2bE5aX7x4sXHOhRVavHz5cr9+/crKykpKSoKCgi5dumTpRtevXz958uT6OSwXLlywdIuHDx+un9jSuC1LtN6wRQ5/keydpf+irP9PY+lL22qXs3WuYqtduVa7YBs2VM9yvxV2NoIPCgp6/fXXR40aRQjZuHEjj2fBRwwnTpxIT0/38/MzftywYcPUqVOt1rqRFX7e0NDQuLi4YcOGsSz7t7/9rU+fPoQQizY6b968Cxcu9O3blxDyzjvvhIeHW7rFhhr/lVr6L9kWfpHslAP/01joR7Pa5Wz9q9hqV64j/VZgP3gAAAAHhKEDAACAA0KCBwAAcEBI8AAAAA4ICR4AAMABIcEDAAA4ICR4AAAAB4QEDwAA4ICQ4AEAABwQEjwAAIADQoIHAABwQEjwAAAADggJHgAAwAEhwQMAADggJHgAAAAHhAQPAADggJDgAQAAHBASPAAAgANCggcAAHBASPAAAAAOCAnetty8efO5556bO3fuP//5T65jAQAAO0axLMt1DPDAu+++Gx0dPXDgwDFjxvz2229chwMAAPZKwHUA8JCVK1eKxeK7d+96eXlxHQsAANgx3KK3Lc7Ozrt3705KSvrmm28s2tDx48dnzJhhzW80l4qKitu3b3MYAACAXUCCty2pqamnT5/et2+fr68v17GYzYYNG0JDQ319fV9++WWNRkMI2b9/f2hoaEhIyP/7f//P5OS//vWvgYGBXbt2XbJkCSHkzp07I0aMCA0NrT9z+fLlOp3Oyj8CAIDdQYK3Lfv27bty5coLL7zw8ssvt/Fb6urqmjxu+UyrycrKWr169bFjx65evVpeXv7555+Xl5cvXLjw0KFDFy5c+Oabb/7888/6kw8dOnT06NGrV69eunQpJSXll19+WbNmzYcffvjnn39u3bq1qqrq6tWrhJDevXtb/wcBALAvSPBcevvtt3v16nXmzBlCyO7du/v06fPtt9+mpKTs2rXrhx9+MDl57dq1wcHBPXv2fPvttxmGOXXqVExMTHR09EcffdTwmBCyatWqkJCQ4ODgd9991+TMxjGYVDtp0qT9+/cbvzR06NDffvvN5IT2/oz5+fmvv/66r6+vVCqdOXPm9evXU1NTx44dGxQUJJVKo6Ojd+3aVX+yq6vrxx9/7OzsLJfL+/fvr1arZTLZpUuXFAqFVqsVCoWrVq1atmxZe2MAAOiMWODUnj17Jk6cqFarH3vssfT09OZOO3bs2OOPP15SUlJTUzNnzpxPPvnk5MmTMpksLy+PZdmGxz///HPv3r1VKpVWqx01atS//vWvhl9tWOFzzz3XuNrvv//+5ZdfZln2xo0bwcHBGRkZJicYv7FhVc8+++xjjVy8eNHkRygrKxs6dOi+ffuMSdpYuHnz5vnz5zf+O5k8efLUqVO1Wq1KpYqPj58wYcKhQ4fOnj27dOnSDv5FAwB0MphFz7EZM2a89957CQkJYWFhkyZNau60I0eOqFSqF154gRBSWlpaXV09duzY8PDw+pvV9cdHjx6NioqSSqWEkOjo6KNHjwYHBzc8s+Vqf/zxx/fee6+urm7Hjh1z5849evSoyQmjRo0yqSQtLa3VH/PHH3/88MMPP/zww+eee+6PP/5o+KXGDw5GjBjBMMwnn3ySkZExadKk7777zlg+a9asDRs2/Oc//1m3bt2AAQM++eQTJyenVpsGAOickOA5RlHU/Pnz33///YaPohtzdXWNj49/7733CCFarVav1+fm5rq5udWfUH/MsixFUcZjPp9P03TDr7ZarVQqHT58eEZGxs6dO/fu3btr1y6TEy5cuGBSycSJExsHn5aWFhYWRghhGOaVV15RqVQnTpwwzhwMCAg4duyY8bS7d+8GBgbWf5dxduGIESOioqLy8/MPHDhQ3+k5dOjQE088QdP0hx9+uG/fvo0bN65bt+6tt95q4S8NAKAzwzN47l24cMHNza1hnmvsmWee2bp1a2lpqU6nmzlz5qlTp5o7c9y4cXv27KmpqdHr9T/++OO4cePaW+2cOXPWrFnj7u7evXv3trT7yy+/3G3EmN0JIQcOHFAqlfv3769/L2DKlCmHDx8uLS3VarU7duyYMWMGy7JXrlwxGAwFBQXLli2rqqqqqqpKS0vr06eP8VtYll23bt3ChQtra2vd3d0DAwMHDBhQVlbW4t8rAECnhgTPsVOnTh0/fnzkyJG7d+9u4bTw8PDFixePGDEiJCQkJCTk2Wefbe7MSZMmvfjiiwMHDuzTp88TTzwRGxvb3mqnTZt25syZuXPntqvd5pw4ceLEiRNSqdTNzc3Nze2VV17x9vZOTk5++umnhwwZMn/+/EGDBun1+tDQ0IKCgsTExLCwsLCwsH79+oWGhiYmJhor2blz5/Tp08VicVBQ0KhRo/r167d27dpFixa1NxgAgM4DS9VyiWGYJ554IikpqVu3bkuWLDl9+rRAgIcmAABgBkgnXNqyZQtFUXPnzqUoqk+fPt27d//tt9+6d+/OdVwAAGD3MIIHAABwQHgGDwAA4ICQ4AEAABwQEjwAAIADQoIHAABwQEjwAAAADggJHgAAwAHZ7nvwe/bsqd9lBKBz6tu37//93/9xHYU14HoHMPv1brvvwb/77rsqlWrWrFlcBwLAjXv37r3zzjsKhYLrQKwB1zt0cpa43m13BE8I6dGjx4QJE7iOAoAbd+/e5ToEq8L1Dp2ZJa53u38Gr6HVv5Qe4DoKAAAA22L3Cb6iruxE+WGuowAAALAtdp/gAQAAoDGbfgbfApWhokCTTwgp05fqGO2l6hxCCEVRvV368yj0WgAAoLOz1wR/S33t8L1UQoiO0dYYqtNK9xJChJQwQNzNTSDlOjoAAACO2WuCD5c+ES59ghCi1Cq+ufP5kuCVXEcENk2tVut0Oq6jeEAsFjs7O3MdBQA4MntN8ABtV1FR4evr6+LiwnUgD+j1+qqqKh7PcR4nqVQqqVRKUVRWVtbly5cHDx7cp08froMC6NTsPsGLeCInnth4XKDNv1ST87TXNG5DAluj1Wo9PT2VSiXXgTwgFAoZhnGYBL9+/fqvvvoqNzf3iy++2Lhx4+jRo5ctW7Z8+fIFCxZwHRpA52X3Cd5L5LO0xxrjcZGu4GrNJSR4ACtbtWpVbm6uUCj89ttvc3JyPDw8SktLR44c2VyC37Nnz/Xr1xuWZGRkDBkyxCrBAnQWdp/gCSECyhF+CgD75e3trdfrCSFyudx4W4LP57dwfnFxcUVFRcOSgoICTEoAMC9HSI13NDduq28QQu5obt7TFx8vO0QIkfBdhsojuA4NoFNYtWrVyJEjp0+f3qtXr7Fjxz7zzDOpqakLFy5s7vzExESTkoyMDKkU778AmJMjJPhCbcFtzXVCyD19cS1dazwWUU5D5CMpQnEdHYDjmzhx4pkzZw4cOCCXy0NDQ729vffs2RMaGsp1XACdmiMk+OHuY4a7jyGEZKl+P13x29wA08EBAFiaXC6fO3cu11EAwAMOMokXAAAAGnKoBO/Cd5UKZFxHAQAAwD1HSPAlusKvbq4khIS6Dng54C9chwP2KrVk9z19cce+91//+lf3/wkICKAoKj8//+OPP+7bt29gYOCaNWvMGyoAQKsc4Rl8RV35n9XZarpWwrehpcrA7lyqvhgs6eUl8unA98bGxsbGxhqP33rrrfz8/GvXru3fv//8+fM1NTWDBw+eOHHioEGDzBovAEBLHGEEr2U0LGGv1l7iOhAAkpKSsn///o0bN5aVlcXHx4vFYi8vr1GjRikUCq5DA4DOxRFG8FpGQwi5UvNHuHQo17GATdMz+ovV51iWrS9hCXtbfYNhaUJIsa4w4156juocIcTDyctd4Nnwe71FPt0kPVquv6CgYMGCBfv27ZPL5S+88IKxMDc397///e8333xj5h8GAKBFDpHgaU1XceCV2j+5DgRsnYauPa86TbN0fQlLmFvqGyxhCCHVhqor6j+FlIgQ4iH0chc+lOB7u/RrOcHTNP3iiy++9dZbw4cPv185y3799dfffvvt3r17ZTJM/wQAq3KIBM9o+rqGnSw/isfw0DKZ0D0u6K3mvvrFjRVTfaL6uIZ1rPKVK1c6OzsvWbLE+JGm6VmzZsnl8nPnzmGNNgCwPodI8LTGReAWLOl5vfZymBT7VQAHMjIyNm/efP78eYq6v3jizp07RSLR5s2buQ3MwaSV7BFSoqe9saEUQOscIcFrGLW70LO3azGbk/sAACAASURBVP+82j+R4KHDJHwX547eAfriiy9qamoiIh5sf9C7d+8TJ074+fkZP27YsGHq1KlmiLJz0zE6mqJbPw8AHCPBa2mN2Mm5u6TnDuX3XMcCduz1bn/t8PempaWZMRIAgEfnEAme0WgZzX8rMop0BRpa7cyXcB0RAJjT3sJtxfpCQkiBNp8i1F3tHUJIV6fASN85XIcGYLscI8FraZYu0iq7igPvau/0dOnDdUQAYE5h0iEVdWWEEIZl+BR/sGwEIaTaUMV1XAA2zSESPK124osIITKBe5WhkutwAMDMerjc33n2rvaOgBIMlUfQLP2X3BfGeT3LbWAAtsy+E3xeTe4f1ReKdAW5Vefv6Yu1jPp42SGlVvFsl+cFlH3/aAAAAI/CvrOgkBJJ+K4MyzjzXXgU34XvxhDGiSemCMV1aABgfj1cQg2MoZauMa5WVEvXEEJ4hIeZNwCNWTDBMwyTk5NTVFRkMBj8/f3Dw8N5PDMvfR/i0jvEpfeh0gNPyEdX6MsHyoYV6Qqe8Z5u3lYAwEYIKeF6xVfGY5ql37t8f/fIN4NXBEt6cRcXgC2yVILPyMhISEjw8/Pz9/cnhCiVSoVCsWnTpjFjxpi9LQ2tEVFOhBCpQHYNW84AOK5Q1wFr+28jhBifwRuPAaBJlkrwixYtSk1NDQkJqS9RKBRRUVGZmZnmbYhhaZrQXiLv7pIebgJptUFl3vrBAfB4vIqKivrdX2wBwzD1a945nvXr18fFxXEdBUBnZ6kET9N0/RpeRj4+Pg138TIXDaMR88Q+Tl1nd321QJtfhQQPjXTp0mX37t1qtZrrQB6Ii4vj8/lcR2E2aWlppaWl9R8/+ugjJycnQsjcuXMt1CJFCOUQu10DWI6lEnxSUtLgwYMjIyMDAgIoilIqlQcPHkxKSjJ7Q1paI+Y5G4/dBDIkeGiMoqgpU6ZwHYUj+/PPP5cvXz5nzhwPDw9CiFarzc7OJs0n+IsXL5aUlDQsUalUcrm87S3yKP7SHqsfIWQAx2epBJ+YmDht2rT09HSlUkkICQoKSktLCwwMbO78/Pz8hiMAQkhBQYGrq2urDWkZjZh/P8G78t3UdC3D0jzKccZGALbvnXfeGTly5NKlS2NjY8ePH3/48OHk5OQWzt+wYUNeXl7DksLCwnYleEJId0nPjsQK0GlYcBa9r69vbGysWCxWqVRnz56tq6tr4eT333//zz8f2tA9Ly+vf//+K1asaLkVLaNx5t1/Q4ZH8Vz5rtV0lUzg/ojBA0C7REREpKSkLFy4MCUlRafTtXzy2rVrTUqGDRvm5eVlsegAOiNLJfht27YlJCRIpdK33npr/fr1PXr0OH/+/CeffPLaa681ef7WrVtNStp4wWvpByN48r+79EjwANYnlUq3bt36008/mYzOAYATlpql8v777+fk5OTn52/fvn3JkiWpqam5ubmffvqp2RvSMg+ewRNCpAJ5dR0ewwNwZs6cOenp6VxHAQAWG8ELBILg4GBCyCuvvDJhwgRCSHsfsLVRoxG8tJrGFhQAANDZWWoEP2bMmMjIyFOnTiUlJXXv3v3SpUuvvvrqyJEjzd6QxnQEL8N+MwAAAJYawW/YsGHfvn0Mwxg/3rx5c8iQIX/5y1/M3pDJLXo3gQybSAIAAFgqwfN4vJkzZ9Z/nDp1qoUa0tIa54cn2d3R3LBQWwAAAPbC7peCajTJDmvdAAAAOECCp01v0eMZPAAAgP0neEbTcCtoqUCG1+QAAAAcIcGbvAePW/QAAAB2n+A1tLrhe/DOfImBNegZPYchAQAAcM7uE7zJCJ4Y17rBIB4AADo3+0/wD69kR4yr1SLBAwBA52b/Cb7RCB5vygEAANh3gmcJq2N0Yp64YaGbQIYRPAAAdHL2neDrGL2AEvAofsNCjOABAADsO8E3vj9PMIIHAACw+wTfaIYdwQgeAADA3hO8hlE3NYLHa3IAANDZ2XeC19Lapm/R09gxFgAAOjU7T/BME7foMYIHsL6srKwDBw6oVA8uvZSUFA7jAQC7T/DOTU+ywwgewHrWrFnz/PPP79y5Mzw8/MKFC8bCBQsWcBsVQCcn4DqAR9LkJDsnnpgQVs/oRDwnTqIC6GzWrVuXk5Pj6emZm5s7c+bMrKwsNze3Fs5Xq9U6na5hicFgYFnWwmECdC52nuCbek2OEOLKl1YbqjxF3tYPCaATcnNzc3FxIYQMGDAgISFh8eLFmzZtauH8WbNm/f777w1LqqurKYqybJRtc6328umK4y8H/IXrQAAelZ3fom9qBE/wKjyAdUVHR0dERKxfv54Qsnjx4vLy8jlz5qjV6ubOT01NLX/YoEGDfH19rRhys6oNVSpDBddRAJiB3Y/g5UKPxuVuAikm0gNYzYoVKyIiIsrKygghFEXt3r17586d7u7uXMcF0KnZfYJ3engheiMpRvAA1jV+/Pj6Yz6fHx0dHR0dzWE87XW28tSlmhxCyD19SbFOufXuN4QQJ554dtdXKWITzw4A2su+E7yBNQgpYeNyTKQHgHbxEnXp5tyDEMIj/GqDyngs4omQ3cF+2XmCZ+oEvCYTPF6FB2hJQkJCk+XffvutlSOxEd0lPbtLehJCzqsyVYbyJz2f4ToigEfVRILPzs7Ozs6OiYkpKioKDAy0fkxtZ2ANAqqJH8FNICvWFVo/HgB7MXXqVK5DAADLMs2OW7ZsWbNmjUajiYqKGj169PLly+fPn89JZG1BswZ+0wkeI3iAlkyZMsWkhGGY5cuXNy7vbLydfHq49OE6CgAzMH1NLjk5+fTp048//rirq2t2dvann37KSVht1OwIno9n8ACt+/zzz7t27ers7NytWzdXV9ecnByuI+JeoLjbJO/nuI4CwAxME3xtba1YfH9eukQiEQqbeMJtO5q/RY8RPEDr/vGPf1y+fHnevHmpqalHjhzx9/fnOiIAMBvTBB8TEzN9+vRbt25t3rx5woQJMTExnITVRs3foseGcgCtc3JykkqlAwcOzMzMHDFiBEbwAI7ENDuuXLkyNTX15MmTBQUFq1evHjVqFCdhtVFzI3hnvqSOqTOwdYKmXqIDAKMxY8bMmzdv6dKls2bNKi4ulslkXEcEAGZjmh3LysqGDx8+fPjw+o+enp5Wj6qtWkjhrgK3akOVu9B2gwfg3MaNG69evdqrV6+vvvoqKyvr+++/5zoiADAb0wQ/adIk44FKpbp27drjjz+enZ1t9ajaqrkRPLn/GB4JHqAl5eXlXl5eZWVl4eHh4eHhXIcDAOZkmh3Pnj1bf5ybmxsbG9vhqhmGycnJKSoqMhgM/v7+4eHhPJ6Z97ahGQOf11yCx2q1AK2wrw79o2NYmhCKR9n3JlsAbdTSSnYDBgyoq6vrWL0ZGRkJCQl+fn7GeblKpVKhUGzatGnMmDEdq7BJLY3g+ZhID9AKM3bo7cKB4h1SgWy8V2d/1x86CdPs+Oabb9Yf5+Xl9e7du2P1Llq0KDU1NSQkpL5EoVBERUVlZmZ2rMImtXiLHq/CA7TDo3To7UUdW1fH6LmOAsBKTLNjw2nzU6ZMeeqppzpWL03Tfn5+DUt8fHxYlu1Ybc220sxrcgSvwgO0gbk69ABggx5kR+PYOiAgoOGXz507N2zYsA7Um5SUNHjw4MjIyICAAIqilErlwYMHk5KSHjFcEy2P4O9obpi3OQAHY64OvY37193vSvSFhJBiXaGA4v9Rk00I6e7cc6bfS1yHBmBBD7Lj119/TQhRKpUnTpwICwtzdXXNzMycMWPGzp07O1BvYmLitGnT0tPTlUolISQoKCgtLa2FrWueeuqpY8eOmRQOGjSo5VZaeE0OI3iAFpi3Q2/jxnpOrDFUEUKOlx+S8FyGyiMIIXKhB9dxEULIedVpb5FvoHM3rgMBB/QgwW/fvp0Q8vTTT1++fNl4p+7WrVtxcXEdrrqkpMTHx2f27Nn1q2ekpKQ0t4dVRkaGScmwYcO8vb1bqJ8lLMMyfIrf5FfxDB6gBebt0Nu4IOfuxoM/arKlfGlft8e5jaehnKpzPVxCkeDBEkxfF7lz506vXr2Mx926dTOOvztgzZo1zz///M6dO8PDwy9cuGAsXLBgQYcDbayFB/AEI3iAFm3fvn379u0ikejy5cvZ2dknT568evVqRUUF13EBgNmYJsgRI0ZERUXFxMSwLLt9+/ahQ4d2rN5169bl5OR4enrm5ubOnDkzKyvLzc3tkaN9SAsP4Ak2lANoA3N16O1FP9fHxTxnrqMghJBb6msaWk0IqawrV2oVl6pzCCEeIm9fp65chwaOwzRBrl+//rvvvvv3v//N4/GefPLJDt+id3Nzc3FxIYQMGDAgISFh8eLFmzZtetRgH2ZgDM3dnyeEuAhctYyGYWle8+cAdHLm6tATqyxs9ej6udnKan2H76WoDJWEEKVWUVZXqtDeJoQMcBvk6x3JcWTgQB4k+IULF3788cfvv/8+IcT4htvVq1eXLFmydu3aDtQbHR0dERERHx8fFxe3ePHi559/fs6cOWq12lxxE0Jo1iDgNbuXDEUoN4FMZajEarUAzTFXh946C1s5kgVB919Q/F7xjx4uoaM9JnAbDzikB13sCRMmiMXiSY10rN4VK1Z8/vnncrmcEEJR1O7duyMjI1988UXzRE0Iae0WPSFELnBX1eGZIkATFi5cWFlZuWTJkuvXr/v5+fn4+Bg79B2rzbiwVUZGxrZt27Zt23b06NGjR4++8847zZ3/7LPPejzs3LlzP//8c/3HvXv3Gs/85z//2fA0xytfOOCvU0Jm2E48KOeqvH///vfu3SNmRZksPpOXl3fq1KnXXntt+fLlx44d+/TTT7naMdY4iz4lJaW5E4p1yq9vfbIm9JvmTvh/t1aN8XwmXNrxu44AHLp79+6IESMUCoUlKj9w4MDEiROPHDliUj5lSkeWce3bt++5c+ckEkl9iV6vHzVq1JkzZ5o8X61W63S6hiXjx4/38vLasWOH8aNMJjPe4TcYDNXV1fWnOV75rsJ/BUt6jguaZCPxoJyr8itXrkycOPHu3bvEfExHwG+88ca8efPOnDlz8uTJTz/9dNWqVenp6WZsz4xaHcHLhBjBAzQtMjKSEBISEmLSoe9Ybe1d2EoikTTsDRBChEKhSCRyd3c3OVMgELi7u2epfu/r+rgzX2JS3rhmuyt/TZ7Eo3gUoWwkHpRzVS6TySiKavylR2E6C6aoqGj27Nl79+596aWXRo0adfPmTfO2Z0YtvyZHCJELPCrryq0WD4DdeeONN1xdXRt26DtWT2Ji4qFDh4KDg+/du1daWmpc2Co+Pt5ccaaV7C3SFZirNpvCp/j12R3AvEwTZO/evd9+++2dO3eePXs2OTlZKpVyElZbtGUEj9VqAVpg7NC/9957j96hDwwMfJR1sVqmpmuu1PzRXdLTQvUDOCTTEfy6dev8/f3379/v6+tbVlZmXN7ONrU+yQ636AFaZOzQ//jjj1OnTrXBDn2ZvrRUX1yqL9bSmtuaG8ZjHaPlOi4A+2CaIL29vceNG5ednR0WFhYfH9/C6vGca8Mseo9KAxI8QLPWrVv3ww8/2GaHXk3XfnVrpYGpI4TUGKovVmfdUl8jhEz1mYWXygDawnQEv2XLltmzZ69YsUKn040ePXrjxo2chNUWrT6Dlwnd8QweoAXGDn1ubm5dXV18fHyPHj24jugBCd9lTtd5k7vMnNxlphPfOVDc3XhsI5vEANg+0wSfnJx8+vTpxx9/3NXVNTs7u8Ozaq3AwNS1sNANIUQmkNcYqhiWsVpIAPbFxjv0Bdr825rrtzXXabauhlYZj5Vai7w3COB4TEfAtbW1YrHYeCyRSITCljIot1q9Rc+j+BK+aw1dJRXIrRYVgB0xduhjY2ONHfohQ4bMnz+f66AemOT9nPEgt+p8b5f+sQEJLZxcpi91F3ryKJtbHxeAK6YXQ0xMzPTp02/durV58+YJEybExMRwElZbtHqLnhAix116gObZS4d+qDxCKmylm74+/yu8NQPQkGmCXLlyZWpq6smTJwsKClavXs3VMnZtoabVF6vOtXyOTOBeWVcRZBPbRwHYHGOHvrCwcPPmzVu2bLHZDr23yJfX2hogDGHwPA6goSZGwFOmTKlfrrK6utrs27yai57VGti6ls+RCz1UmEgP0Ax76dCP83qW6xAA7M+DBH/+/PmlS5eWl5dPnjx54cKFS5cuzc/PN+7/yGF8LTCwNGltBSi5EIvZAbTEXjr0TdLQ6o+vvUOzBkJIhaH8m9ufGSfePuM9fbxXRxbVB3AkDxL8q6++OmPGjAkTJmzdujUsLCwuLi4mJqZbt27cxda0a7WXD99LIYQotQoDU/ftnS+M5S/4veIp8jY5WUtrVYYya4cIYPPsrkPfJGe+5J2QjwysgRCy7s5n07xnBUmCCSHYJBqANEzwFRUVH374ISGkT58+Bw4cMB7boC5OvkNkIwkhmexvxboi4zFFUW6CJhbhuqcvKtXb039YANZhLx36VtXnciEllAs9vEU+3MYDYDseJHg+n2888PT0dHFx4Sie1skE7kPlEYSQW5prfCrHeNwcEd9JQ2usFRqA3bCXDj0AdFgrr5nZMpoxNPelEn2Rmq4lhNQxejVTc1tzgxAiFcg8hF7Wiw/AhtlLh77tpnWZ5e8cxHUUADbkQYK/c+dO/fya2tra+uOGu9PbFIqigp2bXllzX+GPxTolIaS8rkxDa7cqvqEI6enSN9r/NevGCABWEiYdwnUIALblQYLXaOzsVjbDsuHyYU1+Kf6xt40HO5TfH7v389vBH7g29YQeoNOyuw49ALTXgwTv5OTEYRwdYGANAqr1hbfEfEmloQIJHqAhu+vQA0B72fG6zXRra9ETQvgUXyKQ4FV4ABNOzehwhVlZWQcOHFCpVPUlKSkp5ogUADrIjhO8ga1rNcFP95ndXdJLVYfF7AAsaM2aNc8///zOnTvDw8MvXLhgLFywYAG3UQF0cnY8i97Qhs1mRDwnD6FXed0964QE0DmtW7cuJyfH09MzNzd35syZWVlZ9rUiHoBDsuMRPN22Z/Beoi6l+mIrxAPQabm5uRnftRswYEBCQsLixYu5jggA7HwE3+otekKIt8jndMVxK8QD0GlFR0dHRETEx8fHxcUtXrz4+eefnzNnjlqtbu78hQsX5uXlNSzJy8ujWtsvDgDapVMkeIzgASxqxYoVERERZWVlhBCKonbv3r1z5053d/fmzo+PjzdZ9H7hwoUymczigQJ0Jvac4BmDgNd6/B5CL1VdZRt7AwDQMePHj68/5vP50dHR0dHRzZ3cv3///v37NyyRSqVCYetP3ACg7ez7GXyrk+wIITyKLxd6lOtLrRASABBCxo4dy3UIAGDPCb7tg/IuuEsPYEU0TXMdAgDYd4Kva8ssekKIFxI8gBXFxsZyHQIA2HOCb+MtekKIl5PPPSR4AGvBEjcAtsCC884YhsnJySkqKjIYDP7+/uHh4TyeOfsTbb9F7y3yOa86bcamAQAAbJylEnxGRkZCQoKfn5+/vz8hRKlUKhSKTZs2jRkzxlxNtCvB4xY9gMO4rb7u7xwkpERcBwJg0yyV4BctWpSamhoSElJfolAooqKiMjMzzdVEexK87z0dEjyAg9iu3DTLb24Pl1CuAwGwaZZ6Bk/TtJ+fX8MSHx8flmXN2USbn8G7CaQG1qChm11XCwDsC0vM+Z8JgEOy1Ag+KSlp8ODBkZGRAQEBFEUplcqDBw8mJSWZsYk2LnRj5CXqck9fEujczYwBAAAA2CxLJfjExMRp06alp6crlUpCSFBQUFpaWmBgYHPnJyUlXblypWFJq2tTt/01OXL/TbkiJHgAO6VlNG/++aqO0Ro/rrm+1HgQ5ffy5C4zuYsLwHZZcBZ9YGBgXFxcG09OTEw0dgXqJSUltbA2NcPSFMWjSFt3p/B2wjw7ADsm5jl/O+An4/Ga60uj/GJ7uvThNiQAG2cry7P37du3b9++DUtkMlkLa1O3d215b5FPsa6w4/EBAADYFUsl+O++++748SY2ad2+fbtZ6m9vgvcS+fxRfcEsTQMA59p+9w6g07JUgn/55ZczMzOrqqoSExMtUb+BNfApftvP7yLyLdEVtX4eANi86K6v+TsHcR0FgK2zVIJ3cXF58803f/rpp4abSJoRzRraPsOOEOLr1LWirkzP6EQ8J0vEAwCWpqHVznwJIaSbpAfXsQDYAQuuRf//27v3oKjuLA/g53bTLx7NW54NGBCajiDGdSQoCcYYt7JGZxMfuHG14k5IYjRljWsqPqZ0NJqaxQpVm9room7MrJNo3JjSVYwG49skJgaRBFQigi0PYZCH0A/o23f/6B2GQUVo+v6ae/l+/uq+dt9zuvseD/f1+6Wnp2/ZskWklQ/2EL2CU0Zoomttt0TKBwBEdb2zfHvNVm9nASAlUp1sZlD3yLkYdAlmW7U46QCAuByCgxcc3s4CQEqk2+AHOoxdjzjtaLO1Wpx0AAAAhpfhcpvcYPGDGcbOxaBLuNT2jUj5AECPwsLCgY+B0b9TzV+eaf6KiKxOa3t3y8brK4lIo9T+9rH1mGwGoH9SbfCDPQdPRAbdaLOtWiABN9gAeFZRUVFTU1PP040bN2o0GiJavHjxENc8ITArwXcMEVVZKi/cPbEw9nUiUnJKdHeARxpBDd5P6a9V6Jq7msLUo0TKCmBk+vnnn9etW5ebmxsSEkJENpvt8uXL5IkGH+CjD/DRE5GF79QqdAm6xEe+BQBcpNrgBz6VXG8G3Wiz9SYaPIBnrVq1Kisra/Xq1YsWLZo2bVpxcXFBQYG3kwIY6aTa4N3YgycigzbBbKseHzhJjJQARrLJkycfPnx4+fLlhw8fttvt/b9469atlZWVvZdUV1er1f0ddY/RGrJDp3sgUYARQ7oNftC3yRGRQZfwfet5MfIBAL1e//HHH+/du/fq1av9v9JoNOr1+t5Ljh8/3s/cE0QU6BM8KSjbA1kCjBjSbfBu7cHrEg7U7xEjHwBwyc3Nzc3N7f81M2fO7LNk165dvr6+oiUFMBJJ9T54987BR6ij2xytVt4iRkoA4JKTk+PtFABAwg2eH9RkMy4KThGtNdy21YiREgC48Dzv7RQAQLINvtvZrVIM+hw8ESX7ma52lHk8HwDosWjRIm+nAACSbfDuHaInIlPAuJ/vlXo8HwDo8eqrr3o7BQCQbIN37yI7Ikrxe/yWtcrutHk8JQAQid1pW3N1qbezAJAY6TZ4d26TIyK1QpPgm3St42ePpwQAInEIjnuOdm9nASAxUm3w7l1k52IKGFfegaP0AAAgZxK+D97Kd7r33sf9x/2X+QPP5gMAHnfDcu140yEicggOu9O2rSbftXxO1KJwdYRXUwOQAKk2eF5wnG7+akH0b9w4Ex+vS2zrbmnrbglUBYuRGwB4xCh11N8FZhGR3Wkrv1fqesxxnN4n0NupAUiAVBt8t9BNgkAkuPFeBadI8R9b3lH6ZHCOp/MCAI8J8NFPDJpMRJ18x766j1yPAWCApHsO3jGUad3HBowvu/ejB/MBgGGltfsursuDEU5ie/A3LZXvVr4t/GXHPe/KXCJScIqVj21I9U8f+HomBD75P/V/7OQ7/JT+oiQKAJ6jVegGO5Vc8Z+P+Cv9/37UP4qUEsDwJ7EGP9p3zK5xXxDR9pr8H1q/3Z6+172b5fx9AsYGjP+25fS0sH/wdI4A4GFKTjkvavEg3yQ43TqFByAbUj1EX2+rFcg5lDU8Hfrc6ebjnsoHAABgWJHYHnwPnniOOBrCaXijfxpP/I3Oa4l+KR5MDAC8aGvV+hrLDSLqcto5TnG08QARmQLGvRG/ytupAbAm1QbvFJxJ/qnujVbbIzt42um7xxP9Uix85/tVv1835t8e+Ra706ZWaIZyfR8AiOethLXdQhcRHbqzV6fwnx4+k4h0Cp238wLwAok1+E9qd/7SWUFEzV2N9xytG6+vJKIYXfy/GN5yY22TQ6atubq0PeqfBUFo7m4aYAJjA8bjdh2A4UmtUKtJTUQqTq1WqHEVLYxkEmvwM8JnZ4VMJaJ/v7k5Rhv/UtRCIvJV+rm3tgAf/VOh0z+r+3hu1EBnt3QI3Q6h271wAMASjrTBCCexBh+qDg+lcCJScAq9Up+gSxzK2m5YrnU42n9s+7bNcdfKWz6+/aFr+ayI+cGqUA+kCzBitLW16fV6juMuXbpUUVExYcKE1NRUL+YzOeQZlVu32ADIhsQafA+nwCvcnWymR4gq7DHfFCWn+rHtWyWnTNAlEZGSU95/SOA/qv9gdVqIqNZWc8t683zLSSIy+Y97ftSLQ8wBQAYKCwvff//9srKy/Pz8nTt3Zmdnr127dt26dV6cGD5KE+ut0ADDhFQbvEqhTg1Ic+ONVZbrBl2CilMTUbAq9OnQ54iosavuRud11+MHmhkxp9PRQURFjZ8n+CaZ/McRUYQm2s3sAeRl8+bNZWVlKpVq27ZtpaWlISEhTU1NWVlZXmzwACDV++A5gUvyc+cA4J9qd9y21vRZ+E9Rv+kWur9s/OJh74rXJZoCxpkCxgWqgmO0ca7HoepwNxIAkJ/w8PCuri4iCgoKUigURKRUDvUAGwAMkVT34HnileSx/0HCNBGTg6d+3fylTunXz348ADzQ5s2bs7KyZs2alZycnJOT89xzzx05cmT58uUPe/2RI0dqa2t7L2lsbPTzc/NqWQB4IKk2+Pbu1qbuBk/tQ6sVmlcMyxrt9e/f3FhluZ4bvUSn9H3gK58IzIzRxnkk6KBUWa4fbzr0evy/sg8N8EgzZsy4ePHiwYMHg4KCjEZjeHj4559/bjQaH/b68vLyX375pfcSHx+fiAhM8Q7gSVJt8LzAdzsHeruahe/8bfmSLqfd9XRT5Soi4ojLjVkyPeyFnpeN0kT9PrlgX93u9ddXzI7InRScG8hU5AAAC3xJREFUff9A9xMCn/RE+oNm5S2dfIdXQgMMRFBQ0OLFAx0uftWqvuPKvf322+HhOOcF4EkiNnin01laWtrQ0OBwOGJiYjIyMlwn5zxCIFIO+Cp6X6Xf9rR9rsebKlctjMkb7Tvmga/UKLSLYl+/2lFW1Hhgf/0fs0Kmjg0Yn+yX6t6UNgAjU05OzqlTp7ydBcBIJ1aDP3ny5BtvvBEVFRUTE0NEdXV1ZrN5165dTz31lNvr7HLa/7v2P/+yI+783zv7XbPFPBn8dIb+V57Jm4iIjP5pRv+0evvt71rOflH/p9u2mihtbIw2bpQ6MkQVplcF+SsD/JT+WqWvD+fj9jA7A/F96/kf2i4QUbujtd5Wu60mn4i0Ct2i2DcG/vcNAGM8z3s7BQAQrcG/9dZbR44cSUz860A0ZrN5zpw533333QNfv23btpqav7m43Ww2azSa3kt8ONV4/a94gRdI+L71vNE/LVoTS0SRg7nhdeCDW0VpYn8dueDXkQtsTmudzXzbVvPnrsaKjrJ2R2sHf8/i6LA6LQ7BYeUtRKRRaHsGxueIe9gp/EFRcj5KUnQJXUTU7eyy8ZZqyy+u9W+ufHvo62dPyflolFpvZzG8KEgxN3qxQZvg7UQ8adGigQ4NCQDiEavB8zwfFRXVe0lERIQgPHR65tDQ0Pb29t5LYmJiTCZT7yUKTvFEYKbr8aE7e03+6WMGf6fcwpi8WF3CoN6iVege801+zDe5n9fYnFZe+P+9FkEQXKPiDBEvOGxOm+txleX6hZZTC2Pyhr5aL+IFh523eTuLYSdcLbeLy3D7O8BwIFaDX7Zs2YQJE2bPnh0bG8txXF1d3aFDh5YtW/aw18+bN6/Pkubm5n4uuvFTBriXWIJvkntv7J/2b6er8ic303uYTsc9nUI3xKF5AQBg5BCrwS9duvSFF144evRoXV0dEcXFxRUVFRkMBk+tf5x+4ogaZyZcE5keMMHbWQAAgGSIeBW9wWDIyxPrkPJIGwR+lDpyevgLj34dAAAAEUl3qFoAAADox7Ae6Obu3btVVVUP/KebN2/6+DBN3m63K5VKxkE7OzsZj99psVh0Oh3HMZ1Im/3HZB/R6XTqdLpRo0YN/C0NDQ3i5TMMDYd6Z7ZhINAwDyQIgtVq9fX1wP1Q/XM6nX5+fmFhYWLU+/Bt8KNHj966detnn332wH+tqqpSqZgOPsPzPMdxHhyr55EEQeB5nvGfFCPkYzocDqVSyfLvGKfTqVQqY2MHN4dpenq6SPkMN8Oh3gVBcDgcbAKx2eblF8jpdDqdTpkF8vHxcQ0Y4/l6FyTI9b8z46ArVqwoKChgGbGmpiYuLo5lREEQ5syZs3//fpYRz58/n5WVxTKiIAiZmZnffPMNy4j79u2bN28ey4iywazeW1tbAwMDGQQym82xsbEMAlVUVBiNRgaBLl68OHHiRAaBTpw48cwzzzAIdPDgwVmzZjEItGfPnpdfflmkleMcPAAAgAyhwQMAAMgQGjwAAIAMocEDAADIkCQbPMdxarWacVD298j5+PgolaynjMPHFIlXPqY8MKt3ZlsFs40BgUZ4IE54+AQww1ltba3rvgJmWlpa1Go145un2X/MO3fuhISEsLwFURCE+vr66OhoZhGJqK6uLioqiuVtct3d3S0tLYO6Dx56MCsEBHIPsyp2Op137tzpM5OZGBwOR3Nzc0SE6BNBdXV1tbW19TPxylBItcEDAABAPyR5iB4AAAD6hwYPAAAgQ2jwAAAAMoQGDwAAIENo8AAAADKEBg8AACBD0mvwH374oclkSk9PP3nypNixNm3aZDKZDAbDe++9xzJ6fn5+UVERs4jHjh2bOHFifHx8YWEhm6DvvPPO6NGjExMT9+zZwyBiZ2fn9u3be57eH8vj0ftE9NaGJANif1GMfxoGpc2snBlUMbPKZVawfQK5iLhViDRLnUhu3bplNBo7OzsrKytTUlJ4nhcvVnFx8RNPPGG1WpuamuLi4i5dusQm+rlz59Rq9e7duwUmn7elpWXMmDFNTU1tbW0pKSltbW1iBz19+nRGRobNZquvrw8MDOzo6BA14pUrV5YsWZKbm+t6en8sj0fvE9FbG5IMiP1FMf5pGJQ2s3JmUMXMKpdZwfYJ5CLqViGxPfijR4/Onj3b19c3KSkpMjKytLRUvFjNzc2vvfaaVqsNCwubMmWK2WxmEL29vX316tW5ubmupwwiuqY9DgsL0+v1ZWVlAQEBYgdVKpVqtVqlUmm1WtfIoKJG/PTTT1tbW3ue3h/L49H7RPTKhiQPYn9RLH8aNqXNrJwZVDGzymVWsH0CkfhbhcQafH19fWxsrOtxbGxsQ0ODeLHmzZuXl5dHRGVlZRcuXMjJyWEQffny5evXrw8JCXE9ZRDRbDZXV1enpaXFxcVt3bqV4zixg06ePDk5OTk6OtpgMGzYsMHPz0/UiFu2bFm6dGnP0/tjeTx6n4he2ZDkQewviuVPw6a0mZUzgypmVrnMCrZPIBJ/q5BYg3c6nb3HD3c4HKKGEwShoKDgpZdeOnDgQGBgoNjR9+3bFxISMm3atJ4lDD6v1WqtrKw8c+bM5cuXd+zYcenSJbGDfv3115WVladOnSoqKiooKKirq2P5s94fi0F0xhuSbMjmp2FW2szKmX0Vs6xc2WwVTCfUGrro6Ohbt265HtfV1Yk6twHP83Pnzg0KCvrhhx/0ej2D6Lt3775x40ZxcXF9ff3+/fvtdjuDzxseHj5jxozg4GAiys7Ovnr1qthBi4qKFi5caDQajUZjZmbmmTNnWP6s98cSOzr7DUk2ZPPTMCttZuXMvoqZVa6stoohnsNnrKamZuzYsXa73Ww2JyUliXp10ieffDJ//nyvRF+xYoXrmgsGESsqKh5//PHm5ubGxsa4uLjy8nKxgxYWFj7//PM917CUlJSIHbG4uLjnwpb7Y4kRvXdEL25IUif2F8X+pxG7tJmVM5sqZla5zAq2d6Ae4m0VEtuDj4uLe/PNN6dMmUJEO3fuVChEPMVw9uzZo0eP9sxLuGPHjpkzZzKL7sLg8xqNxry8vEmTJgmCsGbNmtTUVCISNeiSJUtKSkpMJhMRrVq1KiMjQ+yIvd3/lYr9JQ+HDUmiZPzTiPTRmJUz+ypmVrly2iowXSwAAIAMYdcBAABAhtDgAQAAZAgNHgAAQIbQ4AEAAGQIDR4AAECG0OABAABkCA0eAABAhtDgAQAAZAgNHgAAQIbQ4AEAAGQIDR4AAECG0OABAABkCA0eAABAhtDgAQAAZAgNHgAAQIbQ4AEAAGQIDR4AAECGfLydAHjNiy++WFJSYrPZWltbIyMjiWjBggX37t374IMPvJ0aAHgY6n0E4gRB8HYO4E2nT59+9913v/rqKyKy2Ww2my0oKMjbSQGAKFDvIwoO0cNfnThx4ne/+92JEyeeffbZtLQ0g8GQn58/a9as1NTUTZs2uV6zYcOGpKSkjIyM3bt3ezVZABgS1Lvs4RA9PMC1a9fKy8srKioyMzN/+umn+Pj4xMTENWvWHDt27Ny5c1euXLHb7ZmZmRkZGRkZGd5OFgCGBPUuV2jw8ABTp04NCAhISUlJTk42mUxEFBwc7HA4zp4929DQMH/+fCKyWq0lJSUoeACpQ73LFRo8PIBSqXQ9UKlUvZfrdLqVK1e+8sorRNTR0aHRaLyQHAB4FOpdrnAOHgZh6tSpH330UVdXV1tb29ixYxsaGrydEQCIBfUuddiDh0HIzs6ePn16enp6d3f32rVrDQaDtzMCALGg3qUOt8kBAADIEA7RAwAAyBAaPAAAgAyhwQMAAMgQGjwAAIAMocEDAADIEBo8AACADKHBAwAAyBAaPAAAgAyhwQMAAMgQGjwAAIAMocEDAADIEBo8AACADKHBAwAAyBAaPAAAgAyhwQMAAMjQ/wGkKu79d0Do7AAAAABJRU5ErkJggg==" /><!-- --></p> -<p>This results in a much better representation of the behaviour of the parent compound Z0.</p> -<p>Finally, Z3 is added as well. These models appear overparameterised (no covariance matrix returned) if the sink for Z1 is left in the models.</p> +<p><img src="data:image/png;base64,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" /><!-- --></p> +<p>This results in a much better representation of the behaviour of the +parent compound Z0.</p> +<p>Finally, Z3 is added as well. These models appear overparameterised +(no covariance matrix returned) if the sink for Z1 is left in the +models.</p> <pre class="r"><code>Z.mkin.4 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE), Z1 = mkinsub("SFO", "Z2", sink = FALSE), Z2 = mkinsub("SFO", "Z3"), @@ -1771,11 +1825,15 @@ FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)</code></pre> <pre class="r"><code>m.Z.mkin.4 <- mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin, parms.ini = m.Z.mkin.3$bparms.ode, quiet = TRUE)</code></pre> -<pre><code>## Warning in mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin, parms.ini = m.Z.mkin. -## 3$bparms.ode, : Observations with value of zero were removed from the data</code></pre> +<pre><code>## Warning in mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin, parms.ini = +## m.Z.mkin.3$bparms.ode, : Observations with value of zero were removed from the +## data</code></pre> <pre class="r"><code>plot_sep(m.Z.mkin.4)</code></pre> -<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAMACAIAAAB0NULIAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdd1xT1/s48JNACDMMQTYKqIAKglQQGVK04EKl4lawFqVStO6PCs46a1vaWkedqFVEQRwMN0KxiguQKrjBQEA2gQAh4/7+yO8b0zAKITc3Cc/75R83JyfnPAGuzx3nnkPCMAwBAAAAQLmQiQ4AAAAAANIHCR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDg5cu7d++mTp0aGhr6xx9/EB0LAAAABUbCMIzoGMAna9eunT17touLi4+PT2ZmJtHhAAAAUFRwBi9ftm7d6uzsXFJSYmhoiGtHGRkZQUFBsvxgfX29WAmXy62qqupuO7W1tUVFRRIEAAAAvQokePmioaGRkJAQGRl54MABomORJiaT6ePjI1YYFRW1YsUK0ZLvv/+e8n9UVVVdXV2Li4s9PDzs7e1/++03QZ3o6Gg2my2juAEAQGFBgpcvKSkpDx48SEpKMjEx6eJHOBxOu9ud15SlnTt3uru7M5lM0cKMjIzjx4+L1dy4cSPn/6xcufK7777btWvXli1bnj9/fvLkSSaT+erVK4SQnZ2d7KIHAADFBAmeSKtWrRo0aNDDhw8RQgkJCQ4ODomJiS9fvpwxY8b8+fPFKu/bt8/GxmbgwIGrVq3i8/n37t2bO3fu7Nmzt23bJrqNENqxY4etra2Njc3atWvFaraNQazZcePGXbp0SfDWiBEjMjMzxSpI8DUDAgJ27twpWlJXV7dmzZpNmzZ19JHHjx8XFhaGhITo6uq+ePGCTqe3tLRQKJQdO3ZERUVJEAMAAPQ6GCBUYmJiQEBAU1NTv3790tLSOqp29+7dYcOGVVRUNDY2zpo1a/v27VlZWbq6uoWFhRiGiW5fu3bNzs6uvr6+paXFy8vr1KlTou+KNjh16tS2zZ44cWL+/PkYhr19+9bGxiY9PV2sguCDok2NHz++XxvPnj0TrVNSUtK/f3/hy9mzZ1+5cuXcuXPz5s1r9/t6e3s/f/4cw7D6+vrw8PCxY8feuHHj0aNH69ev7+5PGAAAeidVog8werugoKB169YtWbLEyclp3LhxHVW7fft2fX39jBkzEEKVlZUNDQ2+vr7Ozs7Ci9XC7Tt37gQHB9NoNITQ7Nmz79y5Y2NjI1qz82bPnDmzbt06DocTHx8fGhp6584dsQpeXl5ijaSmpnbrK585c0ZbWzswMDA+Pr7dCmlpaX379h08eDBCiEajHTp0SFA+ffr0I0eOXL16df/+/Y6Ojtu3b6dSqd3qGgAAeg9I8AQjkUhhYWEbN258/vx5J9W0tbXDw8PXrVuHEGppaWltbc3Pz9fR0RFWEG5jGEYikQTbKioqPB5P9N3/bJZGo40cOTI9Pf38+fMXL168cOGCWIWcnByxRgICAtoGn5qa6uTk1G6nCQkJ2dnZ1tbWLBarqalp4sSJKSkpohX+/PPPadOmiX3qxo0bbm5uPB5vy5YtSUlJR48e3b9//8qVKzv6iQEAQC8H9+CJl5OTo6OjY2lp2Ukdf3//kydPVlZWstnsadOm3bt3r6Oafn5+iYmJjY2Nra2tZ86c8fPz626zs2bN2rVrl76+vrW1dVf6vX79ekkbHWV3hFBSUhKDwXj//v2+ffuCgoJSUlIwDHv58iWXy0UIsdnsa9euiV3MwDBs//79S5cuZbFY+vr6lpaWjo6O1dXVnXw1AADo5SDBE+zevXsZGRmjRo1KSEjopJqzs/Py5cs9PDxsbW1tbW3Hjx/fUc1x48bNmTPHxcXFwcHBzc0tJCSku80GBgY+fPgwNDS0W/32RGtrq729fWlpKULoxo0bw4YN09fXF61w/vz5yZMnq6urW1lZeXl5DRkyZN++fcuWLcMjGAAAUA4wkx2R+Hy+m5tbZGRk//79V69e/eDBA1VVuGkCAABACiCdECk2NpZEIoWGhpJIJAcHB2tr68zMTGtra6LjAgAAoPDgDB4AAABQQnAPHgAAAFBCkOABAAAAJYTjPXg+n5+Xl1deXs7lcs3NzZ2dnclkOJ4AAAAAZAGvBJ+enr5kyRJTU1Nzc3OEEIPBoNPpx44da7ukGAAAAACkDq9Bdo6OjpcuXbK1tRWW0On04ODg7OxsPLoDAAAAgCi8rpnzeDxTU1PREmNjYxixDwAAAMgGXpfoIyMjXV1dp0yZYmFhQSKRGAzGlStXIiMju97CvXv3rl69ilN4ACgEKyuriIgIoqOQBdjfAZD6/o7jc/B0Oj0tLY3BYCCETE1NJ0yY0Pl062LWrl37/PlzuGcPeq36+vrTp0/T6XSiA5G+o0ePvnnzRrQkJSWFSqVOnz6dqJAAIBYe+zuOo+jNzc1HjBghHEUvGG3XLb6+vmvWrBErLC8v37Nnz4sXL4YOHfq///2vb9++UooXAPlSUlJy+vRpoqPAhYaGhthyA3V1dba2tv/73/+ICgkAYuGxvyvYKPrm5uYxY8YEBgauWrXq+vXrX3zxxcOHD2FRcAAUy9y5c8VKLl68qK2tTUgwACgrvBL8smXLUlJSuj6KPi0tTezSxP37952dncWq3bt3z9jYePfu3Qghf39/T0/P7OxsuIwPAAAAiMErwXd3FP2zZ8/evXsnWvLq1au21Zqammg0mvClrq5uU1NTj4MFAAAAlI28jKJve+8tNzdXV1dXrNDT03PJkiUJCQn+/v6pqanPnj3z8PCQfvRAudTX19vb27PZbKID+YRGo7179w7mdgQA4AevBB8REREYGCgcRW9lZZWamtqtUfTt6tOnz8WLF1etWjV79mwPD4/Lly+3PQgAQExTUxOGYW/fviU6kE/69u3L5/MhwQMA8IPjKHpLS8vFixdLvVl3d/esrCwVFZW7d+/C/4+gi8hkstiwbQAAUG6KmiDV1NRaW1uJjgIAAACQU3idwR86dCgjI6NteVxcnFTap1KpbDZbXV1dKq0BAOQZhmHV1dWGhoZEBwKAIsErwc+fPz87O5vJZOI00aYgwePRMgBArpw9e3b58uV8Pl9LS+v48eNjxowhOiIAFANeCV5LS2vFihXnzp3DaW9UU1ODBA+A0nv9+vWaNWsyMjIcHBwyMzNnzpz58uVL0WdlAQAdwXGQnZOTk5OTE06NU6lUuAcPgNLLysoKCAhwcHBACPn4+NjZ2eXm5sLcVgB0BY4JHldwiR4AucLn8/Py8oRrTzg7O0vlIZe+ffsWFRUJtrlcbklJiZGRUc+bBaA3UNRR9JDggVw5deqU9f8RTO704cOHAwcODB482MnJKT09negA8ZWenj548OCVK1eeOXMmPj5+9erVdnZ2mZmZPW957NixTCZz3rx5Bw8enDx58tChQ+3t7XveLAC9gaKewcNjckC6EhMTb9++3adPn2+++UaClQ9DQkJCQkIE2ytXrvzw4QOJRNq3b9+TJ08YDMakSZNevHihxNM2dHftia6jUqkZGRmHDx/Oz8//8ssvFyxYQCKRetgmAL2EoiZ4OIMHUrRx48bU1NSwsLCioiI3N7eHDx9KkOMFkpOTL1269PTp0/Pnz0+ZMkVTU3PAgAEmJiZ5eXkuLi7SDVt+dHftiW4RjNiVSlMA9CqQ4EEvwuVyMzIyxBIPn8//+eefT58+TaPRBg4c+O7du+jo6LbrmVpZWQ0aNKjz9ktLSxctWpSUlKSnp1dWVmZhYSEot7CwKC8vl+IXkTfdXXsCACADiprg4TE5IIGysrI9e/aIJXgul9va2nrw4EHBSzqd3tTUVFJSIvZZHx+fjRs3dtI4j8ebM2fOypUrR44ciRDi8/miF5O5XK50voNc6u7aEyUlJR8/fhQtYbFYffr0wT1QAHoTRU3w8JgckIClpeWNGzfalru5uU2dOvXbb7/9+PFjQEDA3r17AwMDu9v41q1bNTQ0Vq9eLXhpZmb24cMHwTaDwTAzM+tJ5PKvW2tPbNiw4cWLF6IlRUVFGhoaOMQFQO+leAm+trZ2z549f//9d2Njo5ubm/AqKAASO3PmzPz589evX08ikdauXStBdk9PTz9+/PjTp0+FZ+3jx4+fOHHi5s2bKyoq6HS6Et+Al8CpU6fEStzd3eH5NwCkS8ESPI/HmzBhwvDhw52cnDQ0NEaPHp2TkwPTWoEeGjhw4IMHDxoaGjQ1NVVUVCRoYe/evY2NjZ6ensKS27dvf/vtt15eXgiho0ePKvEQeoT/2hMAAAkoWILPz89nsVj79+8PDw93dXXFMOz27dtBQUFExwWUgY6OjsSfTU1NbVv4zTfffPPNNz2ISGHgvfYEAEACCpbgm5qaBOfrglH0NBqNxWIRHRQAvR3ea08AACQgLwl+9+7d79+/Fy0pKipSVRUPz8XFpbS09MSJEwih3NzcW7du7dmzR3ZRAgA6gOvaEwAACchLgndxcTEwMBAtuXnzZttRtRoaGlevXv3uu+/u3btnZGQUHx+v9IOTAQAAAAnIS4IPCAgQKzl27Ji6unrbmkOHDr19+/a2bdt4PB4sKgUAAAC0S1FH9sJMdgAAAEAnFDXBw2IzAAAAQCcUNcHDGTwAAADQCXm5B99dkOBBt9TX14eHhxMdxSd8Pp/oEAAASk5REzyFQoEED7rI2Nj4wIEDcvUH4+vr2/YpUAAAkCLF+y/m/fv3ixcvvnv3rqqqqpeXl1ydlgH5RCaTQ0NDiY4CAABkSvHuwc+aNSsgICA+Pt7T0/PHH3+8e/cu0REBAAAAckfBEnx1dfW7d+9Wr16tqalJoVDCwsKuXbtGdFAAAACA3FGwBK+lpcVms1ksluAxuYqKCj09PaKDAqC34/F4wu2cnJwzZ868fPmSwHgAAEjh7sGrq6vPmzcvMDDQ19f37du3BQUF2dnZRAcFgOJZsmRJu+UHDx6UoDUTE5PKykqEUExMzI8//ujn5xcVFbVt27aQkJAeRQkA6IF2Enxubm5ubu7cuXPLy8stLS1lH1Pnfvvtt2PHjiUkJDQ3N9+/f18OIwRA/k2aNAmPZn/66afHjx+bmppWVlZ6enp2lOBDQ0OfP38uWlJYWOjg4IBHSAD0WuIJPjY2dteuXc3NzcHBwd7e3tHR0WFhYYRE1hFVVdXw8HAPD4958+bZ2NgQHQ4ACmnixIliJXw+Pzo6um15txgaGpqamgo2VFRUOqr2/fffC874hRYsWGBkZNSTrgEAYsTvwcfExDx48GDYsGHa2tq5ubm7d+8mJKz/RKVSYapaAHrohx9+MDMz09DQ6N+/v7a2dl5enmTtUCiUIUOGjB8/nslknjx5EsOwsLAwLy+vjupbWVm5/pumpiaZrGBDggCQc+Jn8CwWS7iGm2CkusRN8/n8vLy88vJyLpdrbm7u7OwsxR0YZrIDoOd+//33goKCDRs2REREMJnMEydOSNYOg8Fobm4uLi4uKioyMTHBMMzGxmblypXSjRYA0C3iCX7u3LmTJ08uKys7fvx4bGzs3LlzJWs3PT19yZIlpqam5ubmCCEGg0Gn048dOyatBV4hwQPQc1QqlUajubi4ZGdnL1y4cPny5RI3paGhYW9vb29vL3gZFRUlpRgBABIST/Bbt25NSUnJysoqLS3duXNnJxfZOrds2bKUlBRbW1thCZ1ODw4Oltagd1hNDoCe8/HxWbhw4fr166dPn/7x40ddXV2iIwIASI14gq+urh45cuTIkSOFL/v06SNBuzweTzDWRsjY2BjDMMmibAvO4AHouaNHj7569WrQoEE///zzkydPJL5EDwCQQ+IJfty4cYKN+vr6169fDxs2LDc3V4J2IyMjXV1dp0yZYmFhQSKRGAzGlStXIiMjexrv/4EED0DP1dTUGBoaVldXOzs7Ozs7Ex1OO549e7Zu3bp//vnHyclpz549Q4YM+c+PYBh28uTJuLg4FRWVr776avr06TKIEwA5JJ7gHz16JNzOz8+XeJ6KiIiIwMDAtLQ0BoOBELKyskpNTe3kmfXg4OCcnBzRktLS0qFDh3ZUn0Kh8Hg8Pp8PI28BkJi0DuhxUldXN2nSpC1btvzxxx9paWkTJkz4559/dHR0Ov/UwYMHjxw5smPHDg6H87///Y/H482aNUs2AQMgVzqbyc7R0ZHD4UjcNI1GW7RoEYlEevLkSUFBQWNjYyeVDx482NDQIFoSFBTUt2/fTj5CoVBaW1uFY/4BAN0lrQN6nGRlZQ0bNmzhwoUIocWLFycmJt6/f9/f37/zT504ceKPP/5wc3NDCKmpqf3yyy/ymeCbmprWrVt36dIlDQ2NiIiI7777juiIgLIRT/ArVqwQbhcWFtrZ2UnW7uHDh3/++ef8/Py9e/cePXrU29s7KioqOjp60aJF7dY3MjISm+ZCXV2987NzwaPwkOABkIoeHtDjgUKhiN6JY7PZXXlwl81ma2pqCrY1NTXl9l7emjVrqqurs7Ky6urqQkNDDQ0NJX5qqRNNTU3Xr19ns9ljxoyBqYR6G/EELzpsfuLEiZ9//rlk7e7YsSM/P59CoRw8eDAvL8/AwKCysnLUqFEdJXgJwG14AHpIWgf0OPHy8oqMjNy5c+eYMWOuX7/+8eNH4fjfTnz55Zdr1qzZt28fh8OJioqaOXOmDEKVQFJS0tOnT01MTKysrKKjo+Pi4qSe4MvKyry8vOzt7bW1tZcvX3758mV3d3fpdgHk2acEL3iAzcLCQvTtx48fS/YHYWRkJHiMTU9PT3Ai3sm8lZKBBA9AD0nrgB4nWlpat27d2rp165UrV4YOHXrz5k0NDY3//FRUVNSWLVv8/f0Fg+y+/fZbGYQqASqVymKxBNtNTU1UKlXqXezYsWP+/PlbtmxBCCUmJq5ZsyYzM1PqvQC59SnB//LLLwghBoPx119/OTk5aWtrZ2dnBwUFnT9/XoJ2d+zYMWrUqMmTJw8aNMjX19ff3z8lJWXp0qVSCxwehQegB6R7QI+ffv36jR8/vrq6urKy8sGDB8HBwf/5EQqFsmPHjh07dsggvJ5YuHBhSEjI5s2ba2tro6Ojjx07JvUuXr58GRQUJNj29vaOiIiQehdAnn1K8HFxcQihL774oqCgQHCl7v3794sXL5as3YCAgIcPH16+fFlPT8/e3t7IyCgxMVE4y5VUwBk8ABKT7gE9fk6dOrV79+7t27djGBYdHd3S0jJv3jyig5KOqKgoQ0PDvXv3ampqHj58eOzYsVLvwtHRMTU1dcyYMQihtLQ0JycnqXcB5Br2bwMHDuTz+YJtPp8/ePBgjCBubm4TJ05sW87lctevX29sbKyqqjpz5szm5mbZxwaADNDpdAsLC1y7GDt2bGFhoWD73bt3Y8eOxbW7TnS0v3t7e6enpwu2b9265evrK9OwFFx1dfWwYcNGjBjh5+dnaWn5zz//EB0R6BAe+7v4IDsPD4/g4OC5c+diGBYXFzdixAhCDjs68csvvzx8+PDRo0dTpkypqKjYtGnTDz/8QHRQACik4uLiQYMGCbb79+8vmLVCMjgtLtXc3Cx88J1GozU1NSGE8vPzN2zY8Pz5cycnp927d9vb27e0tLx69crIyEhsAs1ezsDA4PHjx/fv32ez2R4eHlpaWkRHBGRKfA88fPiwj4/P2bNn4+PjR48e/ccffxASVieSk5M3btxoaWmpra399ddfX716leiIAFBUggP6ixcvJiYmTp8+XeID+vT09MGDB69cufLMmTPx8fGrV6+2s7OTyniuqVOnbtiw4f379+/fv4+Kipo6dWpdXd3EiROnTJly+/Ztf3//8ePH37x5c+DAgfPnzx82bFhERATW4ymx5e1xwZ5QVVX19vYeO3YsZPde6NMZ/NKlS7///vuNGzcihARHwa9evVq9evW+ffsIi649WlpatbW1CCEqlVpTU6OtrU10RAAoqsOHDx86dOjs2bNkMnn06NESj7np7uJSZ8+epdPpoiXFxcVVVVV79uwRvJw6dapgJJC/v//t27ddXFwQQsOHDx87duzNmzednZ2HDx8uGC6goqLy5Zdf7tu3b8GCBU1NTR4eHvPnz3d0dBRr5+nTpzdv3hR211F5WVnZyZMnW1tbXVxcfH19hRcPutsOlEN5d8sTEhIEF6ik6FOCHzt2rLq6unDqSrkVHh6+dOnShoaG6urqH3/8cdOmTURHBIDike4BfXcXl2poaBAcpgtxuVwOhyMsFD4gw+Vy3dzc3NzcWltbk5OTfX192Ww2n89//Pjx8OHDhw4d2tzczOPxXF1dEUKampo+Pj4PHz4UPh0gbIfNZov22G55QUHBmzdv/vnnn759+y5evDguLk44j3232oFyqZS/ffu2qampb9++og9MER4nj8dTUVHBo/36+vqeX3wSQxJrsbCw8N69e19//XV0dPTdu3d3794t8YqxPeTu7m5kZJScnNz2rRs3bhw7duzevXtz5syBG/BAWZWUlHh4eIid6UrL5cuXAwICbt++LVY+ceJECVo7cODAvn372i4uFR4e3sUWOtnfBZYuXcpms58/fz569Oh9+/bZ2dmVlZWNHz8+KyurvLz8xYsXZmZmCKH58+ePHDlSsmffFy5c6O3t/dVXXyGEWltbDQwM6urqVFU7m88b4IHH482ZMyc7O9vS0vLNmzdnz56VhxkatmzZ8vPPP/N4PE9PzxMnTpibm0u3fTz2d/F78N999522tvbDhw+zsrJ2794tn8+S+vv7x8fHe3t7Dx8+nOhYAFBIU6ZMUVdXt7W1LS8vnzhx4v3793ft2iXxevARERE3btywsbGpqqqqrKwULC7V9ezeFXfv3l2wYMHLly937tz5559/FhUVkUikR48e3bx5c8uWLV5eXuvWrfvyyy9zc3NDQ0PbfrylpSUyMtLQ0NDExGTDhg08Hq9tHX19/fLycsF2VVWVhoYGZHdC/Pnnn7W1ta9fv/7rr7/i4+MFh1zEOnv2bFpa2uvXr5lM5qhRo8LCwoiOqEvE/3zLy8tnzpy5bt26efPmeXl5vXv3jpCwugKegwegh7777ruFCxeKHtCnpaVJ1pSlpaXEt/C7om/fvuXl5RwOp7m5mcPhuLu7Dx06VFdX19LScvny5R4eHnfv3p0yZcqMGTPane1u48aNHz9+fPHiBZvNXrBgwa+//rpy5UqxOosWLfL19SWRSGZmZr/++uuyZcvw+zqgE0+fPg0MDBSsO+Dj48NisaqqqgwNDQkM6ebNmxEREcbGxgihDRs2GBgYcDicrqyMQCzxBG9nZ7dq1arz588/evQoJiaGRqMRElZXwEx2APSQAh3Qr1279quvvrKzsxs2bFhVVdXMmTNPnTp1//59wbvu7u6dz8GXnJx86dIlwQKVmzZt2rx5c9sEb29vf/fu3QMHDhQUFKxdu3bGjBk4fRfQOVtb2+zsbMHMp2/evMEwrE+fPsSGZGBgIHyItKKiQlNTU/6zO2qb4Pfv33/69OlLly6ZmJhUV1cLpreTT3AGD0APKdABfUBAQHJy8tmzZ3Nzc83MzPh8fnp6ev/+/bv4cW1tbeHgptra2o6evrG3t//tt9+kEjCQ2Ndffx0bG+vv7z9gwIDLly//9NNPJBKJ2JAWL148evRoHo9nbm7+66+/iq7SJM/EE7yRkZGfn19ubq6Tk1N4eLilpSUhYXWOyWT+8ssv169fLywsnDNnjoGBAdERAaCQFOKAnsvlFhUVGRoaDh8+XOJhN+Hh4V9//fXmzZvZbPbGjRt///136QYJpEhLSys7O/vKlSsVFRVLly51cHAgOiJkZ2f3119/HThw4N27dxs3bpw2bRrREXWJeIKPjY3dtWtXc3NzcHCwt7d3dHS0vI0m4HK5/v7+Q4cOdXJyKi0t9fX1ffjwIawKD4AE5P+A/v79+7Nnz6ZQKFVVVeHh4bt375asnbCwMD09vXPnzqmqqh4+fNjf31+6cQLpolAo8pZEBw4cGBMTQ3QU3SM+ij4mJubBgwfDhg3T1tbOzc2VeHfCz5MnT7hc7tGjRx0dHQMCAgwMDP766y+igwJAIcXGxs6cOXPTpk1sNtvb2/vo0aNER/QvGIbNmTPn4MGDr1+/fv/+/Y0bN1JSUiRuLTg4OCEh4dy5c5DdQS8hnuBZLJbwbFiW4wiamppq/43L5bb71H99fb1gOKXgHryRkVF9fb1sggRAycj5AX1paSmPxxs/fjxCSE9Pb8aMGVlZWUQHBYDCEL9EP3fu3MmTJ5eVlR0/fjw2Nnbu3LmyiWP69OnCAbECDQ0N7Q6scHNzy8/Pv3r1qqqq6ps3b7KysuB2GgCSIeqAvosEh+/CR6RevHgxcuRIooMCQGGIJ/itW7empKRkZWWVlpbu3LlTZtPYtb3yJpjZqm1NPT29CxcuREZG5uXlCbYFzyYCALqLqAP6LqJSqatXr/bx8Zk9e3ZhYeGTJ0/2799PdFAAKAzxqWrFNDQ0CJdbkLH/nLry6NGj2dnZR44ckWVUAMgMrlPVCgkO6DU1NT///HOi5qVGne7vd+7cuXv3rqmpaUhICCyJBpQVHvv7pzP4p0+frl+/vqamZsKECUuXLl2/fv2HDx8ECzxLsT8pUlNTg+fgAeihiRMnCuefl/iAXrACh2A7JyfnxYsXn332mWCZrJ7z8/Pz8/OTSlMA9CqfBtl99dVXHh4eMTExDAbDycnJ3Nx8/fr1Dx48IDC4zlGpVJjJDgDJPH36NCAgYMSIEZs3b66qqlq0aFFAQMDAgQMla83ExESwERMTM2nSpGvXrgUEBJw6dUp68QIAulWn8uIAACAASURBVO3TGXxtbe2WLVsQQg4ODpcvXxZsyzOYyQ4AiX311VdBQUFjx449efKkk5PT4sWL586d2/WJ4Try008/PX782NTUtLKy0tPTMyQkRBrBAgAk8SnBC6+w9enTRyFudEGCB0BiOB3QGxoaChaGNzQ0FP6XAgAghAIvhgiLzQAgMeke0FMolCFDhlhZWTGZzJMnT4aEhISFhXUyZK+qqorJZIqWtLS08Pn8HoYBABD1KcEXFxcLx9ewWCzhdkNDAwFxdQGcwQMgJxgMRnNzc3FxcVFRkYmJCYZhNjY2bZdrE/rmm29ycnJES0pLS+XtKXwAFN2nBN/c3ExgHBKABA+AxKR+QK+hoWFvb29vby94GRUV1UnlhIQEsZKO5r0QRnj+/Pnq6mo/Pz+J15sBoLf5lOCpVCqBcUgAHpMDQGK4HtD7+vrevXtXWq3V1dW5ubk5Ozv369cvKCgoOjp60aJF0mocACWmwPfg4TE5ACSG6wE9j8eTYmt//PGHn5/foUOHEEJLlixxc3MLCwsjfIFwAOSf+GIzCgQu0QMgn6T7dFxxcfGwYcME2zY2NjweT2yAHgCgXYp9Bg8JHgA5JMVL6Hw+H8MwwTJ3X3/99bVr10xNTXV1daXVPgBKTIETPDwmB4DSmzNnTnl5ed++fVesWLFmzZo+ffpcuHCB6KAAUAw4Jng+ny+Yyp7L5Zqbmzs7O5PJ0rwjAGfwACi3wsLChw8fvnz5kkKh0On0wMDAnTt3urm5ER0XAIoBrwSfnp6+ZMkSU1NTc3NzhBCDwaDT6ceOHfPx8ZFWF5DgAVBuDAbD2tpa8Hy8paXl8OHDKyoqiA4KAIWBV4JftmxZSkqKra2tsIROpwcHB2dnZ0urCwqFwuFwMAyD8bQAKCUXF5fnz58/ffp0+PDh79+/v3Hjxrp164gOCgCFgVeC5/F4gimphYyNjTtfe767SCQShUJpbW1VuCf4AQBdoa+vf+TIkYkTJ6qrqzOZzL179w4aNIjooABQGHgl+MjISFdX1ylTplhYWJBIJAaDceXKlcjISOn2IngUHhI8AMoqMDCQTqeXlJSYmZmpqakRHQ4AigSvBB8REREYGJiWlsZgMBBCVlZWqamplpaWHdXPysoqKysTLampqaHRaJ33IrgNL5xlEwCgfFRVVXu+ji0AvRCOo+grKiqMjY1nzpwpfGg1OTl50qRJ7VZOS0t7/fq1aElNTU0nc1MLwDg7AAAAoF14Jfhdu3YdOnTIy8tr+fLlFy9edHFxQQgtWrRI7DRdaMeOHWIl7u7uBgYGnfcCj8IDAAAA7cIrwe/fvz8vL69Pnz75+fnTpk178uQJHhfS4QweAAAAaBdec9Hr6OhoaWkhhBwdHZcsWbJ8+XI8eoEEDwAAALQLrwQ/e/ZsT0/Pw4cPI4SWL19eU1Mza9aspqYm6fYCK8YCAAAA7cLrEv2mTZs8PT2rq6sRQiQSKSEh4fz58/r6+tLtBVaMBUARffXVV/n5+aIlhYWFQ4YMISoeAJQSjqPox4wZI9xWUVGZPXv27NmzpdsFXKIHQBFt2rSppqZGtCQ0NFTqJwAA9HIKvJocggQPgGKytra2trYWLdHS0lJVVez/jgCQN3jdg5cNeEwOAAAAaJdiJ3g4gwcAAADaBQkeAAAAUEKKmuDfvn27cOHCW7duHTlyRGy0TkeKi4snTpyora09YMCAM2fO4B0hAAAAQCCFTPA1NTV+fn4ODg7bt29/+vTp+PHjeTzef35qxowZ3t7eHz9+jI+Pj4qKevDggQxClYrW1takpKRjx469efOG6FgAAAAoBoVM8NevX/fw8FizZs2YMWOsra3z8vLWr1//+PHj8PDwmTNnnjp1is/nI4RqamouXLiQlJTEYrEqKiqKi4vXrVunpaXl6uoaFhaWmppK9PfoksbGxhEjRhw8ePD+/fve3t7x8fF49/j48eO9e/fGxsZKfWIiAAAAMqOQCb6lpUVTU7OiosLT03P8+PEcDufXX38dMWLE/fv3P//88wMHDmzdujUvL2/IkCHnzp07evTokCFDampqWlpahDfsa2pqtLW1u9tvdnZ2dHT0nj17BGvgysahQ4dcXV1v3Lhx9OjRmzdv4jTpr9DBgweDg4MrKyuTk5NdXV3r6+tx7Q4AAABOFDLBjx07Ni0tbfPmzaNHj+ZwOCQSiUajUSgUc3Pz33//PTIy8tdff/3666/DwsI2bNiwbdu2SZMmbd682c/Pb/z48adPn962bdvZs2eFC9dmZmaOGjXKxMRk0qRJYkvWijp9+vSMGTOoVCqDwXB1dX379q1svuzr169Hjhwp2B46dCiLxWpoaMCpLz6fHxUVlZmZ+cMPPyQkJIwaNerYsWM49QUAAABXCjmzhKWl5fnz5+fPn19VVWVsbPzdd9/du3cvPz9fXV391atXJ0+ebGxsfP78uYqKSlpaGkKosbGxuLjY1NSUyWQuWrQIIUSlUj08PJhMpqBBNTU1DQ2N9PR0BwcHCwsLFRUVhBCFQhGe5Wtqaj558sTR0TEzM5NGoxkaGo4bN87FxUVXV5dM/v8HSXp6eiQSSbAtOieXaB0ajSZoHCGkra1NoVCEAQjW5mn7cRsbm6SkpC+//FJFRSUzM9PY2BiPdfkE6urqyGSylZWV4KWzs3MnRzwKgcFg8Pl8CwsLogNRcjweT/iHnZOT8+LFi88++8zOzo7YqADo5RQywSOEvL29k5KSpkyZ8sUXX1CpVEdHx7y8vO3bt9++fdvR0VFLS4vH402aNCk8PBwhtHHjxurq6gMHDrRt59ixY/fv3//tt98EV+99fX137tzp4OCAEOJwOI2NjYJqLBYrICBg48aNVCqVyWT+/fffWVlZ06dPr6+vF9zvRwjV1dVhGCbYrq2tFXbx/v17YTmTyRSOB2xsbORwOILt1tZWFosl/Ijox5uamqqrq42NjclkMp/P5/P5wsMIKpWqqakprCl69ID+fZSgoqJCo9FEv3hH73I4nAkTJgwePBghdP78eWdn53Xr1mlqalKp1E5aE4ukbTDo3wdACCESiaSnp4f+re2n2rbcRUwmc/r06Xl5eWQyeeDAgRcvXuzTp48E7eDqypUrV69e1dbWDg8Pt7e3JzocyZmYmFRWViKEYmJifvzxRz8/v6ioqG3btoWEhBAdGgC9F44Jns/n5+XllZeXc7lcc3NzZ2dn4YmsVLi4uOzZs2fNmjWlpaUDBgyYMGHC8OHD+Xx+cXHx4cOHa2tr/f39z58/39LSUltbm56e3m4j6urqTU1NmpqampqaGIax2WwbGxsbG5u2NX18fAoLC1etWtXc3Hz8+PE5c+ZMnz5dil+nc/n5+dXV1a6urqKn72w2W3QcnOjRA/r3UQKPxxNerkAIYRhWV1fX7rv6+vqHDx/Oy8tjMpkODg6jRo0ikUgsFku0Ix6PJ3Zm39LS0tzcLFoiFgz69wEQQojP57e9wd/2U2LfUUDsWAEhpK6urqGhIVpSXV2toqLSv39/Eon08uVLe3v7fv36oQ6OGNoesghoaGioq6u3LRe9+tJ5YJ3Uv337dlZWVmBgoJaW1ujRo2/duuXo6Nj2s4rlp59+evz4sampaWVlpaenJyR4AAhEEv0/V4rS09OXLFliampqbm6OEGIwGHQ6/dixYz4+Pl1swd3d3cjIKDk5+T9r5ufn//zzz5WVlWPHjo2IiFBTUxOUs1isv//+m0KheHp6tvvfMUKourraxcVl6dKl7u7u586de/bsWWZmZrsHIkVFRcHBwWVlZU1NTVOnTj1y5IiyTp3NZrMLCwsNDQ0Fvzs5JHasgBBqbm5uaWkRLQkKClq3bt2gQYMQQh8+fFixYkViYiLq4IhB7ACok2YFRK++dB6YQENDA5fLFSs8e/bshAkTyGTypk2b0tLS3r59u3//ftEKJSUlHh4edDq9bYPyxsjISHAG7+zsnJubixDCMGzw4MEFBQVdbKHr+zsASgmP/R2vFLVs2bKUlBRbW1thCZ1ODw4Ozs7Obrf+gQMHPnz4IFpCp9NFLwt3wtHR8cSJE23LtbS0vvjii84/26dPn/T09B07diQnJ7u7u1+5cqWjywz9+/d//PhxSUmJtrZ222vLyoRKpQ4bNozoKDrT9uffdiEyGxsbDofj6uqKEKLT6QMGDBBsywkMw86ePXv8+HHB2Iv8/PyOdg2FQKFQhgwZYmVlxWQyT548GRISEhYW5uXlRXRcAPRqeCV4Ho9namoqWmJsbNzJ1YK+ffuKDQ43NzeXzfrQtra2x48f72JlGK6lKDZs2DBp0qRnz56pqqrGxsbKYP6AbiGRSGPHjt2xY8e2bdvq6+v37du3YMECooOSHIPBaG5uLi4uLioqMjExwTDMxsZm5cqVRMcFQK+GV4KPjIx0dXWdMmWKhYUFiURiMBhXrlyJjIzsqH5wcLBYSXV1tZGREU7hAaUnmBchPj4ew7CMjAw5HNF96NChBQsWCB6y+PbbbxcuXEh0RD2ioaFhb28vHCoYFRVFbDwAALwSfERERGBgYFpammBOGCsrq9TUVEtLS5y6A6AtGxub9evXEx1Fh4yNjdPS0lpaWigUitizA0rvwoUL7969Ey0pKyuT7HEJAEBHcBwmZmlpuXjx4p60kJ2dffjw4XbfunjxoozP7+vq6igUiujT6njDMKy8vFzsTgfeqqurtbW1uzj6QSq4XK7gOUCZ9YgQ+vjxo6GhoSzTKpvN1tLS8vDw6PpHRJ+DkHOHDh3KyMhoWx4XF9du/crKSrFvp6Oj09TURPj+zmAwzMzM8O5FZrs2n8+vqKgwMTHBuyMejyeYlQTvjjgcTm1tbd++ffHuqLW1tb6+XgZ/dS0tLTQazd3dHY/9XX7HgY8bNy4+Pv7Jkydt38Iw7Pr164Kn1WXm48ePFArFwMBAZj1yOJzi4uIBAwbIrEeEUGlpKY1Gw28unbaam5s/fvzYv39/mfWIECoqKjI2NhZ7sg5XgicAhY94dNGKFStwike65s+fn52dzWQyIyIiulK/bTV/f3/C93cej/fmzRsZ3M3hcDhFRUUDBw7EuyM2m11SUiI62Bknzc3N5eXl1tbWeHfEYrGqqqoEj7ziqqGhoa6uTgZXnQVPCwueyZL+/o4pIC6Xq6KiIuNOly9fHhMTI8sei4uLraysZNkjhmHBwcEXLlyQZY/37t0bNWqULHvEMGzkyJH379+XZY/x8fEzZsyQZY8yJljzCY+WZba/19XV6erqyqAjOp1uYWEhg44KCgrs7e1l0NHDhw9HjBghg45u377t5+cng44uX748efJkGXT0559/zp07F6fG5fcMHgCgQJycnJycnIiOAgDwiUIuNgMAAACAzkGCBwBIma+vL9EhAAAgwQMApE1sQQEAACEUMsGTyWQXFxcZd2pmZiaDp01EaWtrC6ZSlyULCwsZPIIiysDAQMZD6BFC/fv3l+UDEQghY2Pj3jMNonTXmJHZ/k6lUmUze6aWlpZsZl7S1dWVzWM4+vr6MhhCjxAyNDQUrmeNKyMjI9lM3GJsbIzfqh94LTYDAAAAAAIp5Bk8AAAAADoHCR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACWkeAn+wIEDgwcPdnJySk9Px7uv77//fvDgwZaWlrt27ZJl73v37k1NTZVZj9evXx8xYkS/fv2Eq3Hj3em6deusra1tbW3//PNPGfTIYrEOHTokfNm2L6n3LtYjUX9ISgDvH5SMfzUy2LVltjvLYC+W2Z4rsx1WrCMBHP8qcFqlDicfPnywt7dnsVivX7+2s7Pj8Xj49XXr1q3hw4c3NzdXVlZaWVk9efJENr1nZWWpqanFxsZiMvm+tbW1AwcOrKysrK+vt7Ozq6+vx7vTjIwMZ2fnlpaWsrIyXV3dxsZGXHt89uzZwoULZ82aJXjZti+p9y7WI1F/SEoA7x+UjH81Mti1ZbY7y2AvltmeK7MdVqwjAVz/KhTsDD4tLW3KlCmampoDBgwwMTHJy8vDr6/q6urw8HB1dXVDQ0MvLy86nS6D3plM5vr162fNmiV4KYMeBcseGxoa0mi0/Px8HR0dvDtVUVFRU1OjUCjq6uqqqqoI568ZFxdXV1cnfNm2L6n3LtYjIX9IygHvH5QsfzWy2bVltjvLYC+W2Z4rsx1WrCOE/1+FgiX4srIy4YTeFhYW5eXl+PU1Y8aMxYsXI4Ty8/P//vtvX19fGfS+dOnSzZs3C6dJl0GPdDq9qKjI0dHRysrqxx9/JJFIeHfq6ek5aNAgMzMzS0vLLVu2aGlp4drjzp07IyIihC/b9iX13sV6JOQPSTng/YOS5a9GNru2zHZnGezFMttzZbbDinWE8P+rULAEz+fzSSSS8CWXy8W1OwzDYmJipk2bdvHiRV1dXbx7j4+PNzAwGDNmjLBEBt+3ubn59evXmZmZubm5R44cefLkCd6d3rlz5/Xr13fv3k1NTY2JiWEwGLL8tbbtSwa9y/gPSWkoza9GZru2zHZn2e/FstxzleavQrWHn5cxMzOzDx8+CLYZDIaZmRl+ffF4vOnTp+vp6T1+/JhGo8mg99jY2Ldv3966dausrOzChQtsNlsG39fIyCggIEBfXx8h5O3tXVhYiHenqamp8+bNs7e3t7e3HzlyZGZmpix/rW37wrt32f8hKQ2l+dXIbNeW2e4s+71YZnuuUv1V9PAevowVFxcPHTqUzWbT6fQBAwbgOjrp7NmzM2fOJKT35cuXC8ZcyKDHgoKCIUOGVFdXV1RUWFlZvXjxAu9ODx8+PGHCBOEYlpycHLx7vHXrlnBgS9u+8OhdtEcC/5AUHd4/KNn/avDetWW2O8tmL5bZniuzHVa0IyH8/ioU7Azeysrq22+/9fLyQggdPXqUTMbxFsNff/2VlpZmamoqeHnkyJFJkybJrHcBGXxfe3v7xYsXu7u7Yxi2YcMGBwcHhBCunS5cuDAnJ2fw4MEIoTVr1jg7O+Pdo6i2P1K8f8jy8IekoJT4V4PTV5PZ7iz7vVhme64y/VXAevAAAACAEoJTBwAAAEAJQYIHAAAAlBAkeAAAAEAJQYIHAAAAlBAkeAAAAEAJQYIHAAAAlBAkeAAAAEAJQYIHAAAAlBAkeAAAAEAJQYIHAAAAlBAkeAAAAEAJQYIHAAAAlBAkeAAAAEAJQYIHAAAAlBAkeAAAAEAJQYIHAAAAlBAkeAAAAEAJQYIHAAAAlBAkeAAAAEAJQYKXL+/evZs6dWpoaOgff/xBdCwAAAAUGAnDMKJjAJ+sXbt29uzZLi4uPj4+mZmZRIcDAABAUakSHQD4l61bt6qrq5eUlBgaGhIdCwAAAAUGl+jli4aGRkJCQmRk5IEDB3DtKCMjIygoSJYfrK+vFyvhcrlVVVXtVu7krdra2qKiIgkCAACAXgUSvHxJSUl58OBBUlKSiYkJ0bFIE5PJ9PHxESuMiopasWJFu/WFbxUXF3t4eNjb2//222+Ct6Kjo9lsNq7RAgCAEoAEL1+SkpJevnw5Y8aM+fPnd/EjHA6n3e3Oa8rSzp073d3dmUymaGFGRsbx48fbrS/61q5du7Zs2fL8+fOTJ08ymcxXr14hhOzs7PCOGQAAFB0keCKtWrVq0KBBDx8+RAglJCQ4ODgcPHgwOTn5woULp0+fFqu8b98+GxubgQMHrlq1is/n37t3b+7cubNnz962bZvoNkJox44dtra2NjY2a9euFavZNgaxZseNG3fp0iXBWyNGjMjMzBSrIMHXDAgI2Llzp2hJXV3dmjVrNm3a1Lay2Fu6urovXryg0+ktLS0UCmXHjh1RUVESxAAAAL0OBgiVmJgYEBDQ1NTUr1+/tLS0jqrdvXt32LBhFRUVjY2Ns2bN2r59e1ZWlq6ubmFhIYZhotvXrl2zs7Orr69vaWnx8vI6deqU6LuiDU6dOrVtsydOnJg/fz6GYW/fvrWxsUlPTxerIPigaFPjx4/v18azZ89E65SUlPTv31/4cvbs2VeuXDl37ty8efPEvqnYW/X19eHh4WPHjr1x48ajR4/Wr18v2c8ZAAB6GxhFT7CgoKB169YtWbLEyclp3LhxHVW7fft2fX39jBkzEEKVlZUNDQ2+vr7Ozs7Ci9XC7Tt37gQHB9NoNITQ7Nmz79y5Y2NjI1qz82bPnDmzbt06DocTHx8fGhp6584dsQpeXl5ijaSmpnbrK585c0ZbWzswMDA+Pv4/36LRaIcOHRJsT58+/ciRI1evXt2/f7+jo+P27dupVGq3ugYAgN4DEjzBSCRSWFjYxo0bnz9/3kk1bW3t8PDwdevWIYRaWlpaW1vz8/N1dHSEFYTbGIaRSCTBtoqKCo/HE333P5ul0WgjR45MT08/f/78xYsXL1y4IFYhJydHrJGAgIC2waempjo5ObXbaUJCQnZ2trW1NYvFampqmjhxYkpKyn++dePGDTc3Nx6Pt2XLlqSkpKNHj+7fv3/lypWd/NAAAKA3g3vwxMvJydHR0bG0tOykjr+//8mTJysrK9ls9rRp0+7du9dRTT8/v8TExMbGxtbW1jNnzvj5+XW32VmzZu3atUtfX9/a2ror/V6/fr2kjY6yO0IoKSmJwWC8f/9+3759QUFBKSkpGIa9fPmSy+W2fUvwEQzD9u/fv3TpUhaLpa+vb2lp6ejoWF1d3clXAwCAXg4SPMHu3buXkZExatSohISETqo5OzsvX77cw8PD1tbW1tZ2/PjxHdUcN27cnDlzXFxcHBwc3NzcQkJCuttsYGDgw4cPQ0NDu9VvT7S2ttrb25eWlnZU4fz585MnT1ZXV7eysvLy8hoyZMi+ffuWLVuGRzAAAKAcYKpaIvH5fDc3t8jIyP79+69evfrBgweqqnDTBAAAgBRAOiFSbGwsiUQKDQ0lkUgODg7W1taZmZnW1tZExwUAAEDhwRk8AAAAoITgHjwAAACghCDBAwAAAEoIEjwAAACghCDBAwAAAEoIEjwAAACghCDBAwAAAEpIfp+DP3369K+//tp5HU2yShOfJ5t4AJC9oUOHxsbGEh2FLHRlfwdAuUl9f5ffBJ+fn+/l5TV//vyOKlDo5bTzt2oXf8nX0ZRlYADIRkVFxeLFi4mOQkb+c38HQLnhsb/Lb4JHCJmbm7u6urb/Hp/PiPuBZGNpkffO6LtQ2cYFgCyUlJQQHYJMdba/A6Ds8NjfFfUefMOtv8nqVJONkezCty0Fb4kOBwAAAJAvCpng+c0tdRfSDBZOI1HV9OcE1pxIQDDhLgAAACBCIRN8XXyq5meOajZWCCEtr8/IVLXGu9lEBwUAAADIEbm+B98u7seqhrQMjc8cq/+I+/9FZHLtmStaXq4kCoXQ0ICcYjKZ9vb2LS0tRAfyia6u7tu3b8lkhTzCBgAoBMVL8GQdLYNFM5DIJXk1GyuSqgpJRYW4oIBcY7FYfD7/7Vs5GqvRt29fPp+vTAmez+fn5eWVl5dzuVxzc3NnZ2dl+nYAKCIcEzxOOzxZU0NnrGfP2wG9CplM1tfXJzoKpZWenr5kyRJTU1Nzc3OEEIPBoNPpx44d8/HxITo0AHovvBI87PAA9B7Lli1LSUmxtbUVltDp9ODg4Oxs6QyOwThcEkXxLjcCQCy89hm8d3gAgPzg8XimpqaiJcbGxpiUnm3hVtcxVu4w2bxMzcZSKg0C0EvgleBx3eEBAHIlMjLS1dV1ypQpFhYWJBKJwWBcuXIlMjJSKo3X/nlJ1diw5kSCybbliESSSpsA9AZ4JXhcd3gAgFyJiIgIDAxMS0tjMBgIISsrq9TUVEvLDk+4ly1bVlBQIFqSl5c3YsSINWvWiNVkv3rPLnhrFhP1cctvrL9ztDyH4xE/AEoJrwTf3R0eAKDQLC0tuz6TdlhYWEVFhWhJZGQkl8sVr4dhNccT9OdOIWuoGywMrow5ofnZUBJVTSoBA6D0cBy3QqPRFi1aRCKRnjx5UlBQ0NjY2Enlzz///O7du2KFI0eObHtEDwBQdE5OTmIlurq6lDbzWDSmP0BkspaXK0KIamdDtbOuv3xLb8YEGUUJgILDK8EfPnz4559/zs/P37t379GjR729vaOioqKjoxctWtRu/fT0dLESd3f3Pn36dN4Lt6qWrKlO1tSQTtCgN8Owj9sP0CZ9ruEyWIJPnzp1avPmzYJtDodTWlpaXFxsZWXFYrFOnz79zTffSDVWuXPo0KGMjIy25XFxcW0Luwhjt9bGJVNtrWrPXPn/RWSV+su3dL7wVNHXlbhZAHoPvBL8jh078vPzKRTKwYMH8/LyDAwMKisrR40a1VGCl0x9wjU1awudAG8ptgl6p8bMR5zS8prjF8xiokmq3Z40KSQkJCQkRLC9cuXKDx8+WFlZ5efn//LLL01NTUqf4OfPn5+dnc1kMiMiIqTWqIqK7pQxGJcnLFDT1lTrZw6X6AHoIrwSvJGRUWtrK0JIT09PML+NCg4zzWEcDr+xSerNgt6G38KuPXPFaOVXzEu3mCnpulPGStxUcnLypUuXnj59ihCKi4urq6tTU1P+hKSlpbVixYpz586NGTNGWm2SVFVok/yk1RoAvRCOZ/CjRo2aPHnyoEGDfH19/f39U1JSli5dKrUO+Py6hGtYK4fPggQPugrj8dkFbzC++OOajXfuq1mYYq1czZEuNccuqBoZkLW1xOpQTAxV+/7HPaPS0tJFixYlJSXp6ekhhHbu3Hn79u2jR49K8SvILScnp7Z31gEABMIrwQcEBDx8+PDy5ct6enr29vZGRkaJiYn29vbSar/h1t91F9IoFiZwAx50Ha+2vj7phtjiwlgrh/26mDqoP/PSDYQQWVujJjZRzcJE7LNUhwF608d31jiPN2fOnJUrV44cOVLqkQMAQHfhOIpeT08vNDQUj5b5rKa686l9V4dVxpzg/ddJFQBCqob6xhvFJ2OosFjH3gAAIABJREFU/OkY1c5ay3uE4CW/uaX2zGX9dd90d960rVu3amhorF69WjqxAgBAzyjk9M5151M13Ydpug9T0ae1vqcTHQ5QbBRLU7K2Zuu7D8ISHT8PjM/vViPp6enHjx9/+vQpCaZaw1990g0tT9f/vGMCQC+neAmeU/qR9ddjs1+jEUKqBrrstx/Yb4qpA/oRHRdQVFJ5rnrv3r2NjY2enp/WObx9+7aVlVXPWwZi2IXvas8ls18V9f1fV+fVAaB3UrwEXxObqDN+NIlM5rOaMC6PrKFRE5to+v0KmKQaECg1NbXd8jFjxkhxYDlAGFYTm2i4ZG79xevNuQUazg5EBwSA/FKwBM9jNra+/cB+VcRMTkcI8ZubEYaw0o/cyhq4XgeA0mu88wCRydqj3chaGjWxiWY/rSfh8PwtAMpBwRK8Ck3b8vhu4cvS5ds5ZRVWx3fD6TsASo/f3FIbn9x37WJEImmOcGq4/lfDjSza+NFExwWAnCITHUDPcHmIx+e3sImOAwCAu/rEa1RrSxUdLe7HKu7HKp1xPnXnU/kNLKLjAkBOKdgZvBiMy0UkEp/VTNZQJzoWAAC+2C/fc2vqy7f9Liwha2q0vHqv6TqUwKgAkFsKn+BVtLX4Tc0I6RMdCwAAFxiXV7Z2j35IkMn3K4iOBQBFotiX6DEOj6ynw2c1Ex0IAAAvDal3MQ6n5kQCxuP9d20AwP+RlzP4M2fOlJSUiJYwGAwNjf+YhhbjclX0dGC9GQCUFa++of7STZPtK2pOJDakZcDyMwB0nbwk+Obm5traWtESPp+PYeKLgojBuFwVmg6/CRI8+A/19fXh4eFER/EJv5sz5fVadWevavu6U8yMDRZMK4v+WcvrMxU9GtFBAaAY5CXBh4WFiZWkp6fr6Oh0+AEMw3h8xMdUaNpwiR50ztjYeP/+/YL1i+XE6NGjVVXlZe+TW61FJU1P/jH/bSNCiGJurD3avS4+tU/4LKLjAkAxKOp/MTUnEnl1dSRVVbKWJiR40DkymbxgwQKio1BmPB5P5f8mnMnJyXnx4sVnn31mZ2fXw2ZrjidSzI0bbmQJXpIoqszUuzrjfdSszHrYMgC9gUIOsuPQy1j3nrBfFZPIJLKmOr8JEjwARDIx+f+r68bExEyaNOnatWsBAQGnTp3qYbMaLg7Ugf35jU2CfwjDdCePganrAOgihTyDrzl5UTd4HImqVnPoHFlLg08vIzoiAABCCP3000+PHz82NTWtrKz09PQMCQlpt9qFCxfevXsnWlJWVqapqSlWTTfIH69AAegFFC/BNz16xq2q1fH34tc3VJNJ7Lcf4AweADlhaGhoamoq2FDp+FS7srJSbFAtl8vlwVNwAEiVgiV4jMurPX3JYOF0kooKxuWp0LSa7uWoWpkSHRcAvRqFQhkyZIiVlRWTyTx58mRISEhYWJiXl1dH9SMiIsRK0tPTaTQYHg+ANClYgm9Iy+AxG1v+edXyzyt+Awtr5ZIoKlzGR6LjAqBXYzAYzc3NxcXFRUVFJiYmGIbZ2NisXLmS6LgA6NUULMGr2VrpTv1CsI1xOCSKqvboEY13HxIbFQAKZ8mSJe2WHzx4ULIGNTQ07O3t7e3tBS+joqIkjKwDnPLK5pwXsHYcAF3XToLPzc3Nzc2dO3dueXm5paWl7GPqhPrgAeqDBwi22W8/sAvf6U71b7h5j9ioAFA4kyZNIjqE7qk5ntCSV6huZ6NmI1//KQEgt8QTfGxs7K5du5qbm4ODg729vaOjo9tOQSMvOFykqkrWVOc3sxGGwZLwAHTdxIkTxUr4fH50dHTbcnnQ/OQf7scqg4XB1ccumG5fATs7AF0h/hx8TEzMgwcPhg0bpq2tnZubu3v3bomb5vP5OTk5aWlpV69effr0qdTn5sS4XBJFFZHJZHUqv6lFuo0D0Bv88MMPZmZmGhoa/fv319bWzsvLIzqidmA8Xs3JJIMF03T8vRCPx/r7KdERAaAYxM/gWSyWuvr/X1tdU1OTQqFI1m56evqSJUtMTU3Nzc0RQgwGg06nHzt2zMfHpyfhisK4PJKqCkKIrKXBZzWRtf5jZRoAgJjff/+9oKBgw4YNERERTCbzxIkTREfUjobUDNW+BupDBiISyeCraZUxJzQ/cyRR1YiOCwB5J57g586dO3ny5LKysuPHj8fGxs6dO1eydpctW5aSkmJrayssodPpwcHB2dnZkgf7bxiHS1JVRQiRNajwKDwAEqBSqTQazcXFJTs7e+HChcuXLyc6InE8ZmNdwjWKuXHFD4f7LJqpokdTNe1bf/mW3owJRIcGgLwTT/Bbt25NSUnJysoqLS3duXNnJ0+ydo7H4wnmuxAyNjb+z9XhukVwiZ517wmnpIJbWaPW30KKjQPQG/j4+CxcuHD9+vXTp0//+PGjrq4u0RGJY794Q1KjsN9+QAiVRceQ1CgIIbghB0BXiCf46urqkSNHjhw5UviyT58+ErQbGRnp6uo6ZcoUCwsLEonEYDCuXLkSGRnZ03hFcXmITKo9fYmsrdFwPUtzhJM0GwegFzh69OirV68GDRr0888/P3nyRA4v0Wu6OdWdT+2zaCbi82vjU8x+XE9SUcgVNACQPfEEP27cOMFGfX3969evhw0blpubK0G7ERERgYGBaWlpDAYDIWRlZZWamtrJQ3fjxo17+PBfj7M3NDQ4OXWWszEul8OooNrbIhKp+Ul+a1GpWn9zCUIFoNeqqakxNDSsrq52dnZ2dnYmOpx2MK//RdbV1nRzQgg13MxqvHVPJ8Cb6KAAUAziCf7Ro0fC7fz8/I7WiugKS0vLxYsXC7YPHz7c+SP1ly9fbmpqEi0ZO3as2EV+Mbx6JqfkY9+1i5kp6RpO9jWxiSZblkkcLQC9kLQO6HHCZzXVJ14z3rRU8NJgwbTyLb9pjRpO1tGSrEGMxyeRECLDNQDQK3Q2k52joyOHw5Gs3dTU1MrKSuHLbdu2UalUhFBoaGi79alUqqDCp8hU/2OWPda9HLX+ZqpGBmRNDbKlOufRs6YHuZoj5fEsBAD5JMUDejzUX7yB8fj1F9KEJRiPV3/5lv68KZI1WPXLCTJNp8+iGVIKEAC5Jp5EV6xYIdwuLCy0s7OTrN3nz59HR0fPmjXLwMAAIdTS0iI4M+gowXcX+01xa1EJxdy4+o+41mIGr6aOx2ysOZWk6T4MJsEAQAI9OaDHiZb3CLUB/URLNEcNp5j2bbcyh/FR1diokzv0Lf+8Yr/9gLW26vh7qvWD23lA+YkneNFh8xMnTvz8888la3fNmjWjRo1av359SEjImDFjbt26FRMTI3mYbVCMDTXdnLBWrpqNFdbS2vqOrkLTVh88ALI7AF0nrQN6nKj1N+/iwBoes7Fs/Y+6Qf7CtSrE8fk1JxINQoN4dQ01J+B2HugVPiV4wRPqFhb/etjs8ePH7u7ukjXt6emZnJy8dOnS5ORkNpvdkyjbIutoCY7Bdb7wbPnnpYqutvHW78rX/8SrY6rowaKTAHSJtA7oCVcXl6zhZM+8fFvb173d/wEabmSRdbQ03Z0Rn99wI6spO1fTvf3beRiHw6upVzU2xDlkAHD3KcH/8ssvCCEGg/HXX385OTlpa2tnZ2cHBQWdP39e4tZpNNrJkyfPnTtXWFgohWD/DeNyyerq3Kra5pwCirkxxcRIa7RbXXxqn/BZUu8LACUj9QN6ArUWlTY9emb+68b6pBu1Z64YfjtPrAKf1VSXkGa8MRIhhMhkg6+mVR04ozF8CKm9mTpr45Kb/n5q/utGmCwPKLpPCT4uLg4h9MUXXxQUFAiu1L1//144DL4nZs2aNWuW9JOuYKra2lNJWj4j2C/eIIT0Zk4oXfa9zhej1GyspN4dAMoEjwN6otScSNCbOZGspaE7LaD0u+3sN8XUf9+5r0u8TlJVZf31mPXXY0EJ1sxmpmboThkr1hSHUcG6m61mayWzyfKYqRlqlibqjvJ1cwQoB/EBKcXFxYMGDRJs9+/fX/AUu5zicLk19az7ORiXyy2vrP3zcn3idVUD3ZrTl4iODAB5FxcXFxcXp6amVlBQkJubm5WV9erVq9raWqLj6jbW/Rw+q0lnjAdCiKyhrj9rYs3xBIRh/MYm4QzWGk72OuN8yNqawn+0KWOodtZtW6s5eVF36hd9vpnTkJbJrazBO3hueVVd3NXqP85hHC7eff0/9u48rokzfQD4O7kIkIMrAnKooAIeiAciXmXVFutZKyosVVtXZaVobWtbbW2rtWp3212668+jWi22ttZbrIBaFe+rIiKtonhBIIAhhCSEkGNmfn+kG2M45MhMDp7vxz+Sl8k8TyCvz8zknfcFnZDlILvY2NiEhITk5GSSJHfv3h0dHW2TtFqDNBiYAp5n8hREkqTBgLlzMYzhFjuw3ffIAtDZWPGAniCIgoKCyspKg8EQEBAQFRXFoOV289rdvyCSrPp8o/EpSZLa4sf1N/5QZp5k8Ny6vL8QIeQaFeEaFfHcXWlu3TWUV/GXzcfYLP7Lo+W7MkVvv0Fp8jU7Dwqnx2vvPlRmnRG+Ynk5AYAOsizwW7du3bJly08//cRgMF544QWrXKKnCGnAWT6evL8MQwgpc87xX4hhennYOikAHIm1DuhpWD2yOT5vvU6on1lrSjgtHpcriPoGQ01tQ+HdVl79JnFCnnHA643pGJuFEBK+8mL5W2sabt/n9ulJSd4INRTe05VKRO/Mw4cNrFjxFe+FaKan3a0FABza0wK/ePHiNWvWfPzxxwgh4xRy9+7dW7Zs2YYNG2yWXYtMq8khhJgeArxWCQUegDax1gF9W1ePlEqlKpXKvKW+vr6uru7hw4fGpwEBAcaZrzQaTUVFhWmzJtoxhHhs83ZJqbhq2y7POa8Q6gb91t3dv/4EYzKeu5/6y/nye/e6Z/7KycpFCDXo9eWKmvLNO40n8W3Ip5XtavXNr7cKJo/RiUsRQm4vDJbvPuqTmmy1/UO7o7WXlpYSBIGsi/yfw4cPazSao42QNjJ06NCJEye2sEHVl9vUl/ONjyvXba6/XkhLXgDQRCwWBwYGUrTztLQ0uVye1kj79hYREaFWq81btFptdHR0c9snJCSEPIvNZru4uJieZmZmGrfcsWOH+Watae/u69fNW2Rs3/nXFGXO2Vbtp3v3HoFB+/6zqb6gqL6gaMvqdT0Cg3oEB3c8nybbN729PFjo2SMo2Phv3383lyS/o31QYq39Q7vDtQcHB7u6upJWhZHPLuFaVFR08eLFv/3tbytXrjxz5swXX3zR7hVjOygmJkYkEh09erTJn9ZfL1QdP8+PH+U2pD9CqHrzj9zeIbyxsfTmCACFysrKYmNjxWIxFTvPzMyMj48/deqURfvEiRPbsbdNmzZt2LCh8eqRKSkprdxDy/299fCaWsm7X/j/4z1WF2+EkL6ssvLT/wR8vdLehubIvt2rvffYopH/4gj+iyNskQ6wPSr6u+V38G+99da8efOuXbt24cKFL774Yu3atTk5OU2+0ob0FVLpV98yPQQWl+htmxUADmTq1KkIodDQUIsD+vbtra2rR1Kndl8OiUjZlp9MLaROrzhyyjN5Cv3JtMB7PsyHDyhnWeArKytnzZq1fPny1157beTIkaavxOyKPOOAYMo45ZGTuFxhbGEKBYbKJ7bNCgCHY8UDevPVI21IMHmsW+wgi0a2H0xLBzojywIfFhb27rvv7t2797fffktPTxcIaJr2dd++fRYHExUVFW5ubo23bCi8qyuvFC2br76Ypzx21jiKnukp0N65T0+qADgNhzigbxN21y7srk2vRgNAZ2N5o+rGjRsDAgIOHz7s5+cnk8mM09vRQCqVyp9lMBhwHLfYjMSJmu8OeL0+HWOzGO5uhLqh/totBJfoAWgX4wH9jz/+OGnSJDoP6AEANLA8gxeJRGPGjLl582ZkZGRKSgpt36KlpqZatOTm5jb+70Z17BxDyDcOrEMGg3DaSzU7D7oOjIACD0A7bNy48YcffqD/gB4AQAPLM/iMjIxZs2Z98sknWq121KhR3377rU3Sao4yK1d771Hp3PdL576vE1fKdx4wPJHVX/+d6cHHa1XPfz0AwIzxgL6wsFCv16ekpPTsSdWkLgAA+lmewaenp1+5cmXOnDk8Hu/mzZtDhgyZP3++TTJrUkD6R6Thz0mbJcu+6PLBQpbIi+HmijCMJAhSq4MFoABovYyMjPXr12s0moSEhFGjRq1cudKu+jsAoCMsz+DVajWXyzU+dnNzYze1nKINYS4chrub8R+J40wBn+HuhjAMIcT0FOByuEoPQBsYD+gHDBhgPKBv921yNmGoqrZ1CgDYNcsCn5ycPGXKlEePHu3YsWPcuHHJyck2SatV9DhiM03P4Gt4ANrKzg/oW9Bwu7gsbbX2fomtEwHAflleol+9enVWVtaFCxfKy8vXrVtnq2nsWoM0PJ2LHkGBB6DtjAf0FRUVO3bsyMjIsOsDenMEUbPjgFvMgJod+/3XvmO8hgcAsGBZ4BFCEydONE1XqVKp+Hw+vSm1FhR4ADrIgQ7ozalOXcK4nC7v/q1ixVd156/zRtvvqtYA2NDTAnnjxo0VK1bU1NRMmDBh8eLFK1asKC0tNS7wbMP8WkAacIxlfokeBtID0GYOcUCvr5RK/7nNb83bDHdXQtNQuy/Hd3kKwjCveQnSf213GxrJ4LrYOkcA7M7T7+DfeOON2NjY9PR0iUQSGRkZEBCwYsWKK1eu2DC5FpA4jjEY5pfm4AwegNa7ceNGfHx8dHT0p59+Wl1dvWDBgvj4+F69etk6r6bJdx4i6jW1+7IRQrV7st0G9+OEBCOEXHr3cOnTS3n4V1snCIA9enoGL5fLV61ahRCKiIjIzMw0PrZbpN6AzE7fERR4ANrijTfemDZt2rhx43bu3BkZGblw4cLk5OTu3bvbOq8mNBTe1Ykl/uuWSd77wn1If9Wxs5xuAVWfbTD+FFep66/e5E+MY/J5ts0TAHtj9h0288966e3t7e5uhaUVCYIwXuE3GAwBAQFRUVEMhuWg/fYz4Bj7mQEEUOABaD1HOaD/c3bqudOZXkLhtBflB477fpT67BrXCGNgTPcm1q0AoJNrYpCdVeTm5i5atMjf3z8gIAAhJJFIxGLx9u3bR48ebZX9W4ywQ1DgAWgLqx/QU0R1/DxDyHOL7o8Q4r8cpzp1mdQbXAf1tXVeADiApzWypKTENL5GrVabHqtU7Rm5tmTJkqysrNDQUFOLWCxOSEi4evVqB7J9itQbsMaX6BUqRJJwzwwAzoFQ19fuzeLFxah+vWhs4XQPrMk42HVABMa03uVAAJzU0wKv0WisuF8cx/39/c1bfH19SYsrax1ANrpEj7GYGNeFqKtn8O33dAQAO2HdA3ocx02XBPLz82/fvj1kyJCwsLAOJknq9O6xg0itTvew1NjC4HJcB4QjHEdQ4AF4nqc10sXFmveZpKWlDR48eOrUqYGBgRiGSSSSI0eOpKWlWWXnpN5Qs20PiVn2cKaQj9cqocAD8FzWPaD38/OTSqUIofT09K+++mrMmDEfffTRZ599NmfOnI7slukp9E5JtFKOAHQ6VH0Hn5qaOnny5JycHIlEghAKDg7Ozs5uYfHZCRMmWNySp1KpBgwY0OTGyqOntXcfIY5l8qwu3vqqanaQf5OvAgCYWPeA3uRf//rX9evX/f39pVLpiBEjOljgAQAdQVWBRwg9efLE19d31qxZQqHQ2HL06NFJkyY1ufH+/fu1Wq15y7hx43x9fRtvideqlEdOe82fIftmt/ZBqUtosOlHnEA/fVklMq4WDwCgnY+Pj/G7OR8fH9NFeytquF2MMZgu4SFW3zMAzoeq77HWr1//6quv7t27NyoqKj8/39i4YMGC5rZ3c3PzfBaLxcKaGi4n33WY9+Jwlq8PS+Rd891+ZPa9PttY4AEA9GKz2X379n355ZeVSuXOnTtJkpw/f34LE98mJCSEPqs1k2aSWl31f7+X/t/3pN5g7XcAgBOi6gx+48aNBQUF3t7ehYWF06dPz8vLs8oUmLqHpZqCooD/fqy995gl8iTUGvXlfPfhg4w/ZQf6qU5c6HgUAECbSCQSjUZTUlLy+PFjPz8/kiRDQkLeeeed5rbfvHmzxWi+adOmdenSpeUoisO/ukSEkjq98uhp4bSXrJM6AM6LqgLP5/ONN9f2799/0aJFS5cu3b59e0d3SpKyb/e5DemnLS7R3X+Ma3Rug/vJfzjsNqQ/xmEjhAy1St2jMrhTDgD6ubq6hoeHh4eHG59+9NFHLWwsEolEIpF5C5fLbXkiLIOsVnXsvP8/30cEUbH8K94LMUwvYcfTBsCJUXWJPikpacSIEVu3bkUILV26tKamJjExsb6+viP7JBq0DHdXQ1W18vAJ9aV8/IlUW/SA3bULLlcghEgDXvv9YUQQ6sv51nkPAIB2iYuLs/o+5d8f5L88miXyYvn68MYNl+/+xeohAHAyVJ3Bf/LJJyNGjJDJZAghDMP279+/d+9eT0/PjuyT4cr1/SjV+Fh9Ma/+WoHo7XmmnyqPnmYH+WMuHPmPR9yHRSErTosLAGgLHMetu0Pt3Yfau498Ul8zPhVOjy9fskZ7v8SlZzfrBgLAmVA4in7s2LGmx0wmMykpKSkpyVo7Jw0GjMU2PcUVKuWRU36fv6M6cV7zW6Hq9GX+uBHWigUAaBOr3x1Xd+aaoVpekvzMl/rq879BgQegBRQWeEpZLAYv//EIb8wwdtcu7AA/g1RW+3OWe+wghrurDTMEoNNq4X6Z9vFOSYQZbwBoK4e9jq03oP8tNqN7WKo+f92lV3fNrbuETqcvf8Ly86k9cMy2CQIAAAA25Lhn8AbTXPS4XMmNCFEdP48QQjhuqJBy+4Qiwmrz3gMAAAAOx3EL/NNL9K6D+7kO7mf6kXj+hz6L5zC9PGyUGgAAAGB7jnqJntRbrgdvwg7y04lhPjsAAACdmqMWeITjiNX0TNecQH+YsBYAAEAn56gFnjS0cAbvrystpzkfAAAAwK44bIHXPx1kZ4Hbt1dDQZH5IjQAAABAZ2Mvg+xycnLEYrF5i1QqbWF9Gov74M2xA3wRi6kTV3CCu1o5SwAAAMBB2EuBv3Xr1sOHD81b1Gq1RqNpbvsWBtkhhNwG9tXc+AMKPAAAgE7LXgr8Bx98YNFy8+bNluauNxhQM5foEUKug/oqDh4XvvKitdIDALSMIAjjmu4GgyEgICAqKqrl1eEAAFSzlwLfVi1cokcIcfv1lqZ/R9TVM3hudGYFQOeUm5u7aNEif3//gIAAhJBEIhGLxdu3bx89erStUwOg83LYAt/iJXqMzeJGhGoKivCaWoaQzxsdTWduAHQ2S5YsycrKCg0NNbWIxeKEhISrV6/aMCsAOjmHLfCGZkfRG7kO7ld/KU/zRzHGYroN7stwh1N5AKiC47i/v795i6+vLwl3sgBgU45b4Fu6RI8Qch3Ut2bHfuGMeLxGWbs32+uNBNpyA6CzSUtLGzx48NSpUwMDAzEMk0gkR44cSUtLs3VeAHRqDjsKxvB0Nbmmf14pRQyMwXP3TJqsvpinK5XQlhoAnU1qauqJEydCQkKqq6ulUmlwcHB2dnZKSoqt8wKgU3PYM/jmJ7pBCCGCqPnugMfMCYoDJ/h/iRVOi6/57oDfp4tpTBCAziUoKGjhwoW2zgIA8JTDFvgWL9GrTl4iNA2ckGCWj2fN94fcBvfTPRTX5/3uZrboHADAVjZt2lRaWmreIhaLXVxcbJUPAE7JcQt8S6PoCZWa7eejPHwCIbLu1EW9uMIlNAivqW1hh3iNguklpCBTAJzfli1bzp4927h99+7dTW7v6+urUqnMWwICAvr27UtJcgB0Vo5a4JGh2dXkEELC6fHC6fHGx9KvMxg8N+/5M41PtcWPOd0DMDbbfHvNjT+efPltwH9Wsrp4U5cyAM5q9uzZV69eVSqVqamprdl++vTpFi0ymUwkElGQGgCdl6MOsmv5DN6cd0qi9t6j2n05CCFDtbzy0/8oDv36zK5wvCbjILdfL/kPh5+7t/rrhXrJk/blDICzcnd3f/vtt8PCwsY+y9Z5AdCpOX+BZ7hyfT9MVZ+7psw6I//hEG/0UGXOWUO13LSBKusMy1/U5f0F2gelmoKiFnaF16qq/7NTtqXpq46Uqr9aUPXZBlgiD9ityMjIdevW2ToLAMBTDlvg9TjGbuk+eHNMD77vx2mqnLP1vxXyJ7wgePkF+Q+HjD/CFSrF4V+95k7D2GzP2a/UZBwgcaK5/ch/OsJ/cQTRoFVfzrfCe2g1Uqev2XlQX1ldd+43OuMCAABwXBQWeIIg8vPzc3Jyfvnllxs3bhBEs4WzHVp/Bm/EEnlhbq7uwwZWffpffXmV5vdiTeFdhFDt7qPuLwxld/VFCLnHDmQKeHWnLzW5B92jMk3+bWHCeK950+U7D5JanVXeSGsoMk+69OwmWvq6fFcmoWmgLS4A7RMXF2frFAAAlA2yo3rxCfJ5E91YUJ26hLFZPotn43Xq+kv5upLyqjX/x+7qpy+rZPn5aO88wFgsjMUkVOraH4+4DohguLsihBCTyeC6IIQQSdbs2OeZNInh5soND3Xp1V3xy2mPhPFWeS8tw2tqVdln/f/xHquLt2tkmOLwr55Jk2mIC0C74Thu6xQAAJQV+LYuPpGQkJCf/8x17/Ly8gEDBjS9d5JksNkYsw2XHxT7jyGEJB98aXyKcdiIRO4jBxlktYSqjlDWExoNqdAROj1isSo++jep0yOEEI4TDVrTThruPKje9OOfTy7n1/58tPUJWMBcOK28AkFqdQjDJO/9AyGESJKob1Bln0MMrN2hgV3BmIwuHyx0CQuxdSLWNGfOHFunAACgrMC3dfGJzZs3W9wXu3bt2pCQZv7Xw7DArZ+3KR+/VW8R9fUWe2EHd215QnuTytVq0XKmAAAgAElEQVQbGgrvWjR6zpkmnNLOccKkVkcaDM/dTPugpGrtZs+ZE7D/zQFSf+kGQ8j3efO19sUFdojh5mrrFKxswYIFtk4BAEBZgW/r4hMikcjiLlhvb28Oh9Pc9m39P5Hl59Om7S1YfZpbzIWDuTT77kwY7u78MbEG6dMx/+zgriyRFyyOBwAAoGVUFfjU1NTJkyfn5ORIJBKEkHHxiaCgIIrCOSuX0GCX0GBbZwEAAMDxUDiTXccXn8jJyZHJZE3+KDMz08PDoyM7byuVSsVisVxd6buaShBETU2Nj0+Hrj20lUKh4HK5dM4KbjAYlEqll5cXbRERQjU1NQKBgNWWcZodpNVqBQLBsGHDWv8Siy+tnJ7N+ztJktXV1TRMqEeSpEwmo6Fr0/Z/CG29WK/X19XVeXp6Uh1Ip9PV19fT8KnTarWenp7R0dFU9Hf7nao2MTHR27vpiWMJgrh79+748XQMYjeprKx0dXXt2rUrbRE1Gk1hYWGvXr1oi4gQevz4sb+/Pw39x0Qul0ulUvPxmDQoKiry9vam8xixoqJCLBa//PLLrX+Jp6fn+vXrqUvJrthDf9fr9Xl5eb1796Y6UENDQ0FBAQ1du66urqKigoZACoWiqqqKhl4sk8mqq6ubHZ5lPVVVVTU1NT169KA6UHl5uVgsfumllyjp76QDMhgMTCaT5qBLly5NT0+nM2JJSUlwcDCdEUmSTEhI2LdvH50RL168OHz4cDojkiQ5bNiwy5cv0xlxz549M2fOpDOi06Ctv9fW1gqFQhoCicXiwMBAGgLduXMnPDychkDXrl2Ljo6mIdCpU6fGjBlDQ6DMzMwpU6bQEGjXrl3JyckU7dxRZ7IDAAAAQAugwAMAAABOCAo8AAAA4ISgwAMAAABOyH5H0beAwWBMnkz3fOx9+vShcwg9QkgoFNK/aEdUVFS3bt3ojOjn5zd06FA6IyKEYmJi/Pz86IzYrVu3ZqdeBi2irb+7urq+9NJLNAQSCAT0dG1vb++RI0fSEMjX17dNt4C2W0BAwJAhQ2gIFBwcPHDgQBoCde/ePTIykqKdYyQsMQ4AAAA4HbhEDwAAADghKPAAAACAE4ICDwAAADghKPAAAACAE4ICDwAAADghKPAAAACAE4ICDwAAADghxyvwmzZt6tOnT2RkZG5uLtWx1qxZ06dPn6CgINMqfvRE//LLL7Ozs2mLePz48ejo6G7dum3dupWeoMuXL+/Ro0doaOiuXbtoiKhWq7ds2WJ62jiW1aNbRLTVB8kJUP2LovlPQ0PXpq0709CLaeu5tHVYi0BGFH4qKFqljiKlpaXh4eFqtbq4uDgsLAzHcepinTx5ctCgQRqNRiqVBgcH5+Xl0RP9woULHA4nIyODpOX9yuXyXr16SaVShUIRFhamUCioDnr27NmoqKiGhoaKigqhUFhXV0dpxFu3bs2bNy8xMdH4tHEsq0e3iGirD5IToPoXRfOfhoauTVt3pqEX09ZzaeuwFoGMKP1UONgZfE5OztSpU93c3Hr27Onn51dQUEBdLJlMlpKSwuVyfXx8Ro4cKRaLaYiuVCpXrFiRmJhofEpDROOyxz4+PgKBoLCwkM/nUx2UyWRyOBw2m83lclksFqL4be7evbu2ttb0tHEsq0e3iGiTD5JzoPoXReefhp6uTVt3pqEX09ZzaeuwFoEQ9Z8KByvwFRUVgYGBxseBgYGVlZXUxZo5c+bChQsRQoWFhZcuXYqLi6Mh+uLFiz/99FMvLy/jUxoiisXix48f9+/fPzg4+KuvvsIwjOqgI0aM6N27d9euXYOCglatWuXu7k5pxHXr1qWmppqeNo5l9egWEW3yQXIOVP+i6PzT0NO1aevONPRi2noubR3WIhCi/lPhYAWeIAgMw0xPDQYDpeFIkkxPT58+ffrBgweFQiHV0ffs2ePl5TV27FhTCw3vV6PRFBcXnzt37ubNm9u2bcvLy6M66OnTp4uLi8+cOZOdnZ2eni6RSOj8szaORUN0mj9ITsNp/jS0dW3aujP9vZjOnus0nwoHW02ua9eupaWlxscSiYTS5d1wHJ8xY4aHh8f169cFAgEN0TMyMh48eHDy5MmKiop9+/ZptVoa3q9IJIqPj/f09EQIjRo1qqioiOqg2dnZr732Wnh4eHh4+LBhw86dO0fnn7VxLKqj0/9BchpO86ehrWvT1p3p78W09Vyn+lR08Dt8mpWUlPTr10+r1YrF4p49e1I6Oumnn36aNWuWTaIvXbrUOOaChoh37tzp27evTCZ78uRJcHDw7du3qQ66devWCRMmmMaw5OfnUx3x5MmTpoEtjWNREd08og0/SI6O6l8U/X8aqrs2bd2Znl5MW8+lrcOaBzKh7lPhYGfwwcHBb775pnGF42+//ZbBoPArhvPnz+fk5Pj7+xufbtu2bdKkSbRFN6Lh/YaHhy9cuDAmJoYkyQ8//DAiIgIhRGnQefPm5efn9+nTByH03nvvRUVFUR3RXONfKdW/ZHv4IDkoJ/7TUPTWaOvO9Pdi2nquM30qYD14AAAAwAnBqQMAAADghKDAAwAAAE4ICjwAAADghKDAAwAAAE4ICjwAAADghKDAAwAAAE4ICjwAAADghKDAAwAAAE4ICjwAAADghKDAAwAAAE4ICjwAAADghKDAAwAAAE4ICjwAAADghKDAAwAAAE4ICjwAAADghKDAAwAAAE4ICjwAAADghKDAAwAAAE4ICjwAAADghKDA25eHDx++8sorc+fO/eabb2ydCwAAAAeGkSRp6xzAU++//35SUtLAgQNHjx597tw5W6cDAADAUbFsnQB4xurVq7lcbllZmY+Pj61zAQAA4MDgEr19cXV13b9/f1pa2qZNmygNdPbs2WnTptH5QmuRy+WPHz+2YQIAAOAQoMDbl6ysrCtXrhw6dMjPz8/WuVjNtm3bwsPD/fz8Zs+erdFoEEKHDx8ODw8PDQ3973//a7HxBx98EBQU1LVr12XLliGESkpKYmNjw8PDTVuuXLlSq9XS/BYAAMDhQIG3L4cOHbp79+7MmTNnz57dypfo9fomH7e8JW3y8vLWrVt35syZe/fu1dTU/POf/6ypqVm8ePGJEyfy8/M3bdr0xx9/mDY+ceLE6dOn7927d/v27aNHjx4/fnz9+vWrVq36448/du7cqVQq7927hxAKCwuj/40AAIBjgQJvS++++27v3r2vXbuGENq/f39ERMTmzZuPHj26b9++H374wWLjDRs2hISE9OrV69133yUI4uLFi8nJyUlJSZ999pn5Y4TQ2rVrQ0NDQ0JC3n//fYstG+dgsdvx48cfPnzY+KPo6Ohz585ZbNDW91haWvrmm2/6+fkJBILp06ffv38/KysrLi4uODhYIBAkJSXt27fPtDGPx1uzZo2rq6uHh0e/fv3q6+uFQuHt27fFYnFDQwObzV67du1HH33U1hwAAKAzIoFNHThwID4+vr6+vlu3bjk5Oc1tdubMmQEDBjx58qSuri4xMfHzzz+/cOGCUCgsKioiSdL88bFjx8LCwhQKRUNDw8iRI7///nvzn5rv8JVXXmm82++++2727NkkST548CAkJCQ3N9diA+MLzXf18ssvd2vk1q1bFm9BJpNFR0cfOnTIWKSNjTt27Jg/f37j38mECRMmTZrU0NCgUChSUlLGjRt34sSJ3377bcWKFe38RQMAQCcDo+htbNq0acuXL1+0aFFkZOT48eOb2+zUqVMKhWLmzJkIIalUqlKp4uLioqKiTBerTY9Pnz6dkJAgEAgQQklJSadPnw4JCTHfsuXd/vjjj8uXL9fr9Xv27Jk7d+7p06ctNhg5cqTFTrKzs5/7Nn/88cdVq1atWrXqlVde+f33381/1PiLg9jYWIIgPv/889zc3PHjx2/ZssXYPmPGjG3btv3yyy8bN27s37//559/7uLi8tzQAADQOUGBtzEMw+bPn//xxx+bfxXdGI/HS0lJWb58OUKooaFBp9MVFhby+XzTBqbHJEliGGZ8zGQycRw3/+lzdysQCIYNG5abm7t3796DBw/u27fPYoP8/HyLncTHxzdOPjs7OzIyEiFEEMTrr7+uUCjOnz9vHDkYGBh45swZ42ZlZWVBQUGmVxlHF8bGxiYkJJSWlmZmZpoOek6cODF06FAcx1etWnXo0KFvv/1248aN77zzTgu/NAAA6MzgO3jby8/P5/P55nWusZdeemnnzp1SqVSr1U6fPv3ixYvNbTlmzJgDBw7U1dXpdLoff/xxzJgxbd1tYmLi+vXrPT09e/To0Zq4x48fL2vEWN0RQpmZmRKJ5PDhw6b7AiZOnHjy5EmpVNrQ0LBnz55p06aRJHn37l2DwVBeXv7RRx8plUqlUpmdnR0REWF8CUmSGzduXLx4sVqt9vT0DAoK6t+/v0wma/H3CgAAnRoUeBu7ePHi2bNnhw8fvn///hY2i4qKWrp0aWxsbGhoaGho6Msvv9zcluPHj//rX/86cODAiIiIoUOHzpkzp627nTx58rVr1+bOndumuM05f/78+fPnBQIBn8/n8/mvv/66SCRKT09/8cUXhwwZMn/+/EGDBul0uvDw8PLy8tTU1MjIyMjIyL59+4aHh6emphp3snfv3ilTpnC53ODg4JEjR/bt23fDhg1LlixpazIAANB5wFS1tkQQxNChQ9PS0rp3775s2bIrV66wWPClCQAAACuAcmJLGRkZGIbNnTsXw7CIiIgePXqcO3euR48ets4LAACAw4MzeAAAAMAJwXfwAAAAgBOCAg8AAAA4ISjwAAAAgBOCAg8AAAA4ISjwAAAAgBOCAg8AAAA4Ifu9D37Tpk3/+te/bJ0FALbUu3fvnJwcW2dBB+jvAFi9v9tvgX/8+PHMmTMXLFjQ8mYqQvGrLvNVbksTsgLgiCorK2fNmmXrLGjSyv4OgLOior/bb4FHCHl5eYWEhLS8jaRBXFsie+5mADgcDodj6xRo1Zr+DoCzoqK/w3fwAAAAgBOy6zP4FjxQ381XXkUI1RmUSoNif8X3CCEMMSZ0edWV6Wbr7ADodBQKhUAgwDAsLy/vzp07gwcPNq32CwCwCUc9g3dhct2YPDcmj8t0ZyDM+JjH5qkNKlunBkCns3Xr1piYGIPBsG7duhkzZvz666/jx4/ftm2brfMCoFNz1DP4QG63QG43hJCkQfy76saELq8ihPIVV/dVfr+o23u2zg7YF7lc7uvrq9frbZ3IUzweT6FQMBiOeoRtYe3atYWFhWw2e/PmzQUFBV5eXlKpdPjw4c0Nmvvoo4+Ki4vNWy5dujRkyJD33oPOC4DVOGqBbxKBCIIkbJ0FsDsNDQ0+Pj4SicTWiTzFZrMJgnCaAi8SiXQ6HULIw8PD+KaYTGYL20+YMMHiz5Gfn69WqylNEoDOxuELPI8l6OUOX/UBYEtr164dPnz4lClTevfuHRcX99JLL2VlZS1evLi57UeMGGHR8tVXX7m4uFCcJgCdi8MXeAFL6MHy+uBOCkJISzRoCa3xsSvT7dPe/8YQZusEAXB+8fHx165dy8zM9PDwCA8PF4lEBw4cCA8Pt3VeAHRqDl/gEULxXaYO9/oLQuh31Y18xW+zA1MQQhyMA9UdANp4eHjMnTvX1lkAAJ5yhgLPZbhyOa4IIQHLw4XhIuL42jojAAAAwMacYYyPwiDPrPzZ1lkAAAAAdsQZzuAlDeIjVXtGe7/Y0z3clelu63QAAFQhSBwhxMBaGqIPADByhjP4OoOKROR1xSUhy7MPL9LW6QAAqJL15MAJ6RFbZwGAY3CGAq/GVSKO71X5eVsnAhzbcWlmte5J+177/fff9/ifwMBADMNKS0vXrFnTp0+foKCg9evXWzfVTstAGvTknxMWnarOsm0yANg5ZyjwKoMy2mNEte7JE22FrXMBDuyWMk+qq2zfa+fMmfPof2bOnDl9+vTi4uLDhw/fuHEjPz9/y5YtN27csG62nRxO4j9Ldtg6CwDsmjN8B19nUHEYLkK2x7XaC5N8Z9g6HdCpHT161FjXT5w4kZKSwuVyuVzuyJEjxWLxoEGDbJ2do/qxfFuFtgwhJNVWIgy7q/6DRAimrQSgZU5R4HGlN7MLIrHLtWehwIMW6AjdLdV1kiRNLSQiH9ffN5aKKm1FbnVOgeI6QsibI/Jge5m/VuTi1901tOX9l5eXL1iw4NChQx4eHjNnzjQ2FhYWXrp0adOmTVZ+M51JnHe8Qi9HCF2Qn9TgDf0FgwiSuFtX+FjzwLhBELc7E0beAfAspyjwBlUgtxuX6Vajf1KhLfN3CbR1RsBO1eN1ebWXCfT0zI9ExKP6ByQiEEIqg/Ju/R9sjIMQ8mL7eLK9zV8b5t635QKP4/hf//rXd955Z9iwYX/unCS//vrrzZs3Hzx4UCgUWv/9dBoB3OAAbjBC6LL8bGn9Hwp9DYkQSRLfi/88bJoblNrteYdfAHQ2FBZ4giAKCgoqKysNBkNAQEBUVJTVl9a4ry66p75dpi3hMDhyfbULwzWran+Qa49xPpPgcB405sH2Sun2bnM//fLBJ5N8EyLaeyPG6tWrXV1dly1bZnyK4/iMGTM8PDyuX78uEAjat09gwYvjE+czfrLvTJzE/14485Pe/7J1RgDYL6oKfG5u7qJFi/z9/QMCAhBCEolELBZv37599OjRVoxCILwer2vAGwhEECTOwThyfY2vS1cSkc9/MQDWk5ubu2PHjhs3bmDYnxMk7927l8Ph7NjRGQeCbd26deHChVTsOYIXCcfuALQSVQV+yZIlWVlZoaFPL5qJxeKEhISrV69aMUpv97693fuers6J8x6fZTgwWDhMqqua7DvTiiFA5+HGdHdluLXvtV9++WVdXZ35ImlhYWHnz5/39/c3Pt22bdukSZOskKVdys7OlkqlpqefffaZcWk4q89OH87rZ3yAIcz4ZQoAoDlUFXgcx03/tRn5+vqaD26yFgNpMJB6DoODEPLmdLlTV2j1EKCTeLP7B+1+bXZ2thUzcTh//PHHypUrExMTvby8EEINDQ03b95EzRf4R48e1dTUmLeo1WpPT8/WR2RgjC/7bOtAygA4P6oKfFpa2uDBg6dOnWqc9EMikRw5ciQtLc3qgeoMSncmX8jyDOAG+XC6tHuiEgBAu7333nvDhw9fsWLFnDlzxo4de/LkyfT09Ba2X7169e+//27e8vjxYze3tl0+cWfy2pMrAJ0GVQU+NTV18uTJOTk5EokEIRQcHJydnR0UFNTc9t98882jR4/MW7KysgYPHvzcQHW4isfi+7kEzA78uwavr9ZVdTx5AEBbjRgx4ujRo4sXLz569KhWq21544yMDIuWmJgYkUhEVXIAdEoUjqL38/ObM2cOl8tVKBS//fabXq9vYWOBQGBxgU6pVJaWlj43Sp1ByWPxjY9dmW4MjKHG6+DQHgD6CQSCnTt3/vzzz0VFRbbOBQBAWYHftWvXokWLBALBO++8s3Xr1p49e964cePzzz//29/+1uT2SUlJFi0HDx7k8Z5fp1UGJY/59B4kH06Xal2VuysUeABsIzExMTEx0dZZAAAom4v+448/LigoKC0t3b1797Jly7KysgoLC7/44gurB1LjKj7LosDD1/AAAAA6O6rO4FksVkhICELo9ddfHzduHELIw8ODikAqg5LH5JueekOBBwAAAKg7gx89evTUqVMvXryYlpbWo0eP27dvv/HGG8OHD7d6oDpc5c56WuB92F1kOmkL2wMAAACdAVVn8Nu2bTt06BBB/Dnp98OHD4cMGfL3v//d6oHUBlU31xDTU7gVHjSGYZhcLjet/mIPCIIwzXkHAABUoKrAMxiM6dOnm55SN4dXU4Ps4BI9eIavr+++ffs0Go2tE3lq/vz5TCZMuQoAoJDDryZnvA/e9FTE8YVb4YEFDMOceJpYAABoElXfwdOmzqDkmY2iN90Kb8OUAAAAAJtz/AKPq8xH0SOEfDi+UjiJBwAA0Lk5doHHSVxHaF2Zz0xh7cPpIoOv4QEAAHRujl3gjSvNYOiZ0cgwzg4AAABw8AL/7Ag7IyjwAAAAgGOPolcZlObz1Bp5s0W3VQU2yQcAR7Fo0aIm2zdv3ty+Hebl5ZWVlcXFxQmFQmPL0aNH4eYFAGyoiQJ/8+bNmzdvJicnV1ZWtrDAqz2ow5+5Cd7Im9OlGiazA6BF1i2969ev37Jly8iRI5cuXXrw4MGBAwcihBYsWFBRUWHFKPSo1Eru1v3+gvdLtk4EgI6yLPAZGRnr16/XaDQJCQmjRo1auXLl/PnzbZJZa9QZmrhE780RyfRwiR6AlkycONGihSCIlStXNm5vjY0bNxYUFHh7excWFk6fPj0vL4/Pt+yY5pYsWXLnzh3zlqKiIjuZ2k/SIC5U5UGBB07AssCnp6dfuXJlzpw5PB7v5s2bQ4YMse8Cr+Q3OoM3LgavwestRtcDACz885///Prrr+Vyua+v75MnT/7yl7+0bz98Pt/d3R0h1L9//0WLFi1dunT79u0tbD9//vwnT545Ck9LSxMILPsyAKAjLAu8Wq3mcrnGx25ubmw2m/aU2qAOV3myvRu3e7F9qvVPgpjdac8IAEfyf//3f3fu3Pnwww9TU1OVSuV3333Xvv0kJSWNGDEiJSVl4cKFS5cuffXVVxMTE+vr65vbPjIy0qJFKBRyOJz2RbeKSm25WPMYIfSo/r5cL/ut9iJCyI3p3pcfZcOsAOgIywKfnJw8ZcqUioqKHTt2ZGRkJCcn2yStVtLg9QHc4Mbtxlvhg7jdac8IAEfi4uIiEAgGDhx49erVefPmLV26tH37+eSTT0aMGCGTyRBCGIbt379/7969np6eVk2WWsXqO7+r8hFCcn1Nrb7muuISQoiNcfrwB1jciAuAo7As8KtXr87Kyrpw4UJ5efm6detGjhxpk7RaSUdoOQyXxu3eHFg0FoDnGz169Lx581asWDFjxoyqqirTAPh2GDt2rOkxk8lMSkpKSkqyRo40GeU1bpTXOITQDcXVS/LTi7q9Z+uMAOgoywIvk8mGDRs2bNgw01Nv7yaugdsJPaljY01c1vNhi6DAA/Bc33777b1793r37v3vf/87Ly+v3ZfoAQB2yLLAjx8/3vhAoVAUFxcPGDDg5s2btGfVWjpCx2E0UeC9OV0eae7Tnw8AjqWmpsbHx0cmk0VFRUVFwZfNCCHEZXA5DK6tswDACiwL/G+//WZ6XFhYOGfOHHrzaRsd0fQZvDdHBJPZAfBcjnVAT48+/AF9+ANsnQUAVtDSTHb9+/fX6/Xt3jVBEAUFBZWVlQaDISAgICoqisGw8sy4erK5M3gRrDcDwHM51gE9AKBNLAv822+/bXpcVFQUFhbWvv3m5uYuWrTI398/ICAAISSRSMRi8fbt20ePHt3uXBvTEVp2UwVewPJoIDTNDcEDADTWwQN6h3C19rwb070/f5CtEwGADpYF3nzY/MSJE9s98cWSJUuysrJCQ0NNLWKxOCEh4erVq+3bYZP0pJ7T1CV6DGGebJ8afbWfS4AVwwHgZKx1QO8oSjQPBUwBFHjQSTwt8MbSGxgYaP7j69evx8TEtGO/OI77+/ubt/j6+pIk2a4km6UndE2ewaM/r9JLocAD0AJrHdADAOzQ0wL/9ddfI4QkEsn58+cjIyN5PN7Vq1enTZu2d+/eduw3LS1t8ODBU6dODQwMxDBMIpEcOXIkLS3NaokjhJq/Dx4h5M2GcXYANMu6B/R27qbymkJfixASax67MrhnZScQQiKOrz0MpqszKDkMbpNjiQDooKcFfvfu3QihF1988c6dO8YrdY8ePVq4cGH79puamjp58uScnByJRIIQCg4Ozs7ObmFtugsXLlgsPFVTU9PyehWo+fvgkXEyOz3cCg9A06x7QG/nyhpKjaNuFQZ5A8Z5rLmPEDKQenso8Acqd/V0Cx/hNcbWiQAnZPkdfElJSe/evY2Pu3fvbizP7fPkyRNfX99Zs2a1Zn3oY8eO3bt3z7ylpqZGJBK1sH8SkXpCz2Y0PVu+N0f0h6qz3/ADQHOse0Bv5yZ1STA+2FuxU8AUjO8yzbb5mCNIgkCErbMAzsmywMfGxiYkJCQnJ5MkuXv37ujo6Pbtt63rQ3/++ecWLTExMV5eXi2EMJB6FsZubppoH5itFoDnseIBPQ23xQIA2sSywG/dunXLli0//fQTg8F44YUX2n1E39b1oduhuWnsjLzZcIkegOew1gE9PbfFdpyQ5cFjWvk/ovb54v5Hcn01QkhlUN5UXjtatQ8hNFAYk9h1nq1TA87jaYFfvHjxmjVrPv74Y4SQcQD8vXv3li1btmHDhnbst63rQ7eDntCxmrk+jxDyZHsp9LU4iTMxpnXjAuA0rHVA39bbYgsLC6uqqsxb5HI5juMnT540Ph0yZIiHhwdC6MmTJ7du3TJt1sF2JnLXIOIkOknR/lvfnuA2t+BWAULonOxXX5euM0f/VeghdGfybJUPtNu8/fz581afiOJpgR83bhyXyzVNXdlBbV0fuh10pK7Jm+CNGBjTk+0l1VX5uXS1YlAAnIN1D+jbelvs1q1bi4qKzFvKyso4HM4//vEP49MPPvhg3LhxCKFz58598803tfoaPkvIxJgW7aaXO1z7zUu3vvlmK0JI0iB2Y7oP9RhpbM8+l2NXeUI7be2bN29Wq9XIqjCLTlhUVHTx4sW//e1vK1euPHPmzBdffNHuFWNPnTolk8lmzpyJEMJxfO/evcb30MqXx8TEiESio0ePNrdBeUPplpKv1oT9t7kN0h9+NtZnQqRgSFszB8AelJWVxcbGisViKnaemZkZHx9/6tQpi/aJEye2Y2+bNm3asGFD49tiU1JSWrmHlvv76nvvzuw6N4IX2Y7c7Nx34v/r6R5uXKkWdGZU9HfLUTBvvfUWj8e7du3ahQsXvvjii7Vr17Z712PHjjVWd/S/9aFbX91boyLTB5AAACAASURBVIVZbox8Xfwrte0fNASAE5s6dSqXyw0NDa2srJw4ceLly5fXr1/f7vXgU1NTT5w4ERISUl1dLZVKjbfFtr66P5fSUPtb7UVr7c2ujPQaG8Hrb+ssgHOyHGRXWVk5a9as5cuXv/baayNHjnz48KFN0mqNli/RI4S6uHStaKDk7AcA5/DWW2/NmzfP/IA+JyenfbsKCgqy+l12Ys1jHOEIIQNpUBpqH2seIIREHF93Jq/xxmdkxwcLY/ksgXVzoFov9whbpwCclmWBDwsLe/fdd/fu3fvbb7+lp6cLBPbbW557Bu/n0vWmwppT3wPgZOz5gL4eV39fvhknDAghDa6+p779vXgTwrAXfSbFesY13v6i/HQQt7vDFXgAqGN5iX7jxo0BAQGHDx/28/OTyWTG2TDs03MXi/Nz6Vqla/q2ewAA+t8B/Y8//jhp0iR7O6B3Y7p7sX3UeJ0aryNIwkAa1Hid2qAqUF63dWoAOAbLM3iRSDRmzJibN29GRkampKS0MLmszdXhquK62y1s4M3pojIoWr5dHoDObOPGjT/88IPdHtDPDvi7hqhHCK0rXh7G6zvdfzZCyPxGdhKRxerbhv+d5T+qv68lGhCGwak8AKhxgc/IyFi/fr1Go0lISBg1atTKlSvnz59vk8yeq4HQaEltCxtgCPPmdHmiqwjkdqMtKwAciJ0f0PNYfB7iI4SYGNONyRNxfC020BINWVUHjN/Ty/U1l2vPuDLdEEIjPP/S5GV8ADoVywKfnp5+5cqVOXPm8Hi8mzdvDhkyxG4LvJ7QNz1LrRk3Jq9AeR0KPABNcpQD+mGeLzS56gSX4fp2yCfGx2vvf5DoPy/U3cmXtAeg9SwLvFqt5nK5xsdubm5sdrNTxdnKo/riczW/IoRKNA9xAt9ZtsnYPsV3lifb22JjnDAUq28jNJ3uLAFwBI5yQD/Mc3Rzq04AAJpjWeCTk5OnTJlSUVGxY8eOjIyM5ORkm6TVAg+2V3fXngihGp0UwxjGx0yM6cZ0b7yxO5NXZ1DRnSIADsL+D+iNWnMRjo2xm1tbEoDOybLAr169Oisr68KFC+Xl5evWrWv3NHbU8WR7v+D9EkJIohXfU98xPrZQonmgNtQhhAykQaZ7cltVgBASsj0DuME0ZwuAPbP/A/rWezvkE3aLE2MA0NlYFniE0MSJE03TVapUKquvAmctBkKPmrlqd072q/EGuSe6CpVBmS09iBDq5R4BBR4Ac/Z/QN96UN0BsPC0wN+4cWPFihU1NTUTJkxYvHjxihUrSktLjQs82zC/FhAk0d2tZ5M/mh34d+ODnyU7TlfnvNntA+PYWgCABWsd0Ofl5ZWVlcXFxZnmuz169OikSZOskyUAoO2eTnTzxhtvxMbGpqenSySSyMjIgICAFStWXLlyxYbJtcyADCM9/9LyNhjC+Cx+FcxID8Czbty4ER8fHx0d/emnn1ZXVy9YsCA+Pr5Xr17t29v69etfffXVvXv3RkVF5efnGxsXLFhgvXwBAG329AxeLpevWrUKIRQREZGZmWl8bM+eO1UtQojN4AjYnlW6iubO9QHonN54441p06aNGzdu586dkZGRCxcuTE5O7t69e/v2tnHjxoKCAm9v78LCwunTp+fl5dntV3sAdB5PCzyTyTQ+8Pb2dndvYkS6vdGTejb2nEGzr/gmMhCjXFOCPEbRkxUADsG6B/R8Pt/4n0b//v0XLVq0dOnS7du3WyNNAED7NTHIzlE8dy56hBADY3Z3C82tPkZPSgA4Cuse0CclJY0YMSIlJWXhwoVLly599dVXExMT6+vrm9t+3rx5t27dMm8pKirq27dvB9MAAJh7WuBLSkpMV9XUarXpsUplp/eR6wjtcy/RI4S6uYaWaB7QkA8AndYnn3wyYsQImUyGEMIwbP/+/Xv37vX09Gxu+5UrV8rlcvOWuXPntrA9AKAdnhZ4jUZj3V0TBGEchG8wGAICAqKiohgMy8XrOkJPPGc9eCNPtjdCmFwvazzPHQCdltUP6MeOHWt6zGQyk5KSkpKSmts4JCTEosXd3Z3FcuALigDYoac9ysXlOZe72yQ3N3fRokX+/v4BAQEIIYlEIhaLt2/fPnr0aGuF0JP61pzBI4S6uYaUaB5CgQfAxOoH9Obi4uLOnDlD3f4BAK1B1SHzkiVLsrKyQkNDTS1isTghIeHq1avWCtGa7+CNgl1DSjQPogTR1goNgKOz7gG9BRzHqds5Quibkn9N8Z3lzw2kNAoAjs6a18zN4Tju7+9v3uLr60uSpBVD6MhWXaJHCAW79ijVPLJiaABAC+bMmUPp/mv01XW4nY4NAsB+UHUGn5aWNnjw4KlTpwYGBmIYJpFIjhw5kpaWZsUQrbkP3qibW+geyXdWDA0AaAFMcQOAPaCqwKempk6ePDknJ0cikSCEgoODs7Ozg4KCmtteKpVajO5paGggCKKFEDpCx2ldgRdxfLVEg9JQK2B5tC59AIB90RG6AxU/6EgtQuiJriLnyaFL7FyE0BDh8L78KFtnB4A9onDYalBQ0MKFC1u58ZtvvpmXl2feIpFIOJxm6zeJSJw0MLFW5Y8hLMi1R6nmUT/+wFbmAwCwKyyMGejajSAJhNCdukJ/l4AuLv4IIT5LaOvUALBT9nJfyt69ey1aYmJiRCJRc9sbr89jzawm11g315ASzQMo8AA4KAbGHOU1zvj4kjw3Sji0l3uEbVMCwM5RVeC3bNly9uzZxu27d++2yv51hO6589Sa6+Yakq+8ZpXQAAAAgP2jqsDPnj376tWrSqUyNTWViv3rSV0r75Ez6u7W60DlLioyAQDQAydxJsZECIXx+sG0FgA8F1UF3t3d/e233/7555/N57eyolbOU2vi59IVIVSllfi6dKUiHwAApR7U382qOrCkx4cIoVf9km2dDgAOgMLv4CMjIyMjIynauZ7Ut/ImeJM+vAG/q/KhwAPgiLSEVkc02DoLABwJVRPdUK31N8Gb9OMP/ENVQFE+AAAAgF2xl1H0bdX6m+BN+vAHZJRtNJB6VltG5wEAbOiK/KxxeKzSoKhoEG8u+RIhxMSY84IWQ0cGoGUOW+BJLQdr22Ta7kyen0vAA/W9MB4sOw2AY+jpHm6c7qKsoURtUA4RDkcIcZmuUN0BeC5HLfDtuESP/rxKnw8FHgDbunz5cllZmXlLTU2NUNjElDU+HF8fji9CyL2O/0BdFO0xgqYUAXB8jlrg23GJHiHUlx/1s2THq+g1KlICALTSL7/8cv/+ffOWmpoaHx8fW+UDgFNy1AKvJ3XsNo6iRwj1dAuTaiuVBoUAprcEwHbWrVtn0RITE+Pt3dKt7b4c/4HCGCqTAsDZdKJR9AghBsbsy4/KU1ymIiUAAHW8OaKxPhNtnQUAjsRRC7yO0Lb1PnijUV7jcquPWT0fAAB1SEQWKK/bOgsAHIyjFng9qa/Ry9rxwj78AXpS96i+2OopAQAoUo+rvy392tZZAOBgHLXA6wjddcVFA6lv6wsxhI32evGM7DgVWQEAAAB2wmELPKnFyNauFWthlNfYPMVlNV5n3ZQAANZFIlKN16nxunpcbXrcyp4r1VXJ23WRDwCn4Xij6I3dW4OrEYbUeB0LYzMxJpfh2vo98FiC/vxBV+RnYcwOAPasUJm3rfRrhBCJSA1ev/zO343ty3uuC+AGt/zas7Lj7kz+y12mUZ4lAPbKwQr8o/rifz9cjRBqIDQ4ia8sSkMIQwildn8/gteGhW3GiSZtfvzlSK+xLgwuVbkCADomUjBkQ79dCCE1Xrf8zt+Nj1uPRCQ1eQHgGByswPdw62Xs5J/eW1qmKUnvm9G+GStD3cLCef2PVO2Z4T8XIUSQxC1VXpQg2srpAgAAADbiYAXeBCcNxnP3dpvZde7Ku0tiPeMCud1UBsXOsk1Rfb577quOVO3p7d43nNevI6EBAG3CQAysdf19/f0Vxeo7pqf7K75HCA0Wxr7Z/QOqkgPAXjlYgb9Yc7pCW4YQUhhqMUQerPyRgRgebO9xbf82XcDymOo766fybe+Frmn9q6q0EhHHt62xAAAd4cp0+6Dn2tZsuaLneuOD/RXfuzF5E7q8SmVeANg1BxtF787iuTF5bkweIlEXl648psCNyROyPNq3txe84vWEflf5VvMxuvW42ro5AwA67rmj6gAAFhzsDD5KMDRKMBQhdKo6K8StVzsOz49LM2M944xz0d9S5VVqyx9pii/IThmQ3jhGl4kxV/Rc7+vS1fxVx54cqsNVCKFSzcN6XF3eUIoQ6uXeZ4BgiFXeFwCOTqFQCAQCDMPy8vLu3LkzePDgiIgI26bUyqv6ADgrBzuDNzEgAwMx2/HCa7UXZLonxseDhDEb+u36V5/tQranC4P7n77fb+i36+u+Oy2qO0KIw+AarxwwMZbL/x4zMEf97QFgXVu3bo2JiTEYDOvWrZsxY8avv/46fvz4bdu22TClOO/xsZ4v2DABAGzOwc7gTeoN9W5MN6vsSsjyXNx9+Zri9//5YOX84Ld8mvqKfYzPy8YH5Q0l/fgDYz3jrBK69YrVd45LM9O6L6c5LgCtsXbt2sLCQjabvXnz5oKCAi8vL6lUOnz48AULFtgqJR9OF1uFBsBOOGqBxxAZxu/fyo1JRN5S5ukILUJIjdfdVhVU654ghELdw7zYPgghIduTzxIMFMasvvdutMeIF0WT/V0CqUu+HXSEVks02DoLAJomEol0Oh1CyMPDg8FgIISYzPZcYAMAWBGFBZ4giIKCgsrKSoPBEBAQEBUVZez51tk5ItitvgO+AddckZ8lEIEQUhtUd9SFpQ2PEEIMjOEl9EEICVgey0JX+7sExnrG5cpy/nn/Y0+Odz/+wHBe/2Budx5LYNpVALebN0dkrXcBgHNYu3bt8OHDp0yZ0rt377i4uJdeeikrK2vx4sXNbT9nzpzbt2+btxQVFfXt25f6TAHoRDCSpGSyp9zc3EWLFvn7+wcEBCCEJBKJWCzevn376NGjW7mHmJgYkUh09OhRU4uWaMgQbzTW6eu1F3vz+hnHyg0RDo/2GNHK3a4pfu+1gIU93Hq1sA1B4vfri35X5t9T3ylvKGFiLBHH14PtJWR7ujN5rkw3V4Ybh8FhYWwWxjLNhcdmcNqxRL05N4Yb9uz3+jcUV+/U3UII1eHKJ9rKELfeCCEmxpzhP5eJdaIzJFeGK+a8Ix7cmbwm28vKymJjY8ViMc35tE9tbW1mZqZYLNZoNCKRaPz48eHh4c1tXFZWVlVVZd7y73//Oyws7JNPPqE+UwDsERX9naoz+CVLlmRlZYWGhppaxGJxQkLC1atXm9x+/Pjx165dM29RqVSDBw82b+EwXIZ5vqAjtCQi8xRXIniR/i4BCCFj2bMiBsbs7d63t/uf5xMKvdy4cIXSUFuPqxV6eRUh0RJanDQYSIPpyrme0OkJXUfi1hP1JEmYt+Akblwxz0AadIT2cX0xQghDWPrD1R0J5HA0hMbiN+NMlvT4qJe7jQecd5yHh8fcuXNbuXFgYGBg4DPfggUEBLi6tmFFCQDAc1FV4HEc9/f3N2/x9fVt4WpBZmZmfX29ecuqVass/gvAEGa6Le1UdVYEr387/lt0YXA5DJc2vUTI9hSyPdsayLr+UN08Jj38bsgq26YBQGvExcWdOXPG1lkA0NlRVeDT0tIGDx48derUwMBADMMkEsmRI0fS0tKa297FxcXFxcWihYrE3gn5lIU56tBCABwCjuO2TgEAQFmBT01NnTx5ck5OjkQiQQgFBwdnZ2cHBQVZa/+DhbFdOH7teKGDVnc/l4CBghhbZwFAq8yZM8fWKQAAqBxFHxQUtHDhwo7s4cCBA/fv32/yR0eOHPH0pPWyeV1dHZPJpPNrQoIg5HK5t7e3Wdu7VAdVKBSurq4cTodGC7aJwWBQqVQ0/zXlcrlAIKDzVi6dTufq6jp8+PDWv0StdtRZk9t3+7vN+ztJkjKZzMfHh4ZANTU1z3ZtShAEUVtb6+XlRXUgHMeVSiUNfyODwVBXV+fh0c7pyVtPr9fX19cLhUKqA2m1Wh6PN2zYMCr6u/2ezi5YsKBnz55N/oggiG3bto0bN47OfK5fv87j8VoYGGx1arX6xIkTL730Em0REULnzp0LCAgIDqZv3m+pVHrjxg2LAZVUO3bsWI8ePUQi+u54LCkpkclkbX2bnedU2B76u06nO3ToUHx8PNWB6uvrjx07RkPXVigUZ8+epaFzyWSya9eu0RCosrLy999/pyFQWVnZ/fv3aQj06NEjpVJpDGT9/k46IIPBwGQyaQ66dOnS9PR0OiOWlJQEBwfTGZEkyYSEhH379tEZ8eLFi8OHD6czIkmSw4YNu3z5Mp0R9+zZM3PmTDojOg3a+nttba1QKKQhkFgsDgwMpCHQnTt3wsPDaQh07dq16OhoGgKdOnVqzJgxNATKzMycMmUKDYF27dqVnJxM0c6d9t5iAAAAoDODAg8AAAA4ISjwAAAAgBOCAg8AAAA4IeaqVatsnUObYRgmk8lefvllOoPW1dWFhoZ2796dtohMJrO2tpbmmwXkcvmgQYO6dKFvqU0Mw3Q63ciRI2mLiBCqrq4ePXo0n8+nLSJBEBwOh+abBZwDbf2dyWTKZDIaRtEzmUy5XE5D12YymUql8i9/+QvVgRgMRkNDw6hRo6gOhGEYjuOxsbFUByJJksFgDB06lOpAOI67uroOHDiQip1TtdgMAAAAAGwILtEDAAAATggKPAAAAOCEoMADAAAATggKPAAAAOCEoMADAAAATggKPAAAAOCEoMADAAAATsjxCvymTZv69OkTGRmZm5tLdaw1a9b06dMnKCho/fr1dEb/8ssvs7OzaYt4/Pjx6Ojobt26bd26lZ6gy5cv79GjR2ho6K5du2iIqFart2zZYnraOJbVo1tEtNUHyQlQ/Yui+U9DQ9emrTvT0Itp67m0dViLQEYUfiooWqWOIqWlpeHh4Wq1uri4OCwsDMdx6mKdPHly0KBBGo1GKpUGBwfn5eXRE/3ChQscDicjI4Ok5f3K5fJevXpJpVKFQhEWFqZQKKgOevbs2aioqIaGhoqKCqFQWFdXR2nEW7duzZs3LzEx0fi0cSyrR7eIaKsPkhOg+hdF85+Ghq5NW3emoRfT1nNp67AWgYwo/VQ42Bl8Tk7O1KlT3dzcevbs6efnV1BQQF0smUyWkpLC5XJ9fHxGjhwpFotpiK5UKlesWJGYmGh8SkNE47LHPj4+AoGgsLCQz+dTHZTJZHI4HDabzeVyWSwWovht7t69u7a21vS0cSyrR7eIaJMPknOg+hdF55+Gnq5NW3emoRfT1nNp67AWgRD1nwoHK/AVFRWBgYHGx4GBgZWVldTFmjlz5sKFCxFChYWFly5diouLoyH64sWLP/30Uy8vL+NTGiKKxeLHjx/3798/ODj4q6++wjCM6qAjRozo3bt3165dg4KCVq1a5e7uTmnEdevWpaammp42jmX16BYRbfJBcg5U/6Lo/NPQ07Vp68409GLaei5tHdYiEKL+U+FgBZ4gCAzDTE8NBgOl4UiSTE9Pnz59+sGDB4VCIdXR9+zZ4+XlNXbsWFMLDe9Xo9EUFxefO3fu5s2b27Zty8vLozro6dOni4uLz5w5k52dnZ6eLpFI6PyzNo5FQ3SaP0hOw2n+NLR1bdq6M/29mM6e6zSfClYHX0+zrl27lpaWGh9LJJKuXbtSFwvH8RkzZnh4eFy/fl0gENAQPSMj48GDBydPnqyoqNi3b59Wq6Xh/YpEovj4eE9PT4TQqFGjioqKqA6anZ392muvhYeHh4eHDxs27Ny5c3T+WRvHojo6/R8kp+E0fxraujZt3Zn+Xkxbz3WqT0UHv8OnWUlJSb9+/bRarVgs7tmzJ6Wjk3766adZs2bZJPrSpUuNYy5oiHjnzp2+ffvKZLInT54EBwffvn2b6qBbt26dMGGCaQxLfn4+1RFPnjxpGtjSOBYV0c0j2vCD5Oio/kXR/6ehumvT1p3p6cW09VzaOqx5IBPqPhUOdgYfHBz85ptvGtcO//bbbxkMCr9iOH/+fE5Ojr+/v/Hptm3bJk2aRFt0Ixreb3h4+MKFC2NiYkiS/PDDDyMiIhBClAadN29efn5+nz59EELvvfdeVFQU1RHNNf6VUv1LtocPkoNy4j8NRW+Ntu5Mfy+mrec606cC1oMHAAAAnBCcOgAAAABOCAo8AAAA4ISgwAMAAABOCAo8AAAA4ISgwAMAAABOCAo8AAAA4ISgwAMAAABOCAo8AAAA4ISgwAMAAABOCAo8AAAA4ISgwAMAAABOCAo8AAAA4ISgwAMAAABOCAo8AAAA4ISgwAMAAABOCAo8AAAA4ISgwAMAAABOCAo8AAAA4ISgwAMAAABOCAq8fXn48OErr7wyd+7cb775xta5AAAAcGAYSZK2zgH8f3t3GtDEtTYA+Ez2hCSEAAIKqKACIoitCy64UtHivuKGXRSutrZWrdWqrVvVT22591qsVVtBa+u+AlbrvrRuCNa6byCCyJIQyJ7MzPcjvUgDIkJmkgzv82syGea8IXPynpk5c84Lc+fOHTduXIcOHXr27Hn27Fl7hwMAAMBZcewdAPiHJUuWCASCp0+fenh42DsWAAAATgwu0TsWoVC4Z8+eDz/8cP369ZQWdObMmeHDh9P5hyqVqh5/VZ1SqczJybHJrgAAgMEgwTuW9PT0ixcv7t+/39vb296x2FJ5eXnPnj0rX27atCk4ONjb23vSpEk6na7qllZv5ebmdu3aNTg4+L///a9lg4ULFxoMBlqjBwAAJwQJ3rHs37//7t27Y8aMmTRpUh3/xGQy1bhc+5Z0WrFiRZcuXcrLyy0vMzMzV6xYcfr06Xv37ikUitWrV1duWf2tlStXLl68+ObNm6mpqeXl5ffu3UMIBQUF2eWDAACAE4EEb0+zZ89u06bN5cuXEUJ79uwJCQn57rvv0tLSdu/evW3bNquN161bFxAQ0Lp169mzZxMEceHChQkTJowbN27p0qVVlxFCX331VWBgYEBAwNy5c622rB6D1W4HDBhw4MABy1udOnU6e/as1Qb1+JgxMTErVqyofPnkyZMPPvjA29tbKpWOHDnywYMHtbzl6up669atvLw8vV7P5XK/+uqrBQsW1CMGAABodEhgV3v37o2JidFqtc2bNz9y5MjLNjt9+nT79u2LiorUanVcXNzy5cvPnz/v6up6584dkiSrLv/6669BQUEqlUqv1/fo0WPr1q1V3626w2HDhlXf7ZYtWyZNmkSS5MOHDwMCAk6dOmW1geUPq+5q4MCBzav5888/q27z9OnTFi1aWH2o0tLSTp067d+/v/rnrXxLpVIlJiZGR0cfO3bsypUr8+fPf73/LwAANFbQi97Ohg8fPm/evGnTpoWHhw8YMOBlm504cUKlUo0ZMwYhVFxcXFFR0bt374iIiMqL1ZXLJ0+eHDVqlFQqRQiNGzfu5MmTAQEBVbesfbfbt2+fN2+eyWTauXPn5MmTT548abVBjx49rHaSkZFRjw++ffv2xYsXL168eNiwYbW/tWHDBsv60aNHb9q06fDhw8nJyWFhYcuXL+fz+fUoGgAAGgNI8HaGYdiUKVMWLVp08+bNWjYTi8WJiYnz5s1DCOn1eqPReOPGDYlEUrlB5TJJkhiGWZbZbDaO41XffeVupVJpZGTkqVOndu3atW/fvt27d1ttkJWVZbWTmJiY6sFnZGSEh4fXWChBEO+8845KpTp37pxVX8Ja3jp27Fjnzp1xHF+8ePH+/fs3b96cnJw8a9asWv5pAADQmME9ePvLysqSSCR+fn61bNO/f//U1NTi4mKDwTBy5MgLFy68bMu+ffvu3btXrVYbjcbt27f37dv3dXcbFxe3cuVKNze3li1b1qXco0ePPq3mZdkdIXTw4MGCgoIDBw5UpnCSJO/evWs2m6u/VblBcnLyjBkzNBqNm5ubn59fWFhYaWlpLR8NAAAaOUjwdnbhwoUzZ85069Ztz549tWwWERExc+bMrl27BgYGBgYGDhw48GVbDhgwYPz48R06dAgJCencuXN8fPzr7nbw4MGXL1+ePHnya5Vbd+fOnTt37pxUKpVIJBKJ5J133jEajcHBwfn5+dXfsvzJrl27hgwZIhAI/P39e/ToERoaum7duo8++qjhwQAAAFPBULX2RBBE586dP/zwwxYtWsyZM+fixYscDtw0AQAAYAOQTuwpJSUFw7DJkydjGBYSEtKyZcuzZ8+2bNnS3nEBAABwenAGDwAAADAQ3IMHAAAAGAgSPAAAAMBAkOABAAAABoIEDwAAADAQJHgAAACAgSDBAwAAAExk16luavPVV1/Z+38DgJ21a9fO3hWRJlDfAbB5fXfcgW7KyspWr1796aef2mRvBIlyVOYA2d+ft8JIak2ElwvbJjsHgApPnz7t2rWrvaOgiW3rOwBOh4r63lgu0VcYifGHSo880iOEyvTE+EOlv+UY7B0UAAAAQBXHPYO3LVc+a0us/J00hUpP7Lit7daMNzFUZO+gAAAAAKo0ljN4hFCIO/fb/rIvz6s8hKz5XaX2DgcAAACgUGM5g0cIqQzEV79XDG4tPPfEcOi+bkhrob0jAjTBcTw5OVmn09k7kBdkMlliYqK9owAAMFljSfA6MznxsCLKjz+3i+R2qenddIWEx+rTnG/vuAAdioqKFixY8MEHH9g7kBc+//zz999/H2YHBgBQp7H8vmAITW4nGhUsQgiFuHO3DXJXm2AavUZEIpGsWrXK3lG88PXXX9s7BAAAwzWKBF9mIL48V37ssT5HhU9/UyziYK3ljeKDAwAAaLSY38nuWqExdldJExHrWJxnoQbvv6P4rsJs76AAAAAAajE/wa/4o/yzSMmCblI/CXttX9msTpKPjyv1Zrg+DwAAoE5+vqX9I99oWS43H2QUzgAAIABJREFUEF+cU+HOkEMYnuDvKczP1ERsqxcd5kcECdu6c1ddLLdjVAAAAJxIGznno+PKC08N5QYiPk3BY2NszN4x1YGjJPjevXtj/7RmzZp9+/Y1cLe77mhHBQutvomlUa6nnxhO5sJIdgAAAF6tozdvQ4zbzONlI/eXdPThLezmHCOpOEqCP336tNUo+Z07d3Z3d2/IPo04efC+bnSw9Yh1Yh62uo9syXmV0SkusgDgbDZu3GjvEACwsTZyrlzIKlATffyd5vlqxnYmv1VielhmDvXg+krYZgI9KjO3qdJzvrMPL9idu/2m9t1wFzsGCQAzZGRkFBcXV75cunQpn89HCE2ePNl+QQFgMxVGcnJaaZQff2CAYNpRZVI/WXdfJ0jzjnIGb3OrLlasulgxKlhkJtCM35Sbr6utNpgbKfk+W602wkk8QCoDUbmME0jz+mMkbN26teX/+Pr6YhiWk5MzZcqU1q1bBwQEbN++3abxOpybN28mJCScPHkyOzs7Oztbr9dbFl62/cCBA+X/lJSUdPDgQTpjBqDuvr5c3tGbt7Cb9E1vXnJ/t89OO0cnO4wkHTTMLl26eHp6pqWl1e/PS3REt63Ph7URlhtJI05uiHHjVesU8empsmZi9sxOkgYHCxzas2fP3nzzzYKCghrf1ZnJXtuL1vaV9fTjG3Fy+jFlmCf34471PypmzZr15MmTcePGpaamHjx48PHjx+3bt1coFFwut3IbLper0+lqH8nOMn1kXl5evSOh04ULF+bPn//ll1/269cvLCzsxo0btWys0+n0en3VNdHR0d7e3unp6RSHCYCDoqK+M/YSfY7K3NaDeyrXwOdgx+M8q2d3hNDMjpIhe0vi27nIhYy9kgFeScjBvh/glvCr8qternvv6ARs7IM36p/d09LSDhw4cO3atRs3bnz22WcYhjVv3lwqleI4XjXBM0/37t3T0tJmzJiRlpZmMLyiB6tQKBQK/zEZBIfDwTBn6JcMgPNgbIK//txUpifaenKNZnLphfLlPV1Z1X49mknYAwMEqX9pPoGT+MYhvwL/6DdljYMgyATY9KNKIQdrKeOM3F9SfYNe/vxZrzpO8vPzp06dun//fplMFhUVhRBKTk7esmXLrFmzBAKBLT6BQ5NKpampqTt27Lhz5469YwEAMDfBb/1LI+GxNg90M5jRO+mK1RfL59U0RWxihHjYvpIp7cUSHpw9MJ+3mL20p2v1u1Jmglx9qcKFg+WWE8NbCzv58Kr/rYfoFZd5cBwfP378rFmzIiMjK1e+/fbbPB5v7dq1U6ZMcXV1bfAncAJxcXFxcXH2jgIAwNwETyD0VS8pl4VxeSh1kPxaobHGzfyk7Chf/o5b2qkR0J2e+dgYCvWwvk6Ok+hfvyrkAta2Qe5/FhsTf1UGu7t1bVZDjq/dkiVLhELhnDlzLC/T0tICAgLatm07derUAwcOXLlyJTo62gafAQAA6oaZ9571ZlKpI0Lc//4pd+FiUX4vfaThXx1cfvhTDYPXNlpmgozw4v0n2o3NQh28eJsGytVG4tV/9k+nTp368ccft27dWnkj+eHDhytXrjSbzSUlJdeuXWvZsqWtAwcAgNpQdQaP4zibzbYsZ2Vl3bp1q2PHjkFBQRQVZ+WOwtxSxqmxY111we7cME/uvnu68W2th8QBjQGfjX3whrjyZfsmXIReuzfcmjVr1Gp19+7dK9dkZGRkZmaGhISQJLls2bLAwEDbhAsAAHVDVYL39va2DHyRlJS0du3avn37LliwYOnSpfHx8RSVWNWNIlO452v8RidEiD89VTY2ROQUwwsDB5SRkVF95datW+mPBAAALCi/RP/1119fvXp127ZtV65cWb58OdXFWdwqMbWtdqu1RloTWWEkO/nwPITs4zn6Yi3hFMMXAADAK63LVP9ZZLIsF2rwRWdV9o0H0IzyBO/h4eHj42NZqLxoT7W/SkyhdTuDP5tnGLmvpEhLvN/e5d9XKgbtLs5VwWzxAAAmCPfkTjmiuF5kKtTg4w8pAmSM7VUNakRVgudyuaGhoQMHDiwvL09NTSVJcsqUKT169KCouKpMBJlTZg6W1+lQHhAgGB0iGn+wVMTBHpbh74W7QB0AADBDL3/+2r6yKRmKkfsVY4KFMPVGY0NVMisoKNDpdLm5uTk5Od7e3iRJBgQEzJo162XbP378WKFQVF2j0Wjc3NzqUfQ9hdlXyhZw6no7fWp7l+cafOoR5aBWguz/Xc4CAAAGaCPncNhYhQGPbOYEk6MA26LwbPXWrVtPnz7t3bu3ZXyPBQsWpKWlDRo0qMaNlyxZ8tdff1Vdk5OTIxLVp1v77RJT9Weda4uzxJT2QDcsSHi1wFhuJHJU5haucBIPAHB6livzk9uJ2npwE48ovh8oj2jiBOMlb8rWkIhMiBAjhAo1+MzjZT++LRdxoQv0a6PqEv3KlStHjBixa9euiIiIrKwsy8qpU6e+bPuUlJSr/xQaGtqkSZN6FJ1XgftL65qh7yrM76Yrlvd0Xd3bdXSIECG0/pr1vHMAAOCMvvq9fGKo6F8dxD39+Kt6yxaccY5OdsODhHvv6r7NVFsaKL38+ZDd64eqU9Xk5OTr16+7u7vfuHFj5MiRmZmZEglN473nV+Dd6nwxykyQ3/xvZt9pHcQSLuvryxUlkYQHTD/DLDiOP3r0yN5RvOCwszgCJln31ou7nH2a8/s0d46r9B5C1vYh7mMPlG65oZnS3mVaB/Gr/wbUhKoEL5FIXFxcEEJhYWHTpk2bOXPmDz/8QFFZVvIr8GaSunbXt7qYP7Gd6K7CtP2mpiGzhQJH4+Li4ubm9tZbb9k7kBdCQ0NZLGhE1sfjMvMjlblf878n7zn6WN/Wg+tX5yoPnIKZIM0kYmMIf+1RJcELVCX4cePGde/ePTExMSEhYebMmSNGjIiLi9NqtRQVV9XT10nw1SVEiEfuL5kaIRbVuZsecHBSqRTmN2MMNgtbdLa8ogs5rI1wx23tt5nq3cPc7R0UsCXLlfm4EOHoYNGEQ6UIoQ/fhJP4+qDqHOKLL75YvXq1TCZDCGEYtmfPnqFDh44fP56i4iqZCVSiI3xc6p/g/aTsjt68PXfoaIsAwBiZmZkHDx5UqV7c5U1LS6OiIH8p+6fB8jWXKuafUSVnqrcPlvuI4fSdUdIe6CeFiqZ1EHsIWdsGu/9RYNCa4JZWfVB4kbBfv35jxoyxLLPZ7HHjxn333XcUlbX4vGrF7+UIoWcaXMrHev9cRDTgePhXB/HGbI0ZLg0BUDev26m2HqYdVZ5/arAs56pwVwG2944uPkzUHJ55YZwp7V0qH9lvImJtH+wOnezqhyF1Y3Zn6Ttppct/L/eTcFQGYsvb7qwGHA/hTbjNXdmHH+iGtxHaLkYAHMi0adNqXF+/VvjrdqqdMWOG1U2TO3fuVE7EV6Op7V0SflWu6SNjs9DME0oBG/vxbbfPTqs8hGyopwDUqIYEn52dnZ2dPWHChMLCQj8/P/pjqgcJD0sZ5D56f0mOShPZjF+Pybyt/CtCvPz38qGthQ1pKADgsF42IkX9vG6n2sTExMLCwqprZsyYYRkw42Xe8OZtHOD2boaCJDEhB9s1zL25Kyd1kDw+TdFSxqn6eHdmoTHC6++5JE0E+VexuYOXEzz8DYDNWSf4lJSUlStX6nS6UaNGRUVFLVy4cMqUKXaJ7HXdU5hK9YSYx6ow2ODaepQfX8DBTubqo1sIGr43ABxNbGys1RqCIBYuXFh9fV28bqfadu3atWvXruoaqVTK5b4iDatNJAdDZhIt7OZquTLfyo1zeJSHhPePZvim6xoRR7umr4wgyelHle5Cdgev2poOADCV9T34pKSkixcvtm/fXiwWZ2dnr1q1yi5hva4rz4zTjir/G+0W5csr0RGW+/EN9K8O4u9g0BvAaKtXr27atKlQKGzRooVYLL5+/Xr99kNDp9qzeYZPT5b9EOu+JdZt+e+qC/+7H+8uZPH+Oc3zf6NlFUbi49+UHxwt47Gx5T0hu4NGyjrBazQageDvc1aRSPTKNrWDuK80r3vLrWsz3nMt8UV3Vz1ONqSTnUVMS4HWTJ7LM9giQAAc0bfffnv79u333nsvPT39xIkTzZo1q/euqO5Ue/iBbuNAeUQT7hvevPUxbukP9S/bksfG/h0tu1JovFVi/KavGweGGwCNlfWxP2HChCFDhjx+/PjHH3+Mjo6eMGGCXcJ6XePbiro05SGE8ivwNnLO0ijXht87Z2FoWgfxf67CSTxgLD6fL5VKO3TocOnSpa5du9b7DJ4Ga/rI2v/vRvub3rwVvV56Xm4iyFknVG94cYPcuZ+eUsJIKaDRsr4Hv2TJkvT09PPnz+fn569YsYKeCV5tBSdRkZaw4UOxsa2E6zLVf+QbG95rDwAH1LNnz/fee2/+/PmjR49+/vx57d3cnMXsE2UcFvpPtNxMkIm/KhefVy2Dq/SgUbJO8KWlpZGRkZGRkZUv3d2dZpSoQjXuLmTZ8IocG0PT3xCvy6zo2sxp/gkA1N3mzZvv3bvXpk2bb775JjMzc8uWLfaOyAbGhIgim/I5LMRhYRsHumUVwhzQoJGyTvADBgywLKhUqvv377dv3z47O5uGONLT0/Pz86uuKSoqsjx4U3f5aryZrce0GtJK+G2m+mK+MRJO4gHjKBQKDw+P0tLSiIiIiIgIe4djGz18X0ypwmdjUHNBo2Wd4K9cuVK5fOPGjfj4eHriuHnz5sOHD6uu0Wq1RqPxtXaSX4H7Sm2c4Nks9HFH8epL5XtHeMAj8YBh7NWgBwDQoLaR7MLCwkwmmq5uzZ0712pNdna25ambumvgNDMvM6iVcGO25nSuwVkmWwSgjuzVoAcA0MA6wX/yySeVy3fu3AkKCqI3ngbJr8CpGLKKhaGZncRrLpX38veEge0AU9HZoAcA0MA6wVftNh8bG9unTx9642mQ/Ap8cCtKBp6LbiHYkKU+8kgXGwijXgPmcOoGPQCgdi8S/KVLlxBCvr6+Vd++evVqly5d6A6qvp5rcK8GTBRbuzmdpfPOlL3VQmA1bBYAzsupG/QAgNq9SPD//ve/EUIFBQXnzp0LDw8Xi8WXLl0aPnz4rl277Bfe6ynSUpjgI5vx2rhxt93Uvh/+en37AXBADGjQAwBq9yLB//LLLwiht9566/bt25YrdY8fP05ISLBbaK/JgJMmAllNO2FDQ/eWfPCGeMFZ1cg2wvSH+nsK05IoGD0DOCsGNOgBALWzvgefm5vbpk0by3KLFi0KCgpoD6meirSEp5DCUac/6ST57FRZZFPejN/KcsvN2wfD0DfAiTl7gx4A8ErWCb5r166jRo2aMGECSZK//PJLp06d7BJWPZRoCQ8RhQm+tz////rIPjqm1OHklrflfrZ+4B4A+jlvgx4A8ErWCX7jxo0bNmz4+eefWSxWr169nKhFX6TFPUXUJt1CDS7gYiRCX19W9/CDZ+KB07Nhg54giOvXrxcWFprN5mbNmkVERLBYMI8bAPb0ogbOmDGjrKxszpw5Dx488PHx8fLyunfv3pw5c+q3XxzHK5ezsrK2b99+9+7dhgZbq2It0YTKM/ifbmq/u6beO8JjbV/ZrRLT99ka6soCgB4bN27s2bPnzz//vHPnzl69en3//ff128+pU6fatm07a9as7du379y5c86cOUFBQWfPnrVttAxz8L7O/L+Z7hQ64vQTmJka2NiLM/jo6GiBQFA5dGUDeXt7FxcXI4SSkpLWrl3bt2/fBQsWLF26lLqhskq0uAeVZ/Dn8gw/DXH3k7D9JOxZncXrr6knhYpEXHhkDjilGTNmLFu2bNGiRQghHx8fhJClQb9u3bp67O2jjz5KT08PDAysXJOXlzdq1ChLX/3q1q9f/+TJk6prHj58WFBQMG/ePMvLsWPHdujQASF07ty59PT0ys2YtH7xur1LMBTdQmDAySueMe/EdO7tz3fAOGE9Pet37NihVtt4dvIXCX7o0KEIocDAwAsXLrz//vsLFy48ffr0qlWrGljA119/ffXqVR8fn+Li4u7du1OX4It1RDsP2w9jV+n7AW6Vy4kdxPeV5v9crZjfVUpdiQBQx7YNehzHLa2ESl5eXiRJvmz7Jk2aVFRUVF3D5XL5fL6b29+1rHLiWjc3t8qVDFs/+g3vg/f1p4tJlYGMi2qSECF2zDhhPT3rXV1d2Wxbn6OS/9S/f/8dO3ZcunSpV69e586dGzBgAFkvHh4eloX27dtbFgiCCA4OrvseOnfuHBsbW/ftp2Yofnusq/v2DaTU4ZGphX8WGWkrETQ2eXl5vr6+lBZx+/btzZs3kyS5YMGC7t27nzt3rn77SU5ODg4O/uyzz9atW/ftt99+/vnn7dq127BhQ9338Lr1nRmeqc1hm5/1/6XIhNs7FGBvVNR365vWhYWFY8eO3bdv38SJE3v06PHo0aP6tRu4XG5oaOjAgQPLy8tTU1NJkpwyZUrVYbNsrliHN6G4k11VMgHrs0jpvNOqyrtoADidjz/+WCwWX758+fz586tWrfrqq6/qt5/p06cfO3YsICCgpKSkuLjY398/IyMjMTHRttFSbf89Xdbzv2ew1JrINZcq8Jdeg7CBUh3xbrrivXCXFjLOR78p4ZcE2Jx1L/qgoKDZs2fv2rXrypUrSUlJUmk9L0EXFBTodLrc3NycnBxvb2+SJAMCAmbNmvWy7f/v//7PqjGRk5PD4dQ22Z0Vqh+Tq25YG+GhB7pN19XTOojpLBcAW7E06OfNm9fABj1CyM/Pz4keuqlRExEr4Vflhhi3EHfuexmKABmH0mGp30lXDAgQfNxRYiLI6UeVyy6oYOwsYFvWGTQ5OXnbtm0HDhzw9vYuLS21jIZRP0KhMDg4ODg42PJywYIFtWwcHh5e9c4EQui3334TCus6swuJUImO8KByoJsaLe/pOmxvSd/mgiD5a7RFAHAQtmrQM0N3X35SP1nCr8qmYnaoB3d5T2rT7Td9Za3lHIQQl4V9FyPPKzdTWhxohKzTkqenZ9++fbOzs8PDwxMTE/38/OiJY+DAgVZrfvjhB4GgrlPDqfSEiIvRPw1MUzF7XqT04+PKgyM9+DAJDXA2NmzQM8Ob3jwfMeuB0vx5VynVc0O3rnJWwGGhljI4SQA2Zn1IpaSkrFy5UqfTjRo1KioqauHChVOmTKnHfjds2HDmzJnq6yn6BSnSEp70Xp+vNCJIeOqJ/j9X1XO7SOwSAAD1Zq8Gfb0p9YSb4O+abiJIgxmJbTf9hM5MTjmiaOfB+6yLYMZvyuT+bl2a8my1cwDoZ50Uk5KSLl682L59e7FYnJ2dXe/H5CZNmiQQCIxG45R/anDA1pIz1ct/Ly/W4R5C1gOlOWZnscZEZceYmizp4Xrgnu5SgZHmcgFooJSUlLFjx37xxRcGgyEqKmrz5s32jqg2FUYyZmfx2TwDQkhnJienKX66acvxplZfrGgu5azo5Rrlx0/qJ/vkRBmlnewAoJr1GbxGo6m8MC4Sibjcej5Z7uLi8sknn+zYsaNfv34NCvBV4sNc3k0vvV1icuGxJqcp5nSRuNA++IxcyPqmr+yTE2UHR3rY60ICAPVgadDHx8dbGvQdO3akohVuKxIe9uPb8vczFIujpNtvan0lbMuz47Yyv6uk8jZflB//9HhPuO0GnJp1NpowYcKQIUMeP378448/RkdHT5gwod67Dg8PX7FiRcPCezUJD9sS6/5IhZ99op/dRTK8TV375dlWZDPeyCDhJyeUBDT5gfOwVYOeNu08uetj3D45XoZhaFVvWd1vkxdqXgyeTf7zZSWrTjz09+kBwLasE/ySJUtmzpw5ePDg/Pz8FStWLFy40C5hvZZiLa4xEC48Vmah0Y7pdWZHiZlA32XZeKxBAKhjwwY9PXRmMulKRU8//r1S86kn+rr/4Yzfyv57VY0QIhFafE615Hw5ZTEC4Chq6LcZGxsbGxtrWa6oqJBIHLrv2AOlOT5NEezBHdFGuPuOdun58i96SO3S8Gaz0H+iZSP2lYZ6cHv7w1xzwAksWbIkPT39/PnzlgY9pUNRNZyZQO9nKHwl7FW9ZdeLjIm/Kv8djXVrVqe69n2M28TDpSRCZXriZok5JVZOdbQA2N2LM/hr167FxMR06tTpyy+/LCkpmTp1akxMTOvWre0YXF1kFhrnRUr4bMzflZ0yyL3CSGhp72RXycuF/W1/tzknyx4o4ZFW4BxiY2NXrly5aNGiHj16WI0P7yBSbmj239MhhEwEGeXLVxlJA0528OJtHijH6zz6m1zI2jbYfdsNzdHH+pRYuQ373gPgsF4k+Hfffbdr165JSUkFBQXh4eHNmjWbP3/+xYsX7RhcXYwNEQ1pLSzR4R5CtoSHre0ro7+TXVUdvLhzIyXTjyrLDTDyJHBcTtSg7+XHX3upYs8drd5Mpj3Ut3HjCDkYQii8CTfKr66XykiEvs1U+7lypDxsPdxHA43Di0v0SqVy8eLFCKGQkJCDBw9alp1Fsf2eg69uTLDovsI87ZgyJVbOpXqwDADq5d133x0+fHh0dHRqamp4eHhCQsKECRNatGhh77hq0FLG2T5EPu6gIumqenArwezO9blpuPxC+V8lpm2D5AacnHi4VMLDYIRpwHgvkmLlRHXu7u4uLi52iqc+TASpMZEygaMkeITQ/K5SVx5rzkkV9KkHjsnSoO/Ro8eqVavMZvPixYt79+7dkASfmZl58OBBlUpVuSYtLc0GgSKEEJLyWGIeVmEg/KXW3YZUBmLQ7pK8ir97xa/6o3xjdg0n6K3dOJYr8+5C1k+D3b1d6JuYCgB7caCkWG8lWkIucKwzZRaGvukne1puXnvJEW9qAmDbBv3KlStHjBixa9euiIiIrKwsy8qpU6c2cLcWCh0xKU3RrwX/0CiP5Ez1z7e0Vd915bMmthNNOFT6pBxffaniQr5xTIio+k7i2ooqb965C1n2ep4WADq9aA7n5uZWdpjXaDSVy47Z76aqYi3RxMXhWioCDvbD2/K4Q6USHvYvuBgIGC05Ofn69evu7u43btwYOXJkZmamDZ++2XZTE92C/0knCUJo22D5h8eUw9sILbfhLeJCRAihIXuKvcWcHUPlMr7D/RoAYBcvErxOp7NjHA1RpMU9hY54wU0mYKXGyuMOlkp4rAmhNZxVAGAvtm3QSyQSy2WAsLCwadOmzZw584cffrBVqB93fNFWCJBxMsZ4Vt/mSTku5rHURqLcQMrgGVUAEEJVEzyfb89qMXny5Js3b1Zdc+fOnZCQkFr+5FSuwUiQMS0FJVrCw4X19eWKd8Nc5LTPGFs7Lxf21sHuEw6VIoQgxwPHYdsG/bhx47p3756YmJiQkDBz5swRI0bExcVptdqXbX/s2LGcnJyqa4qLi+t90r/89/Krz4wZoz0OPdBPOlz60xB3P4kjtvgBoJmjTFC4bNmy4uLiqmveeecdT88amuqV/KTs+DSFCScLNXh2ocnLhSXhO9SN+L/5Sdg7hrpPOFRqIsh3wpyp9yJgMNs26L/44ovu3buXlpYihDAM27Nnz65du9zc3F62fWZmplWC12g09WtzlBuIZ2p86yC5lM+aGCriYOh8nmFcW2hMA4AwknTQjt5dunTx9PSsvSPuwzJz/GGFmSTd+KyDoxx6Rvb8Cnzi4dLRwaLpb8D9eFAnT58+7dq1a15enr0DoUNd6jsADEZFfXesC9qvq4UrJ9STq9CS3Xx5jpzdEULNJOxdwzyOPNIvOV8OE9IAZuvdu7e9QwAAOHOCx0k091SZCSdbydlpDwwH7jl6J0FPEevnIe53Sk0zj5cZYKJpwFw4XsNcbQAAmjlxgt9+U6PUExsGuKkM5H+iXVdfqqhxCkiHIuFhqYPkPDYaf7C0WAtj2QJmio+Pt3cIAABnTvAT2rpsGijnsTGlnujowzs93tMpRqfisbE1fWV9mwtG7i/5s8hk73AAsD1bDXEDAGgIJ07wbBZiY0ipI8Q8jMvCeI59D74qDKEP3hQv6iadckSx49ZLHyUCAAAA6s2JE7xFkRb3dLBn3+vorZaCnUPdt/6lmX2yTG2EW/IAAABsySlTY1XFWqKJM1yZr1FLGWfvCA8hBxu8pzgbLtcDAACwHSYkeMeZKLYehBxseU/X+V2liUcUay5VGKF3PQAAAFtw4tRoUaTFm4ic9Qy+Uv+WgvQxno/LzIP3lFwrNNo7HAAAAE6PAQmeaOLMZ/CVPISs9TFuH3eUfPhb2YIzqjIDPEQHAACg/qhKjVVHusjKytq+ffvdu3epKKhYi3s4/xl8pbcDBcfGevI5WMyO4m1/aXHI8gAAAOqFqgTv7e1tWUhKSho0aNCvv/4aExOzdetWmxdUpGHIGXwlMQ/7orv0p8HuJ3L1b+8uXnhGlf5Qb3mrzEB8cgJGwQMAAPBqlKfGr7/++urVq9u2bbty5cry5cttvv9iHdMSvEVrOSclVv55V+nlQuPsE8q1l8rLDMSkwwovFxZ1o+5rTWTCEaVC9/d1gx23tak3NBSVBXbf0Zr/d4XmmRo/lWuwazgAAKahPDV6eHj4+PhYFtjsl15L79OnD/ZPly9ffvbs2Sv3X6zBPZ32MblX6uXPPzrWc26kZON1Tdetz5uKWXO7SKkrTsTFQj05cQdLi7XEjtva9dfUfZsLqCuukTueY/joN6WZQAVqfNyhUscfaLnuNm7caO8QAACUzQfP5XJDQ0P9/f3Ly8tTU1Pj4+OnTJnSo0ePl21/6tQpqzWW6SNrL0VtJFksJOI4zRh29YAhNCpItOuO7okKv1NqHrCz+L1wl2FthAJqPvXHHSUIocF7itksbMcQdz8pYxtPdvdtf9mHx8oSjigeq8zvhLk49RTmGRkZxcXFlS+XLl1qmW9+8uTJ9gsKgMaOqgRfUFCg0+lyc3NzcnK8vb1JkgwICJg1a5Ykeg7vAAAQgUlEQVRtSynS4p5ChmegcgMRn6bo6csf21806bBiSGvByVzD11cqRrYRjmsrau5q+2/Q24WtN5NyIYuiNgSw4LKwhd0kA3eVNHdlTwx1sXc4DXLz5s2FCxfGxcXJ5XKEkF6vz87ORpDgAbArCi/RC4XC4ODgAQMGREREsFisBQsWCIVC2xZRrCWauDDwBnxVm//UdPflf95NGijjpMTKjz3Wr+sv2zfCA8OwMQdKJxwuPXBPpzXbrNvdztva5GvqQ6M8h7QSTDwMU95R6Jkaj09TfNJZ7CvhWK7VO69PP/305MmTjx8/HjRoUFJSko+PT1JSUlJSkr3jAqBRo+oMnh7FWtyTQc/I1WhWJ0nlchs55+hYT4SQn4T9WaRkVmfxyVzD7jvaJRfK+/jzB7cSdvflNWTSHa2J/OmmdvsQdz8Je2YnCQvDdt3WfvCm2AYfg3Znnhg6+fBEXAwhZMDJ808N/RysP8HEw4rJYS7vhLnEtyOnH1Wu+KP8i+4UdrCgWvfu3dPS0mbMmJGWlmYwvKLD4JMnT6pe0kcIabVagnDmNg4Ajse5EzxjRrmpHy4Li2kpiGkpUOiItIe677PVs0+a+zTnv9VC0NOPb8ltr0XExQ6P8qh8+VFHp0ztFueeGtZfU2+JlbNZ6F+/Kr1c2I6W4FMGyf0kbIQQl4V9FyMv0Tl9JzupVJqamrpjx447d+7UvuWiRYtu3rxZdU1eXp6XlxeV0QHQ6Dh7gsedeiB6W5ELWfHtXOLbuRRpieM5+p23tZ+dLotowovy4/f047eRO/e3XD8LukmXnFfFpymkfEzCw77q6WrviKxZsrsFh4W8mfIwSFxcXFxcXO3bpKamWq2ZO3fuKzvVAgBei3P/9N8tNTt132ObayJijW8rGt9WpDWRv+cbzuYZEn/V6Mxk12b8Lj68jj68Vm7O/Y3XHYbQ/K7S/juKclTo5LgmbGgH0qh3796nT5+2dxQANHZO/HNvIshrz43/jpbZOxBHJOJi0S0E0S0ECKGnFfjFfMOlZ8YN2Wq1kezgxe3gxWvfhBvmyZXyGZv3jDj5wTFl+yY8NwHr/SOKlFi5y+vfswD1U3WkagCAvThxgs96bgqQcVyZm6JsxVfCHhUsGhUsQgg91+DZRaasQmPyNfVfxSZ3ISvUgxviwQ2Sc4Lcub4SygbJo92ckyopj7Wmr4yFoS/Oqj48ptwSK7d3UI1FfHy8vUMAADhzgr/w1NCtGd/eUTgZLxd2TEt2TEsBQogg0WOV+VaJ6Xap+Zdb2nsKc5meCHTjBLpxAlw5LWWcFq7s5q4cJz3xnfGmOMCNY2mwLO3pek9htndEjcjUqVPtHQIAwJkT/O/5xk86OXE3b7tjYShQxgmUcQa3+nuN2kg+LDM/UJoflZnTHuhyy/FclVnExfwk7GYSTjMJu6mY3VTC8nFhe7mw3YUOfe2kdZWuhRhCQY2ypyEAoDFzyl+9pxV4qZa4W2p6w4uHELpaaAyScyU8pzzRdChiHta+Cbd9E27VlcVa4mmF+WkFnl+B31eazubhz9TEcy2uNpKeIpYl03uJWO5CtlzIkgtY7kKWm4AlE7DcBBiXBV8KAADYB4UJniCI69evFxYWms3mZs2aWcazs8men6nxhCPKQBlXwMEyHuqXXVDtHeEh4THkKSNH4ylieYp4Hao9omzEySItUaTBS3XEcy1RqsPvK0zFWkKpJ5QGokxPKPUEj43J+CxXPkvKx6R8lpSHSXgsMQ8Tc1kSHibmYSIuS8TBxDzMhYsJOJiIi4m5LOjxDgAADUdVgj916tS0adN8fHyaNWuGECooKMjLy/vhhx969uzZ8J138uF1acq7kG/4Lkuz9YY6ZZB7UzFkd7rx2JivhO0rqe0/rzGRZXpCZSDKDWS5kSg3EBVGUm0iS3V4TjlZYSC1ZkJrIjUmUm0k9WZSZybVJoIkkYTH4rCQiIsJOBifjQk5GI+N8dlIwME4LMwyho+lfyWfjQRsDCEk4rK4LIQQcuFhbAxDCPE5f7+FEJLyX1xMqLrMZSNhlSH32RgmhktBAABGoCrBf/TRR+np6YGBgZVr8vLyRo0adenSpRq3v3XrVkFBQdU1KpXKzc3tZfvPLTfHBgq/uVy+tp8b3F51WC5czIXLblZrI6A6gkQVRsJEIJ2J1JlJI05qzaQJJ/U4aTAjE0HqTCRCSGUgEEJ6nFQZSYTQM43JRCCEkMZI4iSJEDKYkR7/e5R+y8YW5Qaicux+E450VUbyx0lSbbQe2L9qg6ASj41eNhkPn43VPk8P939tFIt5kZK2HtxatgcAgHqgKjXiOG6ZBr6Sl5cXSb50TpTk5OR79+5VXVNaWtqiRYuXbS/js04/0S+Jcl1xQdVcyo5oAr+PzMHC/j47RzaenKieqjYIKhlxpH/JHD8GnHzZWxYmgtSa/t6Aw8ICZNBCBQDYHlW/LB9++OGbb745dOhQX19fDMMKCgoOHTr04Ycfvmz75ORkqzW1DF35R74xR2VOHeQeJOd4u7ATjyj2jPDwe83TRADqiMHDAQEAGIyqBD99+vTBgwcfOXLEcuHd398/IyPDz8/PJjsPkLF3DfPwk7IRQn2b87fEyt0F8BMMAAAAvEDhtUE/P7+EhISG7EGhUDx69KjGtx4/fvyI8yL44ho3simDwcBmszkcWq+majQaFxcXOkvUarVCoRDDaO1oRv/HpL9EgiCEQmGTJk3q/ieFhYXUxeOAaq/v9FQ92g4MKMjBCyJJUqfTiUSUz3VCEISLi4uHhwcV9d1xb/61bNly7dq1u3btqvHdR48ecbm03nfHcRzDMFs96VcXJEniOE5zk6KRfEyz2cxms+lsxxAEwWazfX19X+uvwsPDKYrH0ThCfSdJ0mw201MQPcc88woiCIIgCIYVxOFwLI+b2b6+k07I8utMc6EzZ85MSkqis8Tc3Fx/f386SyRJctSoUbt376azxAsXLnTr1o3OEkmSjIyM/OOPP+gscefOnWPGjKGzRMagrb6XlZW5urrSUFBeXp6vry8NBd2+fTs4OJiGgi5fvtypUycaCjpx4kTfvn1pKOjgwYNDhgyhoaCffvppwoQJFO0cbl0DAAAADAQJHgAAAGAgSPAAAAAAA0GCBwAAABjIKRM8hmE8Ho/mQul/Ro7D4bDZdI/eAx+TInb5mMxAW32n7aig7WCAghp5QRj58uFjHVl+fr7luQLaKJVKHo9H88PT9H/M58+fy+VyOh9BJEny2bNnTZs2pa1EhFBBQYGPjw+dj8mZTCalUvlaz8GDSrRVBCiofmirxQRBPH/+3GocdCqYzebS0lIvr2rTaNqa0WhUqVQvG7a1gZw1wQMAAACgFk55iR4AAAAAtYMEDwAAADAQJHgAAACAgSDBAwAAAAwECR4AAABgIEjwAAAAAAM5X4Jfv35927Ztw8PDT506RXVZy5Yta9u2rZ+f38qVK+ksfc2aNRkZGbSVePTo0U6dOjVv3nzjxo30FDpv3ryWLVsGBgb+9NNPNJSo0Wg2bNhQ+bJ6WTYv3apEex1IDED1P4rmr4aGqk1bdaahFtNWc2mrsFYFWVB4VFA0Sx1Fnjx5EhwcrNFo7t+/HxQUhOM4dWUdP378jTfe0Ol0xcXF/v7+mZmZ9JR+/vx5Ho+XkpJC0vJ5lUpl69ati4uLVSpVUFCQSqWiutAzZ85ERETo9fpnz565urqq1WpKS/zzzz/fe++9uLg4y8vqZdm8dKsS7XUgMQDV/yiavxoaqjZt1ZmGWkxbzaWtwloVZEHpUeFkZ/BHjhwZOnSoSCRq1aqVt7f39evXqSurtLQ0MTFRIBB4eHj06NEjLy+PhtLLy8vnz58fFxdneUlDiZZpjz08PKRS6Y0bNyQSCdWFstlsHo/H5XIFAoFlZFBKS/zll1/KysoqX1Yvy+alW5VolwOJGaj+R9H51dBTtWmrzjTUYtpqLm0V1qogRP1R4WQJ/tmzZ76+vpZlX1/fwsJC6soaM2ZMQkICQujGjRu///577969aSh9xowZX375pVwut7ykocS8vLycnJywsDB/f/+1a9diGEZ1od27d2/Tpk3Tpk39/PwWL17s4uJCaYkrVqyYPn165cvqZdm8dKsS7XIgMQPV/yg6vxp6qjZt1ZmGWkxbzaWtwloVhKg/KpwswRMEUXX8cLPZTGlxJEkmJSWNHDly3759rq6uVJe+c+dOuVzer1+/yjU0fF6dTnf//v2zZ89mZ2dv2rQpMzOT6kJPnjx5//7906dPZ2RkJCUlFRQU0Pm1Vi+LhtJpPpAYgzFfDW1Vm7bqTH8tprPmMuaooHVCrYZr2rTpkydPLMsFBQWUzm2A4/jo0aNlMtnVq1elUikNpaekpDx8+PD48ePPnj3bvXu3wWCg4fN6enrGxMS4ubkhhKKiou7cuUN1oRkZGRMnTgwODg4ODo6MjDx79iydX2v1sqgunf4DiTEY89XQVrVpq87012Laai6jjooG3sOnWW5ubrt27QwGQ15eXqtWrSjtnfTzzz+PHTvWLqXPnDnT0ueChhJv374dGhpaWlpaVFTk7+9/69YtqgvduHHj22+/XdmHJSsri+oSjx8/XtmxpXpZVJRetUQ7HkjOjup/FP1fDdVVm7bqTE8tpq3m0lZhqxZUibqjwsnO4P39/T/44IMePXoghDZv3sxiUXiL4dy5c0eOHKmcl3DTpk2DBg2irXQLGj5vcHBwQkJCly5dSJL8/PPPQ0JCEEKUFvree+9lZWW1bdsWIfTpp59GRERQXWJV1f+lVP+THeFAclIM/moo+mi0VWf6azFtNZdJRwVMFwsAAAAwEJw6AAAAAAwECR4AAABgIEjwAAAAAANBggcAAAAYCBI8AAAAwECQ4AEAAAAGggQPAAAAMBAkeAAAAICBIMEDAAAADAQJHgAAAGAgSPAAAAAAA0GCBwAAABgIEjwAAADAQJDgAQAAAAaCBA8AAAAwECR4AAAAgIEgwQMAAAAMxLF3AMBuRowYkZWVpdfry8rKvL29EULjxo2rqKhYt26dvUMDANgY1PdGCCNJ0t4xAHs6c+bM8uXLf/vtN4SQXq/X6/UymczeQQEAKAH1vVGBS/TghRMnTixatOjEiRPR0dFhYWF+fn5r1qwZMmRISEjIsmXLLNssXry4VatWERERKSkpdg0WANAgUN8ZDy7RgxrcvXv31q1bt2/fjoyM/Ouvv5o3bx4YGPj5558fPXr0/Pnzf/75p8FgiIyMjIiIiIiIsHewAIAGgfrOVJDgQQ369OkjkUiCgoLatGnTtm1bhJCbm5vZbD537lxhYeHYsWMRQjqdLisrCyo8AM4O6jtTQYIHNWCz2ZYFLpdbdb1QKJw9e/a7776LEFKr1Xw+3w7BAQBsCuo7U8E9ePAa+vTps2XLFqPRqFKp2rVrV1hYaO+IAABUgfru7OAMHryGqKiot956Kzw83GQyLViwwM/Pz94RAQCoAvXd2cFjcgAAAAADwSV6AAAAgIEgwQMAAAAMBAkeAAAAYCBI8AAAAAADQYIHAAAAGAgSPAAAAMBAkOABAAAABoIEDwAAADAQJHgAAACAgSDBAwAAAAwECR4AAABgIEjwAAAAAANBggcAAAAYCBI8AAAAwECQ4AEAAAAG+n9/SiwuhhQOjwAAAABJRU5ErkJggg==" /><!-- --></p> -<p>The error level of the fit, but especially of metabolite Z3, can be improved if the SFORB model is chosen for this metabolite, as this model is capable of representing the tailing of the metabolite decline phase.</p> +<p><img src="data:image/png;base64,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" /><!-- --></p> +<p>The error level of the fit, but especially of metabolite Z3, can be +improved if the SFORB model is chosen for this metabolite, as this model +is capable of representing the tailing of the metabolite decline +phase.</p> <pre class="r"><code>Z.mkin.5 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE), Z1 = mkinsub("SFO", "Z2", sink = FALSE), Z2 = mkinsub("SFO", "Z3"), @@ -1784,22 +1842,30 @@ FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)</code></pre> <pre class="r"><code>m.Z.mkin.5 <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini = m.Z.mkin.4$bparms.ode[1:4], quiet = TRUE)</code></pre> -<pre><code>## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini = m.Z.mkin. -## 4$bparms.ode[1:4], : Observations with value of zero were removed from the data</code></pre> +<pre><code>## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini = +## m.Z.mkin.4$bparms.ode[1:4], : Observations with value of zero were removed from +## the data</code></pre> <pre class="r"><code>plot_sep(m.Z.mkin.5)</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> -<p>The summary view of the backtransformed parameters shows that we get no confidence intervals due to overparameterisation. As the optimized is excessively small, it seems reasonable to fix it to zero.</p> +<p><img 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" /><!-- --></p> +<p>The summary view of the backtransformed parameters shows that we get +no confidence intervals due to overparameterisation. As the optimized is +excessively small, it seems reasonable to fix it to zero.</p> <pre class="r"><code>m.Z.mkin.5a <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini = c(m.Z.mkin.5$bparms.ode[1:7], k_Z3_bound_free = 0), fixed_parms = "k_Z3_bound_free", quiet = TRUE)</code></pre> -<pre><code>## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini = c(m.Z.mkin. -## 5$bparms.ode[1:7], : Observations with value of zero were removed from the data</code></pre> +<pre><code>## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini = +## c(m.Z.mkin.5$bparms.ode[1:7], : Observations with value of zero were removed +## from the data</code></pre> <pre class="r"><code>plot_sep(m.Z.mkin.5a)</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> -<p>As expected, the residual plots for Z0 and Z3 are more random than in the case of the all SFO model for which they were shown above. In conclusion, the model is proposed as the best-fit model for the dataset from Appendix 7 of the FOCUS report.</p> -<p>A graphical representation of the confidence intervals can finally be obtained.</p> +<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAIAAAD17khjAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdd1xT19sA8JOQMEMYggzZOBhlWJGhiAsFRUEtThRaF9WCdVfrqBNsq1XrngXrAkFxAI4qoqCiIijKcAEGArITyCLjvn/k98Y0jCJkQHy+H/+4OTk55wnm5Lm54xwchmEIAAAAAMoFr+gAAAAAACB9kOABAAAAJQQJHgAAAFBCkOABAAAAJQQJHgAAAFBCkOABAAAAJQQJHgAAAFBCkOABAAAAJQQJHgAAAFBCkOABAAAAJQQJHgAAAFBCkOABAAAAJQQJHgAAAFBCkOABAAAAJQQJHgAAAFBCkOABAAAAJQQJHgAAAFBCkOABAAAAJQQJHgAAAFBCkOABAAAAJQQJHgAAAFBCkOABAAAAJQQJHgAAAFBCkOABAAAAJQQJHgAAAFBCkOABAAAAJQQJHgAAAFBCkOABAAAAJQQJHgAAAFBCkOABAAAAJQQJvnt5//79pEmTwsLCjhw5ouhYAAAA9GA4DMMUHQP4ZPXq1TNnzhw4cKCPj8+9e/cUHQ4AAICeCn7Bdy+bN292dXUtKyszMDCQaUfp6emTJ0+W5wtpNJpECY/Hq6mp+dx26uvrS0pKOhEAAAB8USDBdy8aGhoJCQkREREHDx5UdCzSRKfTfXx8JArXrVu3bNky8ZKtW7cS/x+BQBg0aFBpaamXl5ednd2ff/4prLN+/XoOhyOnuAEAoMeCBN+9JCcnP3r06NKlS8bGxh18CZfLbXW7/ZryFBUV5eHhQafTxQvT09NPnjwpUXPDhg3c/7d8+fIff/wxOjp606ZNr169io2NpdPpr1+/RggNGDBAftEDAEDPBAlekVasWNG/f//Hjx8jhBISEuzt7RMTE4uKiqZNmzZnzhyJyvv27bOxsenXr9+KFSsEAkFmZmZISMjMmTO3bNkivo0Q2r59u62trY2NzerVqyVqtoxBoll/f/+kpCThU4MHD753755EhU68TT8/v6ioKPGShoaGVatWbdy4sa2XPH36tLCwMDQ0VEdHJz8/n0KhsNlsIpG4ffv2devWdSIGAAD44mBAoRITE/38/JhMpqWlZWpqalvV7t696+LiUlVV1dTUNGPGjG3btmVkZOjo6BQWFmIYJr59/fr1AQMG0Gg0Npvt7e196tQp8WfFG5w0aVLLZv/66685c+ZgGPbu3TsbG5u0tDSJCsIXijc1btw4yxZevHghXqesrMzKykr0cObMmVeuXDl//vzs2bNbfb/Dhg179eoVhmE0Gi08PNzX1/fmzZtPnjxZu3bt5/6FAQDgy0RQ9A7Gl27y5Mlr1qxZtGiRs7Ozv79/W9Vu375No9GmTZuGEKqurm5sbBwxYoSrq6voYLVo+86dO8HBwWQyGSE0c+bMO3fu2NjYiNdsv9kzZ86sWbOGy+XGxcWFhYXduXNHooK3t7dEIykpKZ/1ls+cOUMikSZOnBgXF9dqhdTU1N69ezs4OCCEyGTy4cOHheVTp049duzY1atXDxw44OTktG3bNjU1tc/qGgAAvhyQ4BUMh8PNnz9/w4YNr169aqcaiUQKDw9fs2YNQojNZjc3N+fl5Wlra4sqiLYxDMPhcMJtFRUVPp8v/ux/Nksmkz09PdPS0uLj4y9evHjhwgWJCjk5ORKN+Pn5tQw+JSXF2dm51U4TEhKysrKsra0ZDAaTyQwICEhOThavcPr06W+++UbiVTdv3nR3d+fz+Zs2bbp06dLx48cPHDiwfPnytv5iAADwhYNz8IqXk5Ojra1tbm7eTp2xY8fGxsZWV1dzOJxvvvkmMzOzrZqjRo1KTExsampqbm4+c+bMqFGjPrfZGTNmREdH6+npWVtbd6TfGzdulLXQVnZHCF26dIlKpRYXF+/bt2/y5MnJyckYhhUVFfF4PIQQh8O5fv26xMEMDMMOHDgQGRnJYDD09PTMzc2dnJxqa2vbeWsAAPCFgwSvYJmZmenp6UOGDElISGinmqur69KlS728vGxtbW1tbceNG9dWTX9//1mzZg0cONDe3t7d3T00NPRzm504ceLjx4/DwsI+q9+uaG5utrOzKy8vRwjdvHnTxcVFT09PvEJ8fHxgYKC6urqFhYW3t7ejo+O+ffuWLFkii2AAAEA5wEx2iiQQCNzd3SMiIqysrFauXPno0SMCAU6aAAAAkAJIJ4oUExODw+HCwsJwOJy9vb21tfW9e/esra0VHRcAAIAeD37BAwAAAEoIzsEDAAAASggSPAAAAKCEIMEDAAAASggSPAAAAKCEIMEDAAAASghukwPgS7Ro0aJWyw8dOiTnSAAAMtJKgs/Nzc3NzQ0JCamsrGx//lSZyszMvHr1astyNputpqaGw+EwDONwOOrq6vKPDQD5sLCwWLx4sSxanjBhgiya7aCSkhKJmYYfPXpUVFSkqampqJAAUDipj3fJ++BjYmKio6NZLFZ+fv5XX321fv36+fPnS7G/thQXF9fV1YmX7Ny5s6amxtfXV7ywtLT08uXLkZGROBxOIBDs2bNn2rRpZmZmcogQADmj0Wh///03hUKRT3cCgWD9+vVRUVFy6Ovbb799+fKleElhYaG+vv4PP/wgh94B6IZkMd4lE7yLi8vdu3dDQ0OvXr3a0NDg5ub29u1bKfbXllYH/FdfffXo0SPxwoSEhPj4+Pj4+KysLA8Pj0mTJs2dOzcwMFAOEYKei8ViTZs2jc1mKzqQT3R0dC5cuCBa969VZWVlXl5eMk3wv/322549e+rr642MjKqqqkaOHCmxsp/ceHh4GBoaXrt2reVT1dXVO3bsyMvLs7e3X7NmjYmJifzDA0DWZDHeJQ/RMxgM0UFvTU1NIpEoxc7aERMTI1Hi4eFhYGAgUeju7h4ZGVlUVDR8+PDHjx8/evQIThmC/9TQ0PDgwYO2lp9XCH9/fz6fr/ClB/bv319QUPDzzz8vXryYTqf/9ddfio2npebm5rFjx44cOXLVqlVpaWkjR4589uwZHMkHoCMkv19CQkICAwMrKipOnjwZExMTEhKikLDaYmFhsXPnTm9vbx6PN2rUqIMHD8LuPOgINTU1idM9itX+b3e5UVNTI5PJAwcOzMrKmjt37tKlSxUdkaSnT5+qqan98ccfCKExY8Y8efLk3r17EqsJAwBaJZngN2/enJycnJGRUV5eHhUV5e3trZCw2hESEtK/f//AwMDt27dPmzZN0eEA0IP5+PjMnTt37dq1U6dO/fjxo46OjqIjksRkMslksughmUxmMpkKjAeAHkQywdfW1np6enp6eooe9urVS+5RtWfTpk2nTp1CCG3dujU9PT02NlbREQHQUx0/fvz169f9+/f/448/srOz5XaIPi8v7+PHj+IldDpdV1e3ZU13d/fCwsIzZ84EBATcuXPn4cOHR44ckU+QAPR0kgledOyLRqO9efPGxcUlNzdX7lG1iUKhHDlypKCgIDw8PCgoKDo6+v79+8OGDVN0XAD0SHV1dQYGBrW1ta6urq6urnLr9+jRo4WFheIlVCq11eMHZDL58uXLy5Yti4iIcHR0vHjxYstLcwAArZJM8E+ePBFt5+XlhYaGyieOc+fOffjwQbyESqVqaGhIVHvz5o2Dg4Ourq6BgUFdXZ2np2dRUREkeAA6R1E79Pv27ZMoafWiWqGBAwfevXtX5jEBoHTau4jXycmJy+XKJw46nV5fXy9ewufzBQKBRDUHB4eXL1/GxMRkZ2e/evWKQqEsXLhQPhECoHwUtUMPAJADyQS/bNky0XZhYeGAAQM63bRAIHj+/HllZSWPx+vTp4+rqyse3+bU9+Hh4RIlaWlp4hfXCBkbGw8cOHDBggWGhoY1NTUaGhpWVladjhAAINKVHXo+n6+ioiLczsnJyc/Pd3Nz68q3BwCg6yQTvPhl8wEBASNHjuxcu2lpaYsWLTIxMenTpw9CiEqlUiiUEydO+Pj4dDpWhFBzc/ODBw+ePXt25syZFy9eWFlZxcbGrly5sittAtB1p06d+uWXX4TbXC63vLy8tLT02rVr+/fvJxAIe/fu7fRQkilp7dAbGxtXV1cjhHbv3r1z585Ro0atW7duy5YtcEgAAAX6lOCzsrIQQhLTvj59+tTDw6MT7S5ZsiQ5OdnW1lZUQqFQgoODhb10Go1G09DQcHJyGjt2bFZWlo2NjcS1uAB0zrt37+7fv9+rV69x48Z1Yv6Z0NBQUTJbvnz5hw8fcDjcvn37srOzqVTqhAkT8vPz2zmCpSjS2qEX2bVr19OnT01MTKqrq4cOHdpWgn/48GFZWZl4SV1dXTe8SQ+AHu3TF9mePXsQQlQq9f79+87OziQSKSsra/LkyfHx8Z1ol8/nS0xBY2RkJDEtbicYGhoaGhr+/fffrq6ulZWVf//9d3R0dBfbBOD8+fPLli3z9/cvKSnZvHnzvXv3Oj1X2rVr15KSkp49exYfHx8UFKSpqdm3b19jY+Pnz58PHDhQumF3hXR36EUMDAyEA9/AwEB00L6lq1evSsyBLbyev2VNCoWyZcuWgoICR0fHjRs3Co8IdoRAIIiNjb17966JiUlkZGTHXwiA0viU4M+dO4cQGjNmTEFBgfBIXXFxcacvYYuIiBg0aFBQUJCZmRkOh6NSqVeuXImIiOh6xOfPn585c2ZlZWVtbe2WLVvGjx/f9TbBl4NGo0lcvIlh2JIlS65evdq/f3+EUHh4+N69e7///nuJF+ro6PznT/Dy8vIFCxZcunRJV1e3oqJClD7NzMwqKyul9yakQLo79EQi0dHR0cLCgk6nx8bGhoaGzp8/v51pslouaePh4dFyyg0GgzF69Og5c+Z8++23N2/eHDNmzLNnzzq4gGRkZOTLly/nzZtXWFjo4eHx9OlTY2Pjz31fCnH8+PHo6OiampqxY8fu37/fyMhIpt3l5+ffvXvX0NAwKChIVVVVpn0BOZM8FFlaWir8mkMIWVlZUanUzrW7ePHiiRMnpqamCluwsLBISUlpZ/HZyMhIiftiCwsLW53O86uvvsrLy3v37p29vf3PP//cufDAl+n9+/eDBw+WOJKEYRiNRhs3bpzwIYfDuXLlyu+//y7x2ilTphw/frydxvl8/qxZs5YvXy6cJ0ogEIh/gHk8nnTeg5RId4eeSqWyWKzS0tKSkhJjY2MMw2xsbJYvX97FIDMzMy0tLYcNG3b06FEul4vH4x89ejRixIj/fCGTyTx9+jSVStXS0kII0Wi0s2fPdj0eObh169bvv/9+6dIlKyur7du3h4WFXb9+XXbdxcTErF+/Pigo6N27d9u3b8/MzBT+xYBykEzwXl5ewcHBISEhGIadO3du8ODBnW6aTCYvWLAAh8NlZ2cXFBQ0NTW1U3nBggVVVVXiJREREa2ek2Oz2fPmzbty5QqPxxs/fvz58+dbnQALgJZsbGwkliEXsrKyOn36tLe3N5/PDwwMnDx5cidWSd68ebOGhobokk9TU1PR1A5UKtXU1LQrkcuItHboEUIaGhp2dnZ2dnbCh+vWret6eM3NzY2NjaGhoRs3blRTU7ty5cq1a9eECf727dt79uyh0+njxo1bvny5qqrqixcvLly4QCQSQ0JCNDU1tbS0RLmqT58+rf6//6eKigpNTU15Xhxw7dq1iIgIZ2dnhFBUVJSenh6LxWo5I4hUYBi2cuXKR48e9e3bFyEUGhp6/PjxH3/8URZ9AYWQPOR49OhRHx+fs2fPxsXFDR8+vNOzQh49etTDw4PH40VFRU2dOvXWrVv+/v7Hjh1rq76zs7Pvv+no6LS6ll1UVBSfz6+urra1tTUwMFi9enXnIgRA5MSJE8HBwb6+vnZ2dgQC4dtvv/3cFtLS0k6ePHnq1CnRr/Zx48ZduXKlubm5rKyMQqF0qxPwIsId+osXLyYmJk6dOrUrO/SyMHTo0OfPn0+aNGn8+PFcLldVVfXIkSMmJia9evUSLse3Zs2a9PT0VatWpaSk+Pv7EwgEJpM5ZMgQKpVqYGBw+PBhDMOKi4tjYmL8/PxEzTY2NhYWFnI4nHa6LikpGTx48Ndff21pafndd9/J7QCMlpZWQ0ODcJvBYOBwONkt6VlXV4cQEmZ3hJC7u/u7d+9k1JccVFdXHz9+/OjRo13ZT5Wpqqqq+Pj469evy22CmU/rwUdGRm7dunXDhg0SNVrOOdURlpaWeXl5ZDLZ3Nz8+fPn+vr61dXVQ4YMefPmTQdbaGt96GHDhu3YsWPo0KFDhw5dsmTJ+vXrO94m+DJVVFQMGjSo/WHf0NDw9OnT3r17C388fa7x48c/ePDA0NBQVHL79u2UlJSTJ08ihH7//ffhw4eL1ycSiSwWq/3L9eWwHjyHwzl8+PD9+/fxePywYcMWLlyopqYmu+5Ebt68WVJSIl6ydetWEokkum0vICBAeFmck5NTY2MjjUYzNDQUzm6poqJCIpGampr69u3LZDLnzJmzfv16BweH7du3jx079u3bt1u3bs3Pzw8MDIyJiRH+BN+yZcvYsWPv3LmDEPrnn3+Sk5NNTEyampqOHz9uZ2cnLJfo18fHp3fv3mPHjuVyuYcPH542bZrwu/Ht27et1pdW+Y0bN6ZNmzZ+/PhevXrdvn3by8tL+BGSUb/btm07derUiBEjCgsLp0yZ4uHh4eXlJYv3Jevy69evT58+3cHBQUVF5cWLF3FxccKTbt0nzrdv306fPv3rr79++/Ytg8FYtWqVpqameP3ExMSdO3cKbzeVlk/fL76+vurq6tJah9HQ0LC5uRkhpKurK7w0qZ1Laj+35efPn1+4cKGoqGjXrl1wfB5Iha6ublfWk01JSWlZ+P3337e8WK+bEN+hF173/vr165UrV3Zuh/5zZWdnSyT4pqYmLpebnZ0tfOjs7Cz84vPw8Lh48aK+vn5jY6OKioqRkdGsWbOKi4vj4uLMzc0vXryYk5PD4/GoVKrwXENxcXFDQ0NZWVlZWZmvr++sWbOEu1a3bt3Kzs6uqam5devWlClTfvjhByKROH78+GPHjok6Fe/3yZMns2bNEj5lYmKSlpYm/FsVFxe3Wl9a5Xg8XngtIYfDMTMzmzdvnrCCjPpds2bNrFmzbG1tX79+ra2tTSQShc9K/X3JunzHjh0uLi729vYIITU1tS1btggTfPeJ88cffxQe4UtISHjw4MGpU6cGDx4sXj8vL0/6v+yxfysoKDh+/DiGYevWrRs6dOj9+/exTrl+/Xq/fv1WrFgxZcoUFxeXVatWOTg47N27t+MtuLu7BwQEtCy/e/cugUDw9va2s7NTV1c3NTVtamrqXJDgC0GlUk1MTBQdxb8QCAQul9t+HQqFYmZmJqMAkpKSWCzWtRZk1N1/amu8R0REmJmZaWpqqqioEAiERYsWbdu2LSUlhUAgJCYmkkikkJCQOXPmhIaGrlq1is/nNzc3z549e926da32cuDAgR9++EH00MvL6+HDh63WtLW1zc3NFW5HRUVFRkZ2+S12U42Njffv3y8qKlJ0IF3i5ub25MkT4bZwgUTFxiNBIBCoq6uz2Wzhw8TExClTpkjUkcV4lzwH/+OPP5JIpMePH2dkZOzYsWP79u2d22/w8/N7/Pixk5PTwIEDAwICTE1NExMTlyxZ0uUdEkQgEBwcHIYMGUImk+fOnevo6Hjv3r2uNwvAFyUoKEhdXd3W1raysjIgIODhw4fR0dHdbaoZDocTGxtbUFDAYDDevXunpqb2+vXrPXv27N6929raesGCBQKBQFdX98CBA7t27Xry5ImxsbGRkRGDwWjrEr8+ffrk5+cLt1ksVnFxscRMACIbN24MCgqKiopaunTpn3/+qcSXnpFIJG9vb9G1lj3UoEGDLly4INyOj493c3NTbDwScDico6PjzZs3hQ9v3Ljh5OQkh34lTwFWVlZOnz59zZo1s2fP9vb2fv/+faeb1tXVDQsL62DlESNGpKenSxR+/fXXLWuy2ezevXv/+uuvu3btKi8vJ5FIbDa700EC8CX78ccf586dK75Dn5qaquigPmlqalJTUyORSAghS0vL+fPnp6amGhoaFhUVkUikb7/99pdffhGuWKGtrZ2WllZVVUUgEPT19dtqcPz48b/99ltgYODgwYOvXr06adKkthJ8aGho//79L1++bGxsnJ2d3T1vggAi27Zt8/f3d3R0JBAIGIZ1q4+x0P79+ydPnjxw4MCamhoMw8TP0MuOZIIfMGDAihUr4uPjnzx5snv37pbLvchIy+UghRfZtazp7u7++vXr2NhYdXX1p0+fvnv3rp2L8wEA7ZDiDr0s9OrVy8rKau/eveHh4QcPHjx9+vTy5cvbmf2id+/e7TdIJBLT0tLOnj37/v37DRs2TJw4sZ3Knp6ewikNQPdnYGDw+PHjFy9eCAQCZ2fnTsw2LWuenp75+fkPHz4kkUhDhw6V1kVp7ZP8Kxw4cODvv/9OSkoyNjaura0VzobRrWhra1+9enXZsmXZ2dkqKirXr19vOQEWAKAjFLVDf/Xq1YqKCvGSqqqqVqdYEc5cuWzZMgKBMGbMmIsXL1IolEOHDnW6a1VV1U7cBgm6Pzwe7+rqqugo2qOnpyfnqVclz8EbGhqOGjVKeDlfeHi46BbJbsXZ2fn27du3bt2ytrbubnfuAtCDHDhwoE+fPvLfoX/z5k32vzGZzFYvIRbOWq+np2diYjJgwIB79+5dvnxZNIMQAKAdkr/gY2JioqOjWSxWcHDwsGHD1q9f34kpveTD0NBQurcMAvClEe7Q5+bmOjs7h4eHtzOZtHS1nDU2Nze35SV+FRUV0dHRP/30U2lp6c6dOwcOHDhjxgxbW1sKhWJhYSGfUAHouSQT/O7dux89ehQaGkoikXJzc93c3CDBAyXAZDKPHj2q6Cg+kVjwRlG6+Q59QUHBV199NXbs2ClTpvzyyy8+Pj7JycmvX7+WzxXIAPR0kgmewWCIFmvS1NSU3SyJEp4/fy6RrWk0Wvs37WhpaeFwOCaT2emVPcEXQl9ff86cOeLzTijc0qVL5XOVTfu6+Q59//79X716ZWVlFRkZ6eDgwGAwtLS0Tp48KbdrBQDo0SQTfEhISGBgYEVFxcmTJ2NiYkJCQuQTx8mTJ0X3pwpVVlbq6em1/yoDA4Pq6mpLS0tZhgZ6PDU1NflM0NbjSGuHns/ni/ZXcnJy8vPz3dzchIvUdYWZmdkPP/zg6uo6fPhwAwOD4cOHnzlzRkYrrwCgfCQT/ObNm5OTkzMyMsrLy6OiotpZ0Vm69u7dK1HS6vrQ4ng8no6OTlVVVccTfHNz8+vXr42NjQ0MDDoZKABKRFo79MbGxsIjcLt37965c+eoUaPWrVu3ZcuW0NDQVuufOXOmrKxMvEQ4z3zLmhs2bJg0aVJOTs4PP/zg4eHRufAA+DK1crNgQEBAQECAcLuxsVFbW1u+IXXI8ePHf/rpJwaD4evrO3v27AkTJujr6xcUFDCZTF9fX+GsTI2Njffu3SMQCMOHD1dXV8/IyJg1a5aOjg6VSg0LC/vjjz860S+TySQSiXI7cyFUVFS0b9++urq6MWPGhIWFCef2l53S0tKMjIzevXuPHj1a1n0BxZL6Dv2uXbuePn1qYmJSXV09dOjQthI8i8Wqr68XLxEIBNj/L3wlwcnJCU66A9AZoklrs7Ozx44d6+bmtnHjxurq6vnz548dO9bIyEi6U+N2XFtzU2MYlpeXZ25uXlRU1Lt3b1NTUwKBIJynWkNDY8qUKcbGxomJiS9fvuzTp4+/v/+IESP69u1bVlZmYWFx69YtDMPodLqbm1tSUpKwtcePH/v5+fXr12/GjBklJSVtxUOlUn19fYXrTC9dupTP58viXbf05s0bIyOjHTt2nDlzZujQoStXrpRpd/Hx8UZGRiEhIZ6enkOGDGGxWDLtTqY4HE56evqdO3dEU0B3QxUVFTQardWnZDoXfavodHrnXmhgYCDccHFxEW4IBAI7O7uOt9DOeAfgSyDbuei/++47Ly+v3bt3U6lU4RI3a9euffToUad3HQQCQU5OTmpq6tWrV589eybFy4YzMzMDAgKePHnSt29fDMMQQiNHjsTj8ceOHcvJyTlz5syKFSuWLl26Zs2as2fPXrx4MSQk5KeffhIIBMLlwrS1tYODgx8+fIgQolKpQUFBoaGhycnJTk5OEyZMaGvh5++//97Ly6uxsbGsrOz58+dymz4vJiYmPDz8p59+mjVr1pUrV44ePcrn82XUF4ZhERERt2/fPn369MOHD/v06XPixAkZ9SVrFRUVLi4uP/3008aNGx0cHIqLixUdkSThiuMDBw60sLCYN2+e7P5bW/Xs2TM/P7/Bgwf/8ssvNTU1CxYsEO7mdq41IpHo6Og4btw4Op0eGxuLYdj8+fPldoIPANCqT+vBW1hYCKePqK2ttbe3r6qq6kq7aWlpixYtMjExEa6FR6VSKRTKiRMnfHx8OthCW+vBI4SSkpIOHjw4bNgwHo9XXV0dExOjoqLCZrO1tLTodDqZTKbRaHg8XltbW3iEmU6nt//tSSQShfNd0+l0LS2tVi9vbmhoMDU1Fa6WzWAwmpqajIyMWm1NV1cXh8P95xskkUgdOdRfVFSkra3t6OiIEMIwLCkpKTAwsP0LsHV0dD7r0DqZTBY2yGKxjh07JloT6NmzZ7W1tWPGjGn5Ejwe37mFSTQ0NERXdX0WAoHwWWeLDh06ZGhoGBwcjBBKTk4uKir6+eefpXJ9lujT0kWRkZHu7u5hYWG2trbTp08fP358ZGSkeAWZrgfv4uIyefJkX1/f2NjY5OTkhQsXjhgxwsrKysrKqnMNslis0tLSkpISY2NjZ2fn6Ojo5cuXd/wP3s54B+BLIIvx/ukcvChn9OrVq9U5Iz/LkiVLkpOTbW1tRSUUCiU4ODgrK6vV+ocOHSotLRUvoVAowmza0rhx47Zt2/bPP/8UFxcTiURTU9NJkybt37//8ePHnp6eR44c+fXXX3v37j116klfzkkAACAASURBVNTvvvsOIbR169aysjIbG5sTJ07MmDGjoKAgPz8/KyuLRCKdOnXqn3/+OXHiRFNTE0LIy8vr5MmTwkWFJbi6usbExAgv6Dt+/DiFQtm8eXOr4TU0NGBtnE0UJ1wA+z+rPX78+MiRIz4+Pr169UpKSrKzs5sxY0b7L+lgACKiHSA9PT0ikchgMIRHisrKyuzt7Vu9l0EgEEicQ+0gKpXaucWBeDxeY2Njx+vn5uaKFphqamp6+fLlsWPHWCxWJ7qWwOVyhZ+WLsrJyWGz2RkZGQcOHJg3b15cXJxEgpep+vr6TZs2IYTs7e0vX74s3O4KDQ0NOzs7Ozs74cO21nMDAMiNrGbk5/P5JiYm4iXC0/lt1dfX16fT6eIlZmZmvXv3buve5blz5z569KikpKSiooLP5+/du1dLS8vBwUFfX3/RokVLly4lEokrVqz4888/m5uba2tr161bp6enN3369Pz8fD09vRUrViQlJSGEmpqarl27tnDhwr59+z58+LC5ufnt27etLrkxdOjQWbNm+fn50en0O3furF279sGDB53503yO+vp6CwsLHx+fdevWsVgsBweHBQsWcDic9l/1ub9TxScSmD179qlTp2xsbKqrqw0MDNo5WiCtWc8wDKPRaLq6ulJpTaS8vNzY2NjPzw8hlJ6e3q9fv2nTpomebWho0NHR6ciBFmnh8/mampo2NjaikqCgoNWrVwuPiqelpREIBIlPexePorVPujv0n6vVHXoul9vWeM/JyencgZ9OYzKZBAJBVVVVnp3W19f/573B0tXOMUsZkdF4b193GO//SRbj/dMhejweLxrnwgklhNuf9bNJ5ODBg/v27QsKCjIzM8PhcFQq9cqVKxEREeHh4R1sITY2tp17l7Ozs4VpSXgpgfASXDwej8PhREenheVI7LusVQKBgMvlCl9OJBLb+hAI6yCEcDgcgUCQw2cFwzA2my3nu345HI6KigoOhxP/S8qUQCBobm6W+te38H9f+F/P5/OFnw3Rs2w2W1VVVZ73CAjvFBc/OMRgMBBCWlpaGIbR6fRWb0Pv37//2bNnZRGPtbW16LoE8e3OOXz4cMvlnhFCbU1uHxcXV1JSIl5y9+7d9+/ft3UWRjTe5YbL5QpHutx6VNR4F16kLLceZTTe29cdxntHSH28f0rwbf0ubOs4+X+iUCipqalUKhUhZGJiMn78eGn95uPz+Wpqam1dDScjy5Yts7S0XLp0qdx6/PDhw7BhwyR+6Mja1KlTp0+fLjx1LR8PHjxYtWpVZmam3HpECAmvJ5XnYqDx8fGJiYlxcXFy67F90t2hZzAYERERdDp98eLF4uWjR4/uYpwIxrsswXiXkW4y3j/tn3Y6kbfF3Nx84cKF0m0TACAVUrkcQURLS2vZsmXnz5+XSkYHAEiF/A5AAQC6D6nv0Ds7Ozs7O0u3TQBAV8A8ZQAAAIASggQPAJCyESNGKDoEAEDPTPA4HE7+N/aoqqrK+Z4Z+feokE7hbSof6c7KB+NdmTqFtylPuM+aEaX7+M/V4qVOIcvMyP9tNjY2amlpyXmNGfm/Tfn3KBAIGAxG91y6SeqOHTu2YMECKTYI411GYLzLSDcZ7z01wQMAAACgHT3yED0AAAAA2gcJHgAAAFBCkOABAAAAJQQJHgAAAFBCkOABAAAAJQQJHgAAAFBCPS/BHzx40MHBwdnZOS0tTdZ9bd261cHBwdzcPDo6Wp69//777ykpKXLr8caNG4MHD7a0tDx69Kh8Ol2zZo21tbWtre3p06fl0CODwTh8+LDoYcu+pN67RI+K+iApARjvUgfj/Qsa71iP8uHDBzs7OwaD8ebNmwEDBvD5fNn19c8//3z99dcsFqu6utrCwiI7O1s+vWdkZKiqqsbExGByeb/19fX9+vWrrq6m0WgDBgyg0Wiy7jQ9Pd3V1ZXNZldUVOjo6DQ1Ncm0xxcvXsydO3fGjBnChy37knrvEj0q6oOkBGC8S707GO9f1HjvYb/gU1NTg4KCNDU1+/bta2xs/Pz5c9n1VVtbGx4erq6ubmBg4O3tLVzeXta90+n0tWvXzpgxQ/hQDj1evnw5MDDQwMCATCbn5eVpa2vLulMVFRVVVVUikaiurk4gEJCM3+a5c+caGhpED1v2JfXeJXpUyAdJOcB4l3qPMN6/qPHewxJ8RUWFmZmZcNvMzKyyslJ2fU2bNk24nn1eXt6DBw9GjBghh94jIyN/+eUXfX194UM59EihUEpKSpycnCwsLHbu3InD4WTd6dChQ/v3729qampubr5p0yYtLS2Z9hgVFbV48WLRw5Z9Sb13iR4V8kFSDjDepd4jjPcvarz3sAQvEAhwOJzoIY/Hk2l3GIbt3r37m2++uXjxoo6Ojqx7j4uL09fXHz16tKhEDu+XxWK9efPm3r17ubm5x44dy87OlnWnd+7cefPmzd27d1NSUnbv3k2lUuX539qyLzn0LucPktKA8S7dHhGM9y9svBPk1pNUmJqafvjwQbhNpVJNTU1l1xefz586daquru7Tp0/JZLIceo+JiXn37t0///xTUVFx4cIFDocjh/draGjo5+enp6eHEBo2bFhhYaGsO01JSZk9e7adnZ2dnZ2np+e9e/fk+d/asi9Z9y7/D5LSgPEu3R4RjPcvbbzL7Wy/VJSWln711VccDodCofTt21emVyucPXt2+vTpCul96dKlwotu5NBjQUGBo6NjbW1tVVWVhYVFfn6+rDs9evTo+PHjRReh5OTkyLrHf/75R3QJTMu+ZNG7eI8K/CD1dDDepd4XjPcvarz3sF/wFhYWP/zwg7e3N0Lo+PHjMl3l8P79+6mpqSYmJsKHx44dmzBhgtx6F5LD+7Wzs1u4cKGHhweGYT///LO9vT1CSKadzp07Nycnx8HBASG0atUqV1dXWfcoruWfVNZ/5O7wQeqhYLxLvQsY71/UeIflYgEAAAAlBD8dAAAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgu5f3799PmjQpLCzsyJEjio4FAABAD4bDMEzRMYBPVq9ePXPmzIEDB/r4+Ny7d0/R4QAAAOip4Bd897J582ZXV9eysjIDAwOZdpSenj558mR5vpBGo0mU8Hi8mpqaViu381R9fX1JSUknAgAAgC8KJPjuRUNDIyEhISIi4uDBg4qORZrodLqPj49E4bp165YtW9ZqfdFTpaWlXl5ednZ2f/75p/Cp9evXczgcmUYLAABKABJ895KcnPzo0aNLly4ZGxt38CVcLrfV7fZrylNUVJSHhwedThcvTE9PP3nyZKv1xZ+Kjo7etGnTq1evYmNj6XT669evEUIDBgyQdcwAANDTQYJXpBUrVvTv3//x48cIoYSEBHt7+8TExKKiomnTps2ZM0ei8r59+2xsbPr167dixQqBQJCZmRkSEjJz5swtW7aIbyOEtm/fbmtra2Njs3r1aomaLWOQaNbf3z8pKUn41ODBg+/duydRoRNv08/PLyoqSrykoaFh1apVGzdubFlZ4ikdHZ38/HwKhcJms4lE4vbt29etW9eJGAAA4IuDAYVKTEz08/NjMpmWlpapqaltVbt7966Li0tVVVVTU9OMGTO2bduWkZGho6NTWFiIYZj49vXr1wcMGECj0dhstre396lTp8SfFW9w0qRJLZv966+/5syZg2HYu3fvbGxs0tLSJCoIXyje1Lhx4yxbePHihXidsrIyKysr0cOZM2deuXLl/Pnzs2fPlninEk/RaLTw8HBfX9+bN28+efJk7dq1nfs7AwDAl4ag6B2ML93kyZPXrFmzaNEiZ2dnf3//tqrdvn2bRqNNmzYNIVRdXd3Y2DhixAhXV1fRwWrR9p07d4KDg8lkMkJo5syZd+7csbGxEa/ZfrNnzpxZs2YNl8uNi4sLCwu7c+eORAVvb2+JRlJSUj7rLZ85c4ZEIk2cODEuLu4/nyKTyYcPHxZuT5069dixY1evXj1w4ICTk9O2bdvU1NQ+q2sAAPhyQIJXMBwON3/+/A0bNrx69aqdaiQSKTw8fM2aNQghNpvd3Nycl5enra0tqiDaxjAMh8MJt1VUVPh8vviz/9ksmUz29PRMS0uLj4+/ePHihQsXJCrk5ORINOLn59cy+JSUFGdn51Y7TUhIyMrKsra2ZjAYTCYzICAgOTn5P5+6efOmu7s7n8/ftGnTpUuXjh8/fuDAgeXLl7fzRwMAgC8ZnINXvJycHG1tbXNz83bqjB07NjY2trq6msPhfPPNN5mZmW3VHDVqVGJiYlNTU3Nz85kzZ0aNGvW5zc6YMSM6OlpPT8/a2roj/d64caOshbayO0Lo0qVLVCq1uLh43759kydPTk5OxjCsqKiIx+O1fEr4EgzDDhw4EBkZyWAw9PT0zM3NnZycamtr23lrAADwhYMEr2CZmZnp6elDhgxJSEhop5qrq+vSpUu9vLxsbW1tbW3HjRvXVk1/f/9Zs2YNHDjQ3t7e3d09NDT0c5udOHHi48ePw8LCPqvfrmhubrazsysvL2+rQnx8fGBgoLq6uoWFhbe3t6Oj4759+5YsWSKLYAAAQDnATHaKJBAI3N3dIyIirKysVq5c+ejRIwIBTpoAAACQAkgnihQTE4PD4cLCwnA4nL29vbW19b1796ytrRUdFwAAgB4PfsEDAAAASgjOwQMAAABKCBI8AAAAoIQgwQMAAABKCBI8AAAAoIQgwQMAAABKCBI8AAAAoIS6733w6enpZ8+ebetZPEJEDDXjkBqGODgEt/oBpdS/f/8VK1YoOgp5aH+8A/AlkPp47773wa9evfr9+/djx45t9Vn7AiqGQ4V2pm5P3n000aOY6cs5PABkrb6+fv/+/RQKRdGByEP74x0ApSeL8d59f8EjhDw8PBYuXNiynFtWWZm1t8+e9T7aWtxxFYab/vRbFYnX1pJ/hKCnKC8v53A4io7iE01NTWNj4/brlJWV7d+/Xz7xdAdtjXeEYfWnL+sE++M11OUeFAByIovx3q0TfFvq/krUnTpOmNGJ5iaaHi4Nidf1v/1G0XGBbqq2ttbKysrCwkLRgXxSVlbGZDJVVFQUHUgP0Hj7Ae3aHYTH64UEKjoWAHqSnpfgm4vLWM8LOG9K6s9d+18Rn4/x+XozJ+LUVBUaGuimmpubDQ0N3717p+hAPiESid327Fi3ImCxG+JTjFYvrNl/mjTKi2hiqOiIAOgxel6CV7U2s4j9DSGEcbk4IvF/pXg8ZHcAOm7RokWtlh86dEgOvZ85c6asrEy85Pbt2y4uLi1rNsSnaA76SmPQV+SJo+pPJ/VetUAO4QGgHFpJ8Lm5ubm5uSEhIZWVlebm5vKJIyws7NWrV+Ilb9++dXNzW7VqVcvKeC1NbkV1xaodRhsjVW0tcCpwsx8An2fChAkK7J3NZtfX14uXVFRUkEgkiWrcymrG3cemu39GCJEDR1OXbWM9L9BwsZdfoAD0ZJIJPiYmJjo6msViBQcHDxs2bP369fPnz5dDHFu2bKmpqREv+fbbb1VV2/xRXh97kWhhWvX7Md1v/LT9fWQfIABKJSAgQKJEIBCsX7++ZbkszJs3T6IkLS1NW1tborDur0St4e4Yp5n3sQYhpO03rD72kvrvAwT0RtaLItJwdzmECkDPJZngd+/e/ejRo9DQUBKJlJub6+bmJp8Eb2lpaWlpKV6iqamJx7f+05ydV9RcVtHnj3WUBeuY2S8hwQPQOb/99tuePXvq6+uNjIyqqqpGjhyp6Ig+EbDYvIpqblkl88mLT6U4xPtY3XAhlfHgmaq1maqFqeICBKC7k0zwDAZDXf1/96JoamoSRSe5uw2ML6j7K1H/229wqkSisQH71RuM0wwn4AHohP379xcUFPz888+LFy+m0+l//fWXoiP6BK+h3ufPDcJtAYOJ19IUbnNeF3MK3unNCqw7mWC8aYniAgSgu5P8iRwSEhIYGFhcXHzy5ElfX9+QkBCFhNWOxuv38Dramm5OCCGEw+EQol29o+igAOiR1NTUyGTywIEDs7KyvLy8nj9/ruiIWiFgc8qXRTXeykQIIQyrO5mgNztIZ+IoQSODmdUdAwagm5D8Bb958+bk5OSMjIzy8vKoqChvb2+FhNUWAYvdcCFFzdai9sg5hBCX+lHA5dEu3tQeM1RFR/IEHgCfRcBk4TU1FB2FXPn4+MydO3ft2rVTp079+PGjjo6OoiNqBe3iTaKxQUNcstaQr5lZuQiP1xo6COFw+t9+U3P4rMbXDrjud6ARgO5A8hd8bW2tp6fnypUrFy9ebG9vX1tbq5Cw2oIjEPTnTNb0HMirrmsupSKEcGqq5KDReHU1RYcGejaMy6Wu+rXx+r3OvfzUqVPW/8/MzAyHw3348AEhxGAwDh8+LNVIpen48eNr1qzp37//H3/8oaKi0q0O0QvxqmqbbmUaLv1Oc7Bz/bmr9eeT9ecGIxwOIaTu1F/Vqg/9apqiYwSgm5L8Be/v7y/coNFob968cXFxyc3NlUMcEyZMePDggXhJY2Njy/ticUQCabQXl/qx4dxVHJGANXPVnQaoWpjCOXjQRfQrd1R0tBsupGp5u+FJmp/78tDQ0NDQUOH28uXLP3z4YGFhkZeXt2fPHiaT+f3330s7Xumoq6szMDCora11dXV1dXXtdDt8Pl80K19OTk5+fr6bm9uAAQOkEGHsJfLEkSr6OnohgWWLfxEwWRVrfhevwC3/qDMFZrAHoBWSCf7Jkyei7by8PNF3lqzFx8dLTBXu6+trZGTUauW6mIs6k8fitTVrDpxWtTLjln+US4xAafHrGujX0kx+XUW7crshPll/7tRON3Xt2rWkpKRnz54hhM6dO9fQ0NDO3Z4KJ60demNj4+rqaoTQ7t27d+7cOWrUqHXr1m3ZsqWLXyDsl6+biymGS8MQQniSpra/d/PbD0YbI7vSJgBfjvZmsnNycuJyufKJQ1NTU1PzXz+bCAQCDodrWZP1vJBHrdJetUDAYOLwKgJaI8bjySdI0NMJWOzGW5lIIJAoZ2Zmq1qZMh48UyGT6FdvIzxeRZcsUUfV2lzDxa799svLyxcsWHDp0iVdXV2EUFRU1O3bt48fPy7FtyBdUt+h37Vr19OnT01MTKqrq4cOHdpWg/Pnz5fYkygsLHR0dJSoVnfqEp6kVXcyASHEq6plvyjCMMR6UaThLIVjAwAoPckEv2zZMtF2YWGhVA6ySRHGF9TFJOrPDcYRCQIWR0WXzHiaR+ilq+i4QM+A8XiCRgb69yTw/LoGbmU1afRQQRMTIaTa15qZ9Vxr6CCJ1woam9pvnM/nz5o1a/ny5Z6entINWz6kskNvYGBgYmIi3GhnKZ21a9c2NDSIl4SFhenp6UlU0w0ex6c1IoR4lVXsN6WqNuYCLlfV/D9W4QMACEkmePHL5gMCArrVxBcIocYb9wQNjc0fqM0fqLzaBgzDcDhcc3G5gMWGpSTBf1LRJrVckaxi/R8EIwOMw8E4CCFEMNRjvyxSd+ynMdDhsxrfvHmzhobGypUrpRWtHEhrh55IJDo6OlpYWNDp9NjY2NDQ0Pnz57dzD46tra1EiZaWFoEg+XWk6e6MEMJ4/LIF6xCX23tDxMeNezjvKZqDuuPV/gB0N59GVFZWFkLIzMxM/OmnT596eHjIO6i2Ec1MSKO9hL+0BPQmpILHuFyERw3nrunPDVZ0dKBH0gn0Ff5MFFHrZ0U0+7yfiWlpaSdPnnz27Fmr55W6LWnt0FOpVBaLVVpaWlJSYmxsjGGYjY3N8uXLpRJkQ+INAYut4e5Ev3RT79tvao+e03C2wxG7sFAWhnHelKj1t5ZKeAB0W58GyZ49exBCVCr1/v37zs7OJBIpKytr8uTJ8fHxigtPkobzANHpN+aj3OaySoKJIZ/Bbrr9sPlDhfEvEahHfb2C7kD4M7GLfv/996ampqFDh4pKbt++3a1WoJcg9R16DQ0NOzs7O7v/Xaawbt26LkYoxKc10pNukUZ56k4PoC7bTho9RLWPMT01XSdwdMvKnLeleA11Yp/WL84VabqbVXPorOmvq1WtzdqvCUCP9inBnzt3DiE0ZsyYgoIC4ZG64uLihQsXyieOESNGpKenSxR+/fXX7byEX0/jUasMfwyjX7nNpVZyXr9vuvcE1p8ACpGSktJq+ejRo0ePbiUVKVyP2KFHCNUdv4DxeM0l5TV7Y/BaGpXr/1DRJbMTrpP9hkncHIs1c6v/OKlCJplEr2xnR1/AYtefu6rtO6TurwTjLUvb752WdEvN1lLdqb903szn+Ndy2AB0iuREN6Wlpf37/+/TbGVlRaVSO920QCDIyclJTU29evXqs2fPBC0uXRZ39+5d7N/c3d2FV+u0pSnjKdHcWNXCVMBgqmhpqpC16mISBSx2pwMG4Mtx7ty5c+fOqaqqFhQU5ObmZmRkvH79WmIJV4VrLi5j5RUZRIbqTBqj7T9cd2YghsNp+gzuvSYcpyqZ/GhX/lGztcARCE13s0SFrOcFEt8JtMQbGi72vRZMxzjNjAfP2uv9A5WWeKP26DmMx5fim+qIxhv3qSt3yL9foGQkz2N5eXkFBweHhIRgGHbu3LnBgwd3rt20tLRFixaZmJj06dMHIUSlUikUyokTJ3x8pLPyG5dSwXldomZjXncykZVboKJDJvQ24H4opyXd0ps5USpdAKD0pLhDLwuMhzkIw+pOXBAv5FdUq08aI1GTX9fQmJxu8usqQRPzY/RhTU9XvIY6l1pVFX1Ye9xw/bApwmq8jzVNtx+a/rEW4XD63wVX7/lLc9BXbU2TVR97SS8kkJWTT0+52+oZARkRNDEbLqQQeuk1pqaTJ46SW79A+Ugm+KNHjx4+fPjs2bN4PH748OGdPkS/ZMmS5ORk8WtlKRRKcHCw8Mxf17FfF6v1s1LR1uK8LSGamfKqajS+dlB3tWu8mkYa6Uk0NpRKLwAoN2nt0MuI3qyJerM6tL9e9/dlbb9hhN69UO9eGq72tIs39UIC62IvkieMarrzUHvMUKKpEUKoLvYiOWi0ip4OQkjNzkatnxXt6m3d4HEtG2Rm5fJq60ljvDVcHSrW7iQNcxO+Sg4a4pI1PQeSJ4ysXLtLa5hbyykZAOigTwk+MjJy69atGzZsQAgJj42/fv165cqV+/bt60S7fD5f4gC7kZER9u/7jzuNS/1YdzSOYGSg+bVjw4VU3enjaRdvIoRUtElEc+OGM1cMV8yTSkcAKDdp7dB/LjabzWKxxEt4PB6XyxWdIxDdEy8QCGg0mqhaq+Wcd6W8/DcG388UlqOA4eXrdjVpaza+L7FfOV+FTKqLvWT408LqvILKjCeqJRTqnQc66hoIIQGT2ZT9UjBskOiAv7B9jMev/TuJMH1cA52G1FR47o4N55N7LZrVwXi6Us75QKWmPTCOXtGkRuAOdmiIS+4VPlMO/UJ5dyiXVor8RHTOOykpicViXWsB65QDBw7Y2dn99NNP+/bt279//88///zVV18dPny44y24u7sHBAS0+lTltgP156+VzFhaezKh5vDZmsNnKeHrK7fsE27Xnb3SuZiBsqJSqSYmJoqO4l8IBAKXy22/DoVCMTMzk1EAERER9fX1ES3IqDsJfn5+ev+Gx+NVVFREDxMSEoQ1Dx06JF6t1XJdDa1DQ8eXhq4qDV213XOMjqq6jqqajqq6LpmckJAg4PHKIrf8+fNGPT09PR0d4b/zBw5z3pVy3pXu276jZfsNF2/sGDtZVFmXTD40bALn3YcOxtOV8l8DZ+iStP//jeke9p3Cflsqh36hXOHlOjo66urqbYyYTsJh/95lKCwszMzMnDdv3vr16+/evbtjx45OrxhLoVBSU1OFZ/VMTEzGjx9vbm7eVmUWi8Vm/+taGF9fX2Nj4+TkZMmazwvrjsWZ7l5HXRGNVIl9dq5BCNEu3uA3MfVDJ3cuVKDcKioqBg0a1K1OMBOJRBaL1XJqF3FlZWVeXl4UCkUWAVy+fNnPz+/27dsS5QEBAbLo7j95eHgYGhpeu3atE6/FuDysuVm8pPHGPXbea6NflggfsrJf1v2VaLpnPY7Q5uR64mqPnue8/SBRSA4YIeubdFg5+R+jDpFGeuJU/nf5M6eoGK9DMv7/NwKUmCzGu+T3y48//jh37tzHjx9nZGTs2LFj+/btqampnWva2Ng4NDRUXV2dRqM9efKk/Vkwp0yZInF6vrGxseWcIeJT1eI11PhMFvNRrqanq1p/6/pznflqAODLFBQUhBCytbWV2KFXdFyfgZ33mpXzSi90Mo5IkJj3pvFGBr+J+SFstahEwGCysl9qekguUNmqXgtnSDnWjiGa9JboWtXGQoVMUkgwQAlIJvjKysrp06evWbNm9uzZ3t7e79+/71y7p0+fXrRoEZlMXr58+dGjR/v27fvs2bNt27bNm9f62fGWuxHCPXqJwsaUuwR9HY2vHRFCAgZLd+r4ulOXNAY5qvWzai4pw7i8Ls1vBZSUiopKTU2Nm5ubogP5BMOw7jDnnRR36GUKa+bWHjmnP3+aaEZqjM+vPR7Hr6drerioDbCRqG+6dwPiS95jhtfUkEesXUAwNtA2NlB0FEB5SKbDAQMGrFixIj4+/smTJ7t37yaTO3kB54YNG54/f25paenh4bFy5coFCxbU1NR4eXm1leA7iJ6azqc1CnfMMSar7kS8gMVmPn2p5TWQaNq7uZgC00+Clnr37p2dnd3876O4iqWpqdnOWixyI60delmjXbnNeJSrokvWmzNJWNKYkk4w0NMN9q89ccH019USDWcJwwAAIABJREFUM9vg1dUUESYA3Ytkgj9w4MDff/+dlJRkbGxcW1srnN6uM+0SCDY2Ngihb7/91tfXFyEkXECzi/rsXidaHJYy/+c+f27AEQnCHXO1ATac18WQ4EGrnJycFB1CdyStHXqZ4tc1NCbfNd78Y1XUIdLoIUTT3nx6E+3STeNty4gmvRtvZjTdzSKN7JEr+AEgU5Iz2RkaGo4aNSovL4/L5YaHh/ft27dz7fr4+AQFBWVmZkZERFhbW+fn53/33XdDhgzpYrg4NVW8liZeQ70hPgUhpKJLxmtpCnfe1QdYc4qKu9g+AF+UAwcO9OnTp+s79DJV93eStt8wtb6W5CDf+lOXEEIN565qDXcnmhoJ56upP3tVwGT9ZzsAfGkkE3xMTMz06dM3btzI4XCGDRt2/PjxzrV77Nix0NBQ0fS079+/d3NzO3r0aJeC/X+Ntx80pt7H4f8VvFp/a3ZRNz3ACED3JK0detnhvC7m5L/VmTwGIUQOGNlcXllz8EzT3cdq/a1ZL4pYL4r4TUyCoZ5wJgwAgDjJQ/S7d+9+9OhRaGgoiUTKzc11c3ObP39+J9rF4/HffPON6OGECRPar19dXd3Y+K8lO9lsdqvT1wsYrIa4FP3vZ9QdPsulVhFNe//vnRgZIB6Pdu2OzgSY3BGADomJiYmOjmaxWMHBwcOGDVu/fn3nxrusYFjdyQStYYO55ZXCAoKudtOdR2p9zZtuZYhq4dXVME43usACgG5CMsEzGAx19f9dp6qpqUmU13JGERERT58+FS8pLy9XVW1ljuiGCyma7s6qFqYqejr1fyf1/kls7i28SsPfl7XcnAlwJSoAHSCtHfrPlZmZKTEtQV1dnY6O5Fyw/CYGwuOEv9QRQojL45Z/JPTSwQQCow2wNjQA/0EywYeEhAQGBlZUVJw8eTImJiYkJEQ+ccTFxUmUtHqbHLf8I+PeE9M965uLy4imvbnllazcAg1XeyRcl0KViNPSqDt1sfdqOc24CUCPpqgd+pSUlDdv3oiX1NXVGRhI7peraJNMolaKHlb9ekRruLtO0OiKn3fB2tAA/CfJBL958+bk5OSMjIzy8vKoqKhOT2MnI3UxiVojPDAWm1tRhQgq2n4+dbEXTZ3WIgG//u8kg/CZdScTmt99YD0v0HCxV3SwAHR3itqh3759u0SJh4dHr1692nkJO6+omVJhuHwewuH0v/umetcJTQ8XuB0OgHa0Mi1MQECAaLrKxsZGbW1t+YbUJgGLzauq5VKrmFnPMRZHwOVyqdUIh3gfqxkPctRsLTRc7EijPFm5BXV/JZru+lk03SMAoFVS3KEXCATPnz+vrKzk8Xh9+vRxdXXF46U2ADG+oO6vRP2wb4QzWan1t1Z37Ee7dBPWhgagHZ8S/LNnz9auXVtXVzd+/PjIyMi1a9d++PBBOGIVGJ84vIZ6n70bhNv0q3d4tQ36305BCPHraPSrd4x/iRQwmJqeLrSLNwgmvZtuZWj7S2fteQCUmFR26NPS0hYtWmRiYtKnTx+EEJVKpVAoJ06c8PGRzhhkv3rT/IFa9esR8UI8SVNvxgQ4Ew9AWz4l+O+++27y5Mm+vr6xsbHOzs4LFy4MCQmxsrJSXGztEbDYeM3/nTtk5rxCCFVu/t+ythiHy6VUMJ+8gAQPQFuku0O/ZMmS5ORkW1tbUQmFQgkODpZYYKLTNJwHWCXsl0pTAHw5PiX4+vr6TZs2IYTs7e0vX74s3Jably9fSnyz0On0dia/EzBZBIP/raSrPXqI9uhPU+hwCt9X7z5puGwuQqjh/DUVXTJkegAkSHeHns/nm5iYiJcYGRlhUl/cGgDwOT4leNHM2L169dLS0pJzHIcPHy4qKhIvoVKpLW+bEREwWHjLPq0+pWZno+n1dc2hs3ozAhpvZiAMaXq6quh2xzk4AVAU6e7QR0REDBo0KCgoyMzMDIfDUanUK1euRERESCVUAEDndJe11/bvlzz+5uHh0fK2GREBkyU6RN+SXkhgxU+/Ve+J0Zk6jl9bX3/misEPs9sPQMBiixaqAkDpSXeHfvHixRMnTkxNTRXe3W5hYZGSkmJubt7VKAEAXfApwZeWloqur2EwGKJtiQnmugkBk43TaHPxRxyRQBrlVf/3JaK5CWmER/mP2zhvS9X6WiKEao/Fk/2HEc3/dTiR/epN1Y4jffZuUNFv85iBEOdNCaGX3n9Wkz4M49OaVHS7yx0NAEgwNzdfuBDmnwCgG/mU4FmsnrRag4DBUiG1meAxHr/x5n3dkKCavbHkgBF6MwLqTiaYbF/OfPyckZ7FLa803rRErC1B3V8JREvT+jOXDSJD2+uUyaracUStv/W/ps+TC9rlfxqv3+uzdwNOrZXZ/QD4XIrdof/tt9/evXsnXlJSUtLqzJUAgE77lODV1KQ8ZYRs74tlsXDqbR5RpyenYQIBwVBf5xs/etItvLYW92NN073HtAvXDVfMqz9zmfkoV9PTVVi58VYmnqTVe014+ZKt7MJ36na2bTXbcCFVY6ADp/A963mhhoudtN7Lf+I30OmXbxMtTWmX/9GdNl5u/QIlJt0d+sOHD6enp7csb2t5OkdHR4lLaF+9euXs7CzFkAAAsjoHL+v7YgVMtnAZ+FbhVIlq1ubMB88QQmq2ltyaOpwA1Z1IVNHTRni87rTxdTEXNQY54ohEAYPZcCHVaP0PeHU1vZCJdScTTX9d1eqdtdzKasbdx6a7f+a8Kan7K8F011rc/5/FlLX6M1dIvkO0/X0qVu4gjfQkGOrLp1+gxKS7Qz9nzpysrCw6nb548eKO1BfdeS/y9u3bllNTAwC6QlYJ/nPvi83IyKioqBAvqaurI5PbvPRdwGThtdr8BU8eNxyNGy5ewq+nlf+4TcPZjpaQ2kypwNicssW/qFr04VXX4bU1GfefMDKe4rU0BE1N1bv/Unfoi1okb3pquvpX/ZhP8hBCCIer+fOU+lf923z/0sOrqWdmPdedEcB69kq1v3XVrhPi9wQqFg6Px7V9qSPoIA3nAXgtTUVH0SVaWlrLli07f/786NGjFR0LAOB/ZJXgP/e+2NTUVInFJ1gsFolEaqs+efwI3OcsjFF/+oqava3GYGeNwc4IIe6H8oZz1zRc7OtOJ6la9mHnv0UYhvF4iI8xn73CkzTRv0Pl1Tbwa+rw/Syb339ACKmaGzOfvMARVT4rhs5hPslTtTbjUioQQgRdEif/NSv7pYpet7jrDxMIMCZb0VH0cCoqquYmPT3BI4ScnZ3hGDsA3QpORpNRHDx4cN++fS3viw0PD+9gC6tXr3769Km7e+sLRl2+fLmdaXBa+lHfhoSX/FGe3FTdS0UyQ/MwwX1WneDff5b1Bv2tiBqNfB7u/4/e91IhxtGp15o+djyGzyUQCBz4hA2Wrk/YDaJCC6Jmo4D3S3WhjDql0Wjq6upSvyCjHTwej06n6+vL9byD8PgQgSC/20Q5HA6ZTPb09Oz4SxobG69cuUKhUGQXVfch3fHedY2NjQQCQaPtW3WkTiAQtLqknkzBeJeRbjLeZfWGu35f7IwZM9paXUogEBQVFfn7+3e8tVNYPeK3KNXAlSJey8o6apJfJQf41QUvCzQ0NP411RcR6enpdTyGz8ViseKysj72/vctec31CMmw35KSEhMTE5m+Lwn19fXV1dXiZ3PkoLCwsFevXvLMGRUVFRQKZdy4cR1/iZ6eXnR0tOxCkp0RI0bcvXv3s14i3fHedZWVlRoaGqampnLrkcVi5eXl9evXT249IhjvMtNdxjvWA/F4PBUVFTl3unTp0t27d8uzx9LSUgsLC3n2iGFYcHDwhQsX5NljZmbmkCFD5NkjhmGenp4PHz6UZ49xcXHTpk2TZ48K5O3tLcXWYLzLDox3Gekm4x0WVAUASFloaHvzSQAA5AMSPABAyhYsWKDoEAAAkOABAAAAZQQJHgAAAFBC3WU1uc+Cx+MnTpwo504dHBzkeUktQkhHR2fEiBHy7BEh5OrqamlpKc8ejY2N27o5SnY8PDyMjY3l2aOlpaWLi4s8e1QaMN5lB8a7jHST8S6r++ABAAAAoEBwiB4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAACUECR4AAABQQpDgAQAAgP9j774DojjaBoDPXr+DO7qAdFCKCIINCxo72DBGRAmWxCjE+hI1RoyJGuubmJgvBjVWjFEjFiwUNSr22BAQFRQbIEXwgAOOq7v7/XF5T3IUKdc4nt9fe8PePnN6c8/s7uyMAYIEDwAAABig9pfgt23b1q1bN19f39TUVE3HWrt2bbdu3RwcHJSr+Gkn+g8//JCcnKy1iOfOnevTp4+Tk9POnTu1E3T58uUuLi5ubm5//PGHFiIKhcIdO3YoX9aPpfboKhF19UUyANDe1Q7aewdq77pezq5l8vPzPT09hUJhbm6uh4cHjuOai3XhwoWePXuKRKKysjJHR8e0tDTtRL9+/TqDwYiLiyO18nkrKiq6du1aVlYmEAg8PDwEAoGmg165csXPz08sFhcXF5uYmNTU1Gg04oMHD2bNmjV16lTFy/qx1B5dJaKuvkgGANq72sNBe+9Q7b2dncGnpKRMmDCBw+F06dLFxsYmMzNTc7H4fH5UVBSLxbK0tAwMDCwoKNBC9KqqqpiYmKlTpypeaiHiqVOnQkJCLC0teTxeVlYWl8vVdFAqlcpgMOh0OovFotFoSMMf8/Dhw5WVlcqX9WOpPbpKRJ18kQwDtHe1R4T23qHaeztL8MXFxfb29opte3v7kpISzcUKCwuLjIxECGVlZd28eXPIkCFaiL5w4cJVq1aZm5srXmohYkFBwatXr3x8fBwdHTdv3oxhmKaDDhw40N3dvXPnzg4ODqtXrzYyMtJoxA0bNsybN0/5sn4stUdXiaiTL5JhgPau9ojQ3jtUe29nCZ4gCAzDlC/lcrlGw5EkuWXLlkmTJp04ccLExETT0Y8cOWJubj58+HBliRY+r0gkys3NvXr1akZGxq5du9LS0jQd9NKlS7m5uZcvX05OTt6yZUtRUZE2/1vrx9JCdC1/kQwGtHf1RkTQ3jtYe29nq8l17tw5Pz9fsV1UVKTR5Z5wHJ88ebKpqem9e/d4PJ4WosfFxT1//vzChQvFxcVHjx6VSCRa+LxWVlZBQUFmZmYIoUGDBuXk5Gg6aHJy8rRp0zw9PT09Pfv163f16lVt/rfWj6Xp6Nr/IhkMaO/qjYigvXe09q61u/1qkZeX1717d4lEUlBQ0KVLF42OVjh06NCUKVN0Ej06Olox6EYLEbOzs729vfl8fmlpqaOj4+PHjzUddOfOnWPGjFEOQklPT9d0xAsXLiiHwNSPpYnodSPq8IvU3kF7V3ssaO8dqr23szN4R0fH+fPnBwYGIoR2795NoWjwFsO1a9dSUlJsbW0VL3ft2jVu3DitRVfQwuf19PSMjIwMCAggSXLFihVeXl4IIY0GnTVrVnp6erdu3RBCX375pZ+fn6Yj1lX/n1TT/8j68EVqp6C9qz0EtPcO1d5hPXgAAADAAMGpAwAAAGCAIMEDAAAABggSPAAAAGCAIMEDAAAABggSPAAAAGCAIMEDAAAABggSPAAAAGCAIMEDAAAABggSPAAAAGCAIMEDAAAABggSPAAAAGCAIMEDAAAABggSPAAAAGCAIMEDAAAABggSPAAAAGCAIMEDAAAABggSPAAAAGCAIMEDAAAABggSPAAAAGCAIMHrlxcvXnz44YczZ8787bffdF0XAAAA7RhGkqSu6wDeWbZsWXh4uL+//+DBg69evarr6gAAAGivaLquAPiXNWvWsFis169fW1pa6rouAAAA2jG4RK9f2Gz2sWPHFixYsG3bNo0GunLlysSJE7X5RnWpqKh49eqVDisAAADtAiR4/ZKUlHTr1q2EhAQbGxtd10Vtdu3a5enpaWNjM336dJFIhBA6efKkp6enm5vbL7/8orLzV1995eDg0Llz56VLlyKE8vLy+vfv7+npqdxz5cqVEolEyx8BAADaHUjw+iUhIeHJkydhYWHTp09v5ltkMlmD203vqTVpaWkbNmy4fPny06dPy8vLv//++/Ly8oULF54/fz49PX3btm2PHj1S7nz+/PlLly49ffr08ePHiYmJ586d27hx4+rVqx89erR///6qqqqnT58ihDw8PLT/QQAAoH2BBK9LS5YscXd3v3PnDkLo2LFjXl5e27dvT0xMPHr06IEDB1R23rp1q6ura9euXZcsWUIQxI0bNyIiIsLDw7/77ru62wih9evXu7m5ubq6Llu2TGXP+nVQOWxwcPDJkycVf+rTp8/Vq1dVdmjpZ8zPz58/f76NjQ2Px5s0adKzZ8+SkpKGDBni6OjI4/HCw8OPHj2q3NnY2Hjt2rVsNtvU1LR79+61tbUmJiaPHz8uKCgQi8V0On39+vVff/11S+sAAAAdEQl06vjx40FBQbW1tU5OTikpKY3tdvny5R49epSWltbU1EydOnXdunXXr183MTHJyckhSbLu9tmzZz08PAQCgVgsDgwM/P333+v+te4BP/zww/qH3bdv3/Tp00mSfP78uaura2pqqsoOijfWPdTo0aOd6nnw4IHKR+Dz+X369ElISFAkaUXh3r17Z8+eXf/fZMyYMePGjROLxQKBICoqasSIEefPn797925MTEwr/6EBAKCDgVH0OjZx4sTly5fPnTvX19c3ODi4sd0uXrwoEAjCwsIQQmVlZdXV1UOGDPHz81NerFZuX7p0KTQ0lMfjIYTCw8MvXbrk6upad8+mD3vw4MHly5fLZLIjR47MnDnz0qVLKjsEBgaqHCQ5Ofm9H/PgwYOrV69evXr1hx9++PDhw7p/qn/joH///gRBrFu3LjU1NTg4eMeOHYryyZMn79q168yZM7GxsT4+PuvWrWMyme8NDQAAHRMkeB3DMGz27NnffPNN3VvR9RkbG0dFRS1fvhwhJBaLpVJpVlYWl8tV7qDcJkkSwzDFNpVKxXG87l/fe1gej9evX7/U1NT4+PgTJ04cPXpUZYf09HSVgwQFBdWvfHJysq+vL0KIIIhPPvlEIBBcu3ZNMXLQ3t7+8uXLit1ev37t4OCgfJdidGH//v1DQ0Pz8/NPnTql7PScP3++b9++OI6vXr06ISFh9+7dsbGxixcvbuIfDQAAOjK4B6976enpXC63bp6rb9SoUfv37y8rK5NIJJMmTbpx40Zjew4bNuz48eM1NTVSqfTgwYPDhg1r6WGnTp26ceNGMzMzFxeX5sQ9d+7c63oU2R0hdOrUqaKiopMnTyqfCxg7duyFCxfKysrEYvGRI0cmTpxIkuSTJ0/kcnlhYeHXX39dVVVVVVWVnJzs5eWleAtJkrGxsQsXLhQKhWZmZg4ODj4+Pnw+v8l/VwAA6NAgwevYjRs3rly5MmDAgGPHjjWxm5+fX3R0dP/+/d3c3Nzc3EaPHt3YnsHBwR9//LG/v7+Xl1ffvn1nzJjR0sOOHz/+zp07M2fObFHcxly7du3atWs8Ho/L5XK53E8++cTKymrLli0jR47s3bv37Nmze/bsKZVKPT09CwsL582b5+vr6+vr6+3t7enpOW/ePMVB4uPjQ0JCWCyWo6NjYGCgt7f31q1bFy1a1NLKAABAxwFT1eoSQRB9+/ZdsGCBs7Pz0qVLb926RaPBTRMAAABqAOlEl+Li4jAMmzlzJoZhXl5eLi4uV69edXFx0XW9AAAAtHtwBg8AAAAYILgHDwAAABggSPAAAACAAYIEDwAAABggSPAAAACAAYIEDwAAABggSPAAAACAAdLf5+C3bdv2448/vnc3mjHVzNe47KYAIcQwpXEcmJVZQs3XDgBtcHd3T0lJ0XUttKGZ7R0AA6b29q6/Cf7Vq1dhYWFz5sxp8K8ESUiQGCHEJ0rPiP/8hLMIIfRMnv1EnhXK/kSb9QRAQ0pKSqZMmaLrWjSXQCDg8XgYhqWlpWVnZ/fq1Uu5lEBzNN3eATB4mmjv+pvgEULm5uaurq4N/ula+YX4ojikyPSkeIdoE0KIQHhXI29Xl4bfAjosgiC2bdsmFOrRpR1zc/P3JjMGg6GdyrTdzp07f/rpp6ysrB9++GH37t2DBg36+uuvV65c2aKE3UR7B8DgaaK963WCb8Ig8xGDzEcghIrEBbF5/412+QYh9LD6frrgbpn0DUKITWEb03g6riXQD2/evImJiZk/f76uK/LOihUrPv30U4NZemD9+vVZWVl0On379u2ZmZnm5uZlZWUDBgxoLMFv2rTp5cuXdUsuXLjQs2dPrVQWgI7CEH5fauRVm59/ixCSEGIJIVFss6mcVe4/YQjTde2AXuByuZs2bdJ1Ld4xsPvNVlZWUqkUIWRqakqhUBBCVCq1if39/PzMzc3rlvz1118VFRXvDSQhxPcqbw40b2oRZACAQrtP8BiGcajGJjTT7lw/W5bDrYqr852/0nWlANB3c+fObbB8+/btrTja+vXrBwwYEBIS4u7uPmTIkFGjRiUlJS1cuLCx/YODg1VK9uzZw2Kx3huILy1LKUtQJHgSkWJcxKZyWlFhADqCBhJ8RkZGRkZGRERESUmJg4OD9uvUIjZMu0Hmw0+9+fO1OG+m/TxdVweA9mHcuHFqPFpQUNCdO3dOnTplamrq6elpZWV1/PhxT09PNYao70FV2t8Vlz93WqrRKAC0X6oJPi4ubuPGjSKRKDQ0dNCgQStXrpw9e7ZOatZMGMJq5NX9zYbeF9y6xE82psJ9dwDeb+zYsSolBEGsXLmyfnkzmZqazpw5s831apiYEJVIihBCZZI3MlL6SvQcIVQqLcZJXEMRATAAqgl+y5Ytt27dmjFjhrGxcUZGRu/evfU5wZdIigpELx/WZDiwnIypvDLJG0cTl/uC2/4mfeHuOwDv9f333//8888VFRXW1talpaVDhw7VdY0alll171zpSYSQjJBWyip+L9iGEKolam2YdrquGgD6SzXBC4VC5Z0wDodDp9O1XqUWKBTn3RPcfCMuZFGZNfJqM7pZZvW9WqK2O9efQWk3jxgBoCu//vprdnb2ihUr5s2bV1VVtW/fPl3XqGEBpoMCTAchhIrEBd8//+afUpJ4Jsz57ukShJARjbvYdRV06wGoSzXBR0REhISEFBcX7927Ny4uLiIiQjv1yMzMLCsrq1vy5MkTDHtPc+1l0r+rUbfH1ZmhNjNOFB+a6TB38/NVsxwWQjsHoDmYTCaPx/P39799+/asWbOio6O1E/e3335TeUyuoKCAyWQ2571sGmeGwzyEUHbNg4dV6ZM7z0QIMTAGtHoAVKgm+DVr1iQlJV2/fr2wsHDDhg2BgYHaqcfevXsfP35ct+Tu3bsqKb9BBaKXDmwXxbYt055JYRaIXjqyYboM0GIivLajDckePHjwrFmzYmJiJk+e/ObNGxMTE+3E5fF4ZmZmdUuoVKri+bqmUTAqHTGc2W4IIb60lEM1UmwDAOpTTfB8Pr9fv379+vVTvrSwsNBCPf7v//5PpSQgIEDlSdkGFYhfObCdWRSOEc0YIdSD1ye96g4keNAKO/I2j7Oe3NWoBROsKv3++++rVq1SbMtkssLCwry8vP379x8+fLi6unrevHkxMTFqrax67N69++nTp+7u7j/99FNaWprWLtGHh4erlJw4ccLY2Pi9b7Rhdv6qyzrNVAoAQ6Oa4JXPpwoEgtzc3B49emRkZGi9Vi3wWvTKw7i7I9tloXMMQqgHr0980b4J1lN1XS/Q/shJuZyUte69M2bMmDFjhmJ78eLF+fn5ubm5J0+evH//fk1NTa9evYKCgvRwprby8nJLS0s+n+/n5+fn56fr6jSLEfWffoAzuwsG62EC0DjVBH/37l3ldlZWlvI3S28ViF+NsBqvfOlu1O2trKxCxjeja+PCAwAqEhMTFXn9/PnzUVFRLBaLxWIFBgYWFBToYYJvdx36uiwYVhYMK13XAgD91dRMdj4+PjJZK09otAMn8TeSos7Md7PxUDBKd65/ZtW9IRZBOqwY0E98aVnc61gSkcoSEhEFta9IDCGExLho66uNVIyGEOLRTM3o/7pD1NXI671XhgoLC+fMmZOQkGBqahoWFqYozMrKunnz5rZt29T7WdSi3XXotexs2Uk3jkfr7toAoHOqCf6LL75Qbufk5Hh4eGi3Pi1TIim0oHdSeSLOj9fn74rLkOBBfWZ0i3GdJuOkvG5hkfi1jJAihK6Un/cy9unEsEUIWTKtjKjcurt1Yto0fXAcxz/++OPFixcrh7CQJPnzzz9v3779xIkTWhu/1mr636HXvgLRSxOaKSR40E6pJvi6w+bHjh3blokvCILIzMwsKSmRy+V2dnZ+fn7NGSXbIvmiF47/G0Kv5M31iyuIlRJSeBQeqKBgFA9jb5XCbtweio2HNel9zQK9jH1bd/A1a9aw2eylS/+ZORXH8cmTJ5uamt67d4/H09MJFnXVoY+Pj1d5TK64uJjD6ViPMACgae8S/O3btxFC9vb2df987969gICAVhw3NTV17ty5tra2dnZ2CKGioqKCgoI9e/YMHjy4bRX+l9fiPDu2k0qhEdXYke3yRPjQh6t3tzyBoUpNTd27d+/9+/eVkzfEx8czGIy9e/fqtmJNU2OHvkX4fL7K2nFyuRzH9WXe2cv8c7W4ECFUKM7HSbxCVo4QcmK7enPbxzhEABTeJfiff/4ZIVRUVHTt2jVfX19jY+Pbt29PnDgxPj6+FcddtGhRUlKSm9u7R1QLCgpCQ0MV3Qh14UvL/E0aeCLOl9crqyoNEjxokQFmQ+qO52iRH374oaamZuDAgcoSDw+Pa9eu2draKl7u2rVLveu7tJF6O/QtVX8tu9TUVK1d55CTsgtvk4KtPmxsByFeLcJrEUI4KZcQ4lq8BiEkJkTaqR4A6vIuwR8+fBghNHLkyOzsbMWVupcvX0ZGRrbUJQflAAAgAElEQVTuuDiOK3/aFKytrUmSbGz/1qmWV3EbWl3Gh9dr68uNH9vNUW84YNjassp4cnKyGmuiBert0LcvQnnNubJTTST4sZ1CFRsVMn53rn9/syFaqtn/SAhxsaQQ5vABbaR6Dz4vL8/d3V2x7ezsXFRU1LrjLliwoFevXhMmTLC3t8cwrKio6PTp0wsWLGhTZeupkldy6Q2MXXJgOZOIKJEUwloUADRIvR16oF7PhDlny04ucV2t64qA9k01wffv3z80NDQiIoIkycOHD/fp06d1x503b9748eNTUlIUXQRHR8fk5OQmVpcXiURisbhuiVwuf+8Zf7W8ikdteHByd67/g6o0GytI8AA0Sl0deqThQbVyUkbD2rrwlQivfVmbixASymvkpOxxdSZCiEahdeF4UjBqg2+xYtiY0N8/nyYA+kk1we/cuXPHjh2HDh2iUCgffPBBW3r0paWl1tbWU6ZMUT4glJiY2NhtyI8++kjl9nx1dXXTxycRKcSrjWkN37fz5fa6yE8eZRXS8ooD0FGoq0Ov6UG1m3JXDDAfOsxyTFsO8qL2aUpZAkJITsglhDi57ARCCEPYTPu5lgzrBt/yoY3qlLoAtCPvEvzChQvXrl37zTffIIQUt8+fPn26dOnSrVu3tuK4Gzdu3LFjR2BgYHR09IkTJ/z9/RFCc+bMKS4ubnD/lJQUlZKAgAArq6amqarFhUwKi9pI17sbt8fugv+rxYUcqlEr6g9AR6CuDn1LB9VevXr1zZs3dUvevHkjEomOHj2qeDlw4MDOnTsjhF68eJGWlna3MD2f8YZvJVQpV769OeWP03KdUHeEUC0ufGHzbKnPGkV56qmrLTqO5spPlfz5R9oefrZAUZiEkjv5mXeysfrJe1/+y3zt1wfKtVl+/vx5iUSC1Opdgh8xYgSLxVJOXdlGsbGxmZmZFhYWWVlZkyZNSktL43K5739bS5TLyppYIJJJYXUz7nFfcCvQfLh64wJgANTboW/poNqLFy9mZ2fXLSkvL6+pqVEmeHt7e8UP39OnT48ePfqk6mUh7W0lR6xSrny7ovxs2cmc+89uHr2jUq6yv4yQMj/6Z7KjBo+jq/IJNlOZVNO9d/ZWyStLJIXuRt7/GfhF/+799a2eUK6J8qSkJLUneEylEebk5Ny4ceOzzz5buXLl5cuXN23a1LoVY728vNLT01ksFkJoy5YtDx8+3LNnj62tbWNn8PUpzuATExMb2yG96va2V9/v8j3e2A73BDfPvDka3vkzT+PuLa0/MCTFxcW9evVqyw1mtaPT6SKRiEZraq7o169f9+/fv6CgQBMVOHXqVFBQ0MWLF1XKx44d24qjbdu2bevWrfUH1UZFRTXzCPXbu4QQb3nxnYQQI4QKxflMCsuS0Qkh1Mc0cEynjxo8yJGifaZ08yCrCU3HqpFXf5e79Huv35pZN+17VJ0Bg+w6Gk20d9Xfl//85z+zZs26c+fO9evXN23atH79+voXz5sjPDx84MCBUVFRkZGR0dHRH3300dSpU2tra9VR538I5dVNryXVg9d7V96WJ8IsSPCAIAiVmVU6uAkTJiCE3NzcVDr0rTtaSwfVNgeDwjRnWBVLChBC/1yqwxBCqFCc15bDIoSMadz1nq25UKFNTVyeBKCZVBN8SUnJlClTli9fPm3atMDAwBcvXrTuuN9+++3AgQP5fD5CCMOwY8eOxcfHm5mZtbW+CFXJK5/UPEIIZddkkYi8W3lDEcKX21tlblo6xrBi2OSL2vpzANo7IyMjOp1e9w6xzjk5Oal95uZWUFeHHiHk4OCg3qfsMIRFOv4zk+6y7Eg3jkeU05IG90wqPfZWWooQeiF8SqcySiSFCCFndpcPLEY1dnA6ptfzWHc18uLRTXVdC9DuqSZ4Dw+PJUuWxMfH3717d8uWLW2ZW2r48Hc3v6lUanh4eHi4GoakvpEU3xPcRAi9FuchRCq2EUIu7K7KtSNzarKq5VUIISaV/Uz4WNEJsGBYuXLc214B0O7weDwNXehu79TVodctW6aDMZWHEHorLWVTOM7sLgghq0YGxrcLDArTgeWs61qAdk81wcfGxh44cODkyZM2NjZ8Pl8xG4Ze6WrkpVjcKa7g1woZf67Tl/X3eVidUSYtQQiJ8BohLrxRfolJZbly3CHBA1CXGjv0GuXKcW/ijLanyT/T65ZICk3p5k2cuAPQoagmeCsrq2HDhmVkZPj6+kZFRbXxLppG1eDVGNnwbapQ2+mKjSNF+17U5rpwuk6wec9K3gB0QLrq0GdmZpaVldUtEQgETayoG+m4mETvhgPjJF4mfWPD7KzBKgLQ/qkm+Li4uI0bN4pEotDQ0EGDBq1cuXL27Nk6qdl7CeU1bOr715d0N+52iZ8c3OlDJoWlhVoB0I7oqkO/Z88elcfkiouLTU0bPUenYP8ar/Ci9mlCycFlbutUdnNkuzQ28xUAHZBqgt+yZcutW7dmzJhhbGyckZHRu3dv/U3weM1/XFc2vQ+byjGjW7gbeV8vvzjcsjXP/wBgwHTVof/ll19USgICAiwtLZv5dhIRREMP2Wt/VRgA9JnqOF6hUKh4eB0hxOFw6PS2zv+sOVVyQWMT0SuFWE8ZZD5iTKePzpWdJkh9WW0aAD2h6ND36NFD0aFv9WNyWiAlpAde79B1LQBoT1TP4CMiIkJCQoqLi/fu3RsXFxcREaGdeoSGhqanp9ctKSws7N690efXm56IXoULp6sFw/Ku4GaA6aC2VhQAA9KOOvRiovbvist3Kq8jhAiES3DpwofTEEIsKnudx1a4AQdAfaoJfs2aNUlJSdevXy8sLNywYUPrprFrhe3bt6usLjNx4sROnTo1tn/TE9HXN9pq4rHiP/qYDGhs2SgAOiBddehbh0Fhrff8FSH0vDbnTMmxaNeVCCEKokB2B6BBDcyUOXbsWOV0ldXV1WqfQ75BVlZWKkvLsFisJmYCqZJX8mjvuT5fly+v94W3SWfLTjU2ySUAHZCuOvTNx5eWPaxORwiJiVopIb1XeRMh9EZahGGYEdVY17UDQK+9S/D379+PiYkpLy8fM2bMwoULY2Ji8vPzFQs867B+jamWV3FbkuARQjPt563JXeLP62vLstdQrQBod3TSoX/16pVimksloVBobt7AyutV8spXomcIocc1D3BSrtiuklfCkBoA3utdgv/0008nTpw4YsSI/fv3+/r6RkZGRkREODs7665uTamWC2rk1SQimz9jswXDKsQ6bO/rrSu6bIJ5nkEHp9sO/erVqx8+fFi35NWrVxxOA0+9unC6unC6IoQiH4RyqMYz7echhMSE6FXtcy3UE4B27V2Cr6ioWL16NULIy8vr1KlTim29VSkrfyMtwkk5DWvBsKDhlmPvVd48Xnwg1HaG5uoGgP7TbYc+Li5OpUSxmlwz386isJu5gtSfRXtHWYWY05v7AB4AhuRdgqdS/xl9ZmFhYWRkpKP6NFdW9f0mVptuDIawRS5ff/98JZvKGdspVFkuJ+VX+X8Nsxz93iOUSApNaGbNmWAHAH2m9g69QCDg8XgYhqWlpWVnZ/fq1cvLy6uNx6yRV6UJbim2CZJgUJlX+OcRQn68Pib0Zq1clSvMDjAdBAkedEy6X8+qRSSE+HF15uPqTL60jEQou/qB4qUIb+5CtByq0Rcu314rv3i2NEE5+aVQXn2mNL45b098czSz6m4raw+A3lBvh37nzp0BAQFyuXzDhg2TJ0/+66+/goODd+3a1cbDighRnuj5K9GzV6JnJELdjH1eiZ4ViF/W4sI2HhmAjuDdGXxeXp5yfI1QKFRuqzy9pltvpaUpZQkkIsukbxBCZ9+ewhBGQZSPbCKcOV2ac4StrzaG2k5f5rY29tV/c4SPZjksbNFofBKRdefE1hoZKeVLy2yYdtoPDcB7rV+/Pisri06nb9++PTMz09zcvKysbMCAAXPmzGnLYa0Y1jPs5yq2b5SnTrOLas4tOTkpv8w/KyWkCKFKWcXNitTsmiyEkA+3pwPbuS31AaB9eZfgRSKRDuvRTKn8s0J5NUJITkgRImvlNRjCrJmdm5ndEUKVsnIxLrJl2q/osimh5ODKJwv7m33gYeyNk/gr0XOEEIaQA8tFZe5rncutyT5bdnKx6ypdVwQYCPV26K2srKRSKULI1NRU8XSr8gqB9hEkXiWrJBCBECIQLsZFtZQahJAQ16NzFQC04F2CZzKZ6j00QRCKQblyudzOzs7Pz6+J59qbaVyn0Ep5BUJo3dNlGCKn2UdRMVqLTsGVqBjVw7h7RtWdvytSL75NRoj4LW8zi8KmUeiRjotVFpNelh31VvoGIYQQ+XfF5V35PyOEhlmOnmYX1cZP1Ey6unIADJV6O/Tr168fMGBASEiIu7v7kCFDRo0alZSUtHDhwsb2r6ioqKioqFsikUiaHlVjx3KkoGZ1GhgU5ke20xTb2TVZwyzHKMbhA9DRNDDRjVqkpqbOnTvX1tbWzs4OIVRUVFRQULBnz57Bgwe35bCmdHNTujlCiMRIKqK6sLs0Z2Y6CSE+WrwfJ3GE0FtpaVLpMcUz9P68gHUevyKEXtbm/vDiWx7NNF/00o7lmFKaYMu0s2B0MqObc2k8Iyr3e6/fFIfalb+lO9cf1rQA7Z16O/RBQUF37tw5deqUqampp6enlZXV8ePHPT09G9t/9uzZGRkZdUuKi4ubWC4WIbTK/Ue1VReAjkFTCX7RokVJSUlubm7KkoKCgtDQ0Nu3bze4/7fffpuTk1O35NmzZzTav6onIcTLc+bKCRlCiCQJhFH+82gmQmic9eQgqwlNVIaO0V057jJChhB6VJ1hy3SwZHRCCHVi2ip2MKdbMimsmC4bxYQoX/SyQPTyjaQ4uyarUl5eLRcI5TViQsSmcqiIKidlD6vTk0tP0P99L5BDNUKY+p+tL5O+KZeWof+dwc95MAkhhGGUrkZe8Ch/fWwKR9/urTTHeOswe5aTrmvRVqampjNnzmzmzsePH1cpWbZsWfMfk2s+K4Y1THgHOixNJXgcx21tbeuWWFtbN3EJbuTIkT4+PnVLKioqvL2965YwKaxNntvlpBwh9F3u0qEWoweZD0cIvbcBUzDqALOhiu2r5X/1NAlQuWRHxag0jIYQYlHY7kbd3I261T+ICK/FSfm+17EeRt7eXD8ZKav711pciFr+2N57kSQhISUIoQLRy/uCOxNspiCEaBhdUVugQkTUEiSh61q0DAWjWNDVn9h0a8iQIZcvX9Z1LRBC6HOnpbquAgA6o6k8sWDBgl69ek2YMMHe3h7DsKKiotOnTy9YsKCx/QcNUl3nLT09/cCBA9evX29wf2yAeP9fx6vzm/t0nJLv511jE+OqX6u+kW5M21ZzqIk3yuVyDMMsvUxr34glAlkTe6qRVCplMBgIIZMuxnaBVpvjdmo6okwmo1KpbR8t0XwkScpkMsXH1BqpVEqn0zENXHRpDEEQDAaja9cW3AxWVFJzVdIcHG/xPLI0Gu2XX345cuRIg3/NzMzU8jdE0d61PFRQ2d61Btq7huhJe8daMV1MMxUUFKSkpBQVFSGEbG1tx4wZ4+Dg0Py3C4VClYv2SgRB9OvXb//+/eqpaPMcPHjQwsIiODhYaxH5fP7atWt//vlnrUVECG3durVfv359+vTRWsTc3NzDhw9/++23WouIEFqzZk1ERESXLs19+KLtbt++/fDhw82bN7foXZ06dWpRq9ETu3btaukDctDeob1rTsdt72Q7JJfLqVSqloNGR0dv2bJFmxHz8vIcHR21GZEkydDQ0KNHj2oz4o0bNwYMGKDNiCRJ9uvX7++//9ZmxCNHjoSFhWkzosGA9q450N41RE/ae/sbkQQAAACA94IEDwAAABggSPAAAACAAYIEDwAAABggqp6v+94gCoXi7Ozco0cPbQY1MzPr3r27paX21p1ks9l2dnYq0wNomrm5ub+/P4/H01pEHo9nZ2fXxKxnmmBlZdWnTx8Wi6W1iCYmJs7Ozq6urlqLaDCgvWsOtHcN0ZP2rsHH5AAAAACgK3CJHgAAADBAkOABAAAAAwQJHgAAADBAkOABAAAAAwQJHgAAADBAkOABAAAAA9T+Evy2bdu6devm6+ubmpqq6Vhr167t1q2bg4PDxo0btRn9hx9+SE5O1lrEc+fO9enTx8nJaefOndoJunz5chcXFzc3tz/++EMLEYVC4Y4dO5Qv68dSe3SViLr6IhkAaO9qB+29A7V3Xa920zL5+fmenp5CoTA3N9fDwwPHcc3FunDhQs+ePUUiUVlZmaOjY1pamnaiX79+ncFgxMXFkVr5vBUVFV27di0rKxMIBB4eHgKBQNNBr1y54ufnJxaLi4uLTUxMampqNBrxwYMHs2bNmjp1quJl/Vhqj64SUVdfJAMA7V3t4aC9d6j23s7O4FNSUiZMmMDhcLp06WJjY5OZmam5WHw+PyoqisViWVpaBgYGKpa313T0qqqqmJiYqVOnKl5qIeKpU6dCQkIsLS15PF5WVhaXy9V0UCqVymAw6HQ6i8Wi0WhIwx/z8OHDlZWVypf1Y6k9ukpEnXyRDAO0d7VHhPbeodp7O0vwxcXF9vb2im17e/uSkhLNxQoLC4uMjEQIZWVl3bx5c8iQIVqIvnDhwlWrVpmbmyteaiFiQUHBq1evfHx8HB0dN2/ejGGYpoMOHDjQ3d29c+fODg4Oq1evNjIy0mjEDRs2zJs3T/myfiy1R1eJqJMvkmGA9q72iNDeO1R7b2cJniAIDMOUL+VyuUbDkSS5ZcuWSZMmnThxwsTERNPRjxw5Ym5uPnz4cGWJFj6vSCTKzc29evVqRkbGrl270tLSNB300qVLubm5ly9fTk5O3rJlS1FRkTb/W+vH0kJ0LX+RDAa0d/VGRNDeO1h7p2ktklp07tw5Pz9fsV1UVNS5c2fNxcJxfPLkyaampvfu3VMsxqDp6HFxcc+fP79w4UJxcfHRo0clEokWPq+VlVVQUJCZmRlCaNCgQTk5OZoOmpycPG3aNE9PT09Pz379+l29elWb/631Y2k6uva/SAYD2rt6IyJo7x2tvWvtbr9a5OXlde/eXSKRFBQUdOnSRaOjFQ4dOjRlyhSdRI+OjlYMutFCxOzsbG9vbz6fX1pa6ujo+PjxY00H3blz55gxY5SDUNLT0zUd8cKFC8ohMPVjaSJ63Yg6/CK1d9De1R4L2nuHau/t7Aze0dFx/vz5gYGBCKHdu3dTKBq8xXDt2rWUlBRbW1vFy127do0bN05r0RW08Hk9PT0jIyMDAgJIklyxYoWXlxdCSKNBZ82alZ6e3q1bN4TQl19+6efnp+mIddX/J9X0P7I+fJHaKWjvag8B7b1DtXdYLhYAAAAwQHDqAAAAABggSPAAAACAAYIEDwAAABggSPAAAACAAYIEDwAAABggSPAAAACAAYIEDwAAABggSPAAAACAAYIEDwAAABggSPAAAACAAYIEDwAAABggSPAAAACAAYIEDwAAABggSPAAAACAAYIEDwAAABggSPAAAACAAYIEDwAAABggSPAAAACAAYIEDwAAABggSPAAAACAAYIEr6devHjx4Ycfzpw587ffftN1XQAAALQ/GEmSuq4DaMCyZcvCw8P9/f0HDx589epVXVcHAABAO0PTdQVAw9asWcNisV6/fm1paanrugAAAGh/4BK9nmKz2ceOHVuwYMG2bds0GujKlSsTJ07U5hubIBAIGvtTRUXFq1ev1BsOAAAMGCR4PZWUlHTr1q2EhAQbGxtd10VtfvzxR0dHR3t7+y+++KL+vaGqqqrBgwcrtvPy8vr37+/p6fnLL78oSlauXCmRSLRaXQAAaM8gweuphISEJ0+ehIWFTZ8+vZlvkclkDW43vafWZGZm/vjjj+np6U+ePLl+/XpCQkLdv27YsCEgIKCqqkrxcuPGjatXr3706NH+/furqqqePn2KEPLw8NB+tQEAoJ2CBK8XlixZ4u7ufufOHYTQsWPHvLy8tm/fnpiYePTo0QMHDqjsvHXrVldX165duy5ZsoQgiBs3bkRERISHh3/33Xd1txFC69evd3Nzc3V1XbZsmcqe9eugctjg4OCTJ08q/tSnT5+rV6+q7NDSz0ihUOh0OpvNZjAYLBaLQvnXdy8oKGjDhg3KlyYmJo8fPy4oKBCLxXQ6ff369V9//XVLIwIAQIdGAv1w/PjxoKCg2tpaJyenlJSUxna7fPlyjx49SktLa2pqpk6dum7duuvXr5uYmOTk5JAkWXf77NmzHh4eAoFALBYHBgb+/vvvdf9a94Affvhh/cPu27dv+vTpJEk+f/7c1dU1NTVVZQfFG+seavTo0U71PHjwQLnD0qVL2Wy2iYmJyhsVXr9+7ezsrNgWCARRUVEjRow4f/783bt3Y2JiWv8vCwAAHRKMotcXEydOXL58+dy5c319fYODgxvb7eLFiwKBICwsDCFUVlZWXV09ZMgQPz8/5eVr5falS5dCQ0N5PB5CKDw8/NKlS66urnX3bPqwBw8eXL58uUwmO3LkyMyZMy9duqSyQ2BgoMpBkpOTm/iAN27cSExMvHLlCpfLnT179sGDByMiIhrbmcfj7dixQ7E9efLkXbt2nTlzJjY21sfHZ926dUwms4lAAAAAEDwmpz8wDJs9e/Y333zz6NGjJnYzNjaOiopavnw5QkgsFkul0qysLC6Xq9xBuU2SJIZhim0qlYrjeN2/vvewPB6vX79+qamp8fHxJ06cOHr0qMoO6enpKgcJCgqqX/nk5GRfX1+EUGJi4kcffdSnTx+EUGRk5MmTJ5tI8Ernz5/v27cvjuOrV69OSEjYvXt3bGzs4sWL3/tGAADo4OAevB5JT0/ncrkODg5N7DNq1Kj9+/eXlZVJJJJJkybduHGjsT2HDRt2/PjxmpoaqVR68ODBYcOGtfSwU6dO3bhxo5mZmYuLS3Pinjt37nU9iuyOEPLx8UlOTlac/R8/ftzX15ckySdPnsjl8sZqRZJkbGzswoULhUKhmZmZg4ODj48Pn89v4oMAAABQgASvL27cuHHlypUBAwYcO3asid38/Pyio6P79+/v5ubm5uY2evToxvYMDg7++OOP/f39vby8+vbtO2PGjJYedvz48Xfu3Jk5c2aL4jYmPDx83LhxvXr18vT0tLa2/vLLL6VSqaenZ2FhYWNviY+PDwkJYbFYjo6OgYGB3t7eW7duXbRoUUtDAwBABwRT1eoFgiD69u27YMECZ2fnpUuX3rp1i0aDuycAAABaD7KIXoiLi8MwbObMmRiGeXl5ubi4XL161cXFRdf1AgAA0F7BGTwAAABggOAePAAAAGCAIMEDAAAABggSPAAAAGCAYJAdAB3R3LlzGyzfvn27lmsCANCQBhJ8RkZGRkZGRERESUlJ05OuAADaqXHjxum6CgAAzVIdRR8XF7dx40aRSPT48ePu3buvXLly9uzZOqlZZWXl8+fPdRIaAD1hbm6utaclCYJYuXJl3TX9tAnaOwBqb++qCb5Hjx6XL1+eMWPGmTNnKisre/fu/ezZMzXGa76YmJg//vjD2tpaXQck6ezawPm0oiyZfU/mk3P01/fVdWQANEEqlVZVVb169UpzIb7//vuff/65oqLC2tq6tLR06NChSUlJmgvXBLW3dwDaF020d9VL9EKhkMViKbY5HA6dTldjsBbBcXzRokVffvmlGo95v0QafnrIIHvm7v9OUuNhgZ7DcTw2NlYkEum6Iu+YmppGRUU1vc/r16/79++v0Wr8+uuv2dnZK1asmDdvXlVV1b59+zQargmaaO8AtCOaaO+qCT4iIiIkJKS4uHjv3r1xcXHNWe9LP1WKCVMWBSF0KU+Smid+KZCL5Si/Sv6BI/NRmezkU9GH7mxd1xFoSWlp6ddffz1//nxdV+SdFStWfPbZZzqfkJjJZPJ4PH9//9u3b8+aNSs6Olq39QEAqJHq78uaNWuSkpKuX79eWFi4YcOG+mt+a0hSUpLKoiM3b97s0aNHqw/4aXL5SCdmmYhMzRfbGtGkBCoXEU4mtDyBnMvA1t2o4jKx4U6sNlcctA9cLnfTpk26rsU7P/74o66rgBBCgwcPnjVrVkxMzOTJk9+8eWNiYqLrGgEA1EY1wfP5/H79+vXr10/50sLCQgv1ePTokcoQm9zcXOVy5q3w60izkX+WOZlQA+1ZD0qlu8aY3SuWjXVjkQglPxdtulW9K13Y3ZJubURtc90BaK9279799OlTd3f3n376KS0tTYeX6N9LLCepFESn/PObUC0luYxGfx8qxcTn5yp+GWnWiUNBCP3fvWpTJmWmj5GW6gqAflAdZNenTx/FhkAgyM3N7dGjR0ZGhi4qhgICAqysrBITE1v39s23q59Vyu8USZk0LCXM0pT5ryl95ATadr/mj0fCrwfwJnSFa/UGrri4uFevXkVFRbquyDt0Ol0kEjV9iV5xT66goEBz1eDz+Sol2unQDxs2LDU1VaUwICDg1q1bjb3lj0e1ic9Ee8eYc+jYN1cFZ56Jzk6xsjGiFlbjK68K9owxp/w73e/OFP75uPZgiPmhx7UXX0kOjDc3Y8G8XkB/aaK9q/6+3L17V7mdlZXV9CLieiuHLzuaUzvMmWVjTJXi5MFHtfN7GtfdgUZBi3obD3dmRp4t33yn+kyopSmTwhcRnyWXbx5m2sUM5v8BHUJwcLBiQ8sd+kuXLqmUBAQEWFpaNvGWCG9Obrnsk6Tyr/pxz74Q0SjYzdfSfnaMaWf4n/oaUeqdzM/uYYQQGhP/1opDOTzBArI76ICaymQ+Pj4ymUxrVVEXgkRfXxF4WdCe8GVHJljUyoiPT5ezadgsX9ULdN6W9PNTOo079jbwQOm2UWYbb1UNdWJBdgcdRzvq0GMIrR5k8p+/KiJOl28ebuJtQf/4TDlOkAt6Gc/o3vC19yopwaBiEuN4s1MAAB62SURBVBxJcVgzE3REqsnsiy++UG7n5OR4eHi0+tAEQWRmZpaUlMjlcjs7Oz8/Pwql0U50/UF2paWlRkbNvWcmxUkG9Z8+/MmnIgYVG+bMnujO5jIwLoN6KMT8+mtpg280omMXplpNSuDPSi7vb89cFsBtZkQADIz+d+iz38ruFEsH2jN+z6rdMMSEikgxgbiMhn9VfrpbfemVJDnM8vgTUcTp8oMh5jDgBnQ0qgm+7rD5sWPHDh06tHXHTU1NnTt3rq2trZ2dHUKoqKiooKBgz549gwcPbnD/x48fq8yoU1tb2/yfmw1/V+EE+m6wCYbQjowafi1xMISjvGpnbUSd5NHojfZKMSGWEw482oM30vnnKzYNMW1i8A4AhkSNHXpNe/xW9klS+YYPTIY5sRZfrAw5WrYkgDvMkTU9sRwhNPHfT71Wionst7I/xpubsiizexhhGLqYJ/m4G0dHdQcAhRx7u3GIibclHSF04GHtswrZmkEaf2jlXYK/ffs2Qsje3r7un+/duxcQENCK4y5atCgpKcnNzU1ZUlBQEBoaqohSX/0JLjIyMpr/0M6yfrzZyeUrLgt8O9FfVsgPhFjUvyfXIL6IiDjDH+3Kmt+Tu+B8+cMy+bijZf830syvk85m+AFAa9TVodeCCjGxeZjpYAcmQsiOSx3swJrkzjFlUfaPM/8qVTChK7tukzdlUXaNNle+/Kze7TkAtGxJX+6spPJdo80fv5Xtzqz5I0Qbo1nfJfiff/4ZIVRUVHTt2jVfX19jY+Pbt29PnDgxPj6+FcfFcdzW1rZuibW1tcqIfTXi0LDdY8wnJ7w9/qQ2xJ3drzOjmW98WCab5M6Z42eEEPp1lPnaGwJPC1pUSvkMH6PP/Y2pcCbfMQgkhMn/nrPACSTGSSN6y/7vf//991WrVim2ZTJZYWHhy5cv161bd+XKFRzH165dq29zRqm3Q79jx44rV67ULz98+HCD+4eFhaWlpdUtKSoq6t69exMhBtozldtf1rmV5mZKOzZRG7+VBkmCk8z//cwRJMJJkt7McyPQQh84Mr8fajr9DJ9Dp8RPtHDgauOG0bsEr2iKI0eOzM7OVlype/nyZWRkZOuOu2DBgl69ek2YMMHe3h7DsKKiotOnTy9YsEAtlW7Q/RJpiZBAGJLjiCBRM7+lHzgyP3D854eDRkGKayZDnVhfpQpS8/g/DjNxMoExdwZOJCdH/lmmODuU4uS88xU+VvT/9G7ZaIwZM2YoR6gtXrw4Pz8/LS2ttLT06dOnL1++7NGjR1hYmA4nfq5PvR366dOn3759u6qqat68ec3ZPzY2trq6um7JxIkTraysWhFaQ6ac5K8ZxPO0oCOEjubUPimXrxzA03Wl1KxWRg49VPrrKLM+tgw5gaIvVHQxo0X3gXFImlJUg7NomIwgy0WEthO8Ql5enru7u2Lb2dm51Y8Oz5s3b/z48SkpKYojODo6JicnN7H4bBsH2d14LVl8sXKWr9GN19ISIb7qmmDt4Nbf3rAxosaNMz/wUDgpgT+vp/FMHyM4lTdgbBr2W7BZ5NmK9R+YHM8RsajY/J6t/41LTEw8efLk/fv3s7KyvvrqKwzDnJyceDwejuN6leDV26E3MjL64osv/vzzz+HDhzdnfysrK5V0zmKxmhiEq32f+hrNSCzfO8Y8my/75V7NH+PN3/+e9oZDx7aOMpt/vuLn4WbxObVCGTn3348TAzX6/aFw7wNhwiTLx29lkSnle8aYd7fS+A+CaoLv379/aGhoREQESZKHDx9WznvTCqWlpdbW1lOmTFHeSk9MTGxsFeo2DrIrqMZ/CzbfmlYd3o09zIn1893q5p/ENwhDaEZ3oyGOrJgrlUnPRGsHm0RfqNw4xKSXDQMhtDOj5kUlvmkIzOvZzhRW44v+qpA3dKfIlIXNO1fBpmEuprRJCW/r7/CBI3Px+05uCgsL58yZk5CQYGpqOmjQIIRQbGzsvn37Fi9erFzDSa+oq0OPEPL19fX19VVTvXQv2JWFEIo4w2dTsSMfWhjqlby+toxfRprNSir3NKcf/tCcCacyGnOzUHooxKKzMbWzMRXD0IVXYh0k+J07d+7YsePQoUMUCuWDDz5odY9+48aNO3bsCAwMjI6OPnHihL+/P0Jozpw5xcXFDe7fxkF2U704QhmZViL9daQZh459raaLaY486h/jLY7m1H6aVO5nzYhMKd8WZP6gTBafLToYYoA9eoNnY0z9brBJ/aEgcoL8/na1EQ3LqyImdmX3sW1gDIcl5z3nlziOf/zxx4sXL1bO9IwQGjNmDIPB2Lx58+zZs/Vwpnc1dugNT7WUoFMwGYmqpQb7GD1Ooj8f13pa0Aqq5eklsn52zR29BFpqR5CZcnu4E0s7K6G8S/ALFy5cu3btN998gxBSjI97+vTp0qVLt27d2orjxsbGZmZmWlhYZGVlTZo0KS0tjcvV7K2dG68l/tYMTgvHRr0XhlCYJ2eUC2vz7WqSRDMT+ZZs6vGPLOCZ2vaIiiHFYyp14ST6/Gy5OYtyYJzFgzJp1NkKTwuz/i3/pVuzZg2bzV66dKniZWJioqura7du3ebMmXPy5Mm7d++OGDFCDZ9BrdTVoTc8R3Nqf7lXc3yiRTZfPiu5fK9WLqhqGUGiRX9VSHDyzwkWd4ulCy9U/BZk1tMGcrzheJfgR4wYwWKxlFNXthGXy1XcQffx8Zk7d250dPSePXvUcuTGXM6XDHFkvn+/VjFlUtYNNuHQKX88FL6pJQ48qv2iDxeuZhkGOUH6WTM+9zOmUpC/NWPXaPNSId7Sg6Smpu7du/f+/fvKFZKeP39+5MiRffv2VVZW3r9/38XFRd0VbxP1duhbauXKlU+fPq1bkp2d/fz587CwMGX1FPc4kpKS9u/fX7fa2inffeS0ScbxL09jCCGWEN8hifw1MkiH9dFEOU4i21GfbZs1ikHFKjMvmBzbFx1P2hhR9a2eHaR8x44dAoEAqdW7BD9hwgSEkJub240bNz777LOVK1devny51StshoeHDxw4MCoqKjIyMjo6+qOPPpo6dWptbW1j+ycmJqrc/2vmILtdGUKcJKP8jS/nSz7yYE05yd871rylzzg1x2/pNZdeiVM/tjrzTLz5TtXJJ6Iv+nJDurLgqZL2jknF6i5V0KMTHaEWn6v98MMPNTU1AwcOVJYkJyenpaV5eXmRJLl27dq6c0LoA/V26FsqODhYZTHoO3fu8Hi8yZMnK1527dpVseHt7a0sbHt5Won00VvZNG+jrl27VkqIqJSKeS4eDe7/28x+d7u9u0o3aFAPTdRH5+WDBvVQzAHq7e0dNWOKzuvTkcvHjh3b2Dwxraa6mlxQUNCsWbNcXFyWLVu2bt269evXp6SktO7QFy9e5PP5ii45juPx8fFXr17dvn17gztv3rw5Nze3bsnJkyfd3d2vXbvWdBTFTDV9bRlX86UUCprsyZ7rr5GBoEsvVX4ZwFVcmb/xWpL0TFwsxJ+Uyz/x4YR5ckxhKQs9BqvJNSYnJ0elQ1936httauPqkc0kI8j55yoZVPTdIJPPUsr72DBWGNzDbx3TyquCD93ZvW0YCKFHb2X7Hgg3DzPVdaVaRiPtnfw3X19fkiS/+uqrXbt2kSTp7u5O6kjfvn3Hjh3bnD3f1uK99pV47Szedr9a07VS8fit9MtLFf57S768VHG3WKLl6KCZioqKbG1tdV2Lf6HRaDKZrOl9CgoK7O3tNVqNUaNG/fnnn7dv3/7ggw+uXbsWHBys0XBNaH57byMpTnyayPffW7z2ukAL4YB23C6S9IkruVUoeVQmDdhfcv6FSNc1ajFNtHfVEwgPD48lS5bEx8ffvXt3y5YtPF476N7KCFIoI1k0JCe0HdrLgv79UNNKMXHsiejrKwKcQBPd2eO6sAz1oRpgYEpKSqZMmbJ8+fJp06YFBga+ePFC1zXSOJGMLJfgJgxqiRCXE4gGl94MQl9bxq+jzD4/W4FhaNMHJiNd9PGpVO1T/XbHxsba2dmdPHnSxsaGz+c3NtOkPvj9oXDJpcrCGnzqqXIMIVMW5VRu7da0Gu3XRLGgxbkpVj8MM30rIqac4k84/nbb/Zrccrn2KwNA8yk69AcPHhw3blx76dC3RaWEmHamvJ8t89xUy1o5ufhiBa71s4KmxabV7Mz450esoAoPTXgrgbVum8eYjmEYwgmSx4Re2z9UTzStrKyGDRuWkZHh6+sbFRXVxNxz6tWKQXbh3TjXCiqiUio8zWn8WvxTH6PRruwvLlbM8jXSxCC75vC3pvtb01cO5N0tkv71Sjw7pRxDaLAjc5A9M6AzA752QN/ExsYeOHBA+x36Q4cOqdxrLCoqYrMbXfJR6VmF3IRJsfrfnAR3iqW9bBjNf54lrUQ6zImpmI11R5DZiiuC/Cq5i6keXW+b0o0TcZovw9GH7uzpZ/if9TCGyWea4/Fb2azk8k0fmJiwKAvOV2wdaRbQ7BVJDJjqNzsuLm7jxo0ikSg0NHTQoEErV66cPXu2FuqRk5OjMsjuvTPZ0SnYtiCzWUnlqfni0a6sGd2NEEJ/jNf9shNUDPWzY/SzY3wzkJdbLr9SIDn0uHZpaqWzCa2PLaOXDaOnNR0eowf6QFcdeqFQWFFRUbeEIAiykcWoimtwsZxUpOH7b6S/3Kv5fZy5qyltV6bw8OPapMmWbFpzU2DdCUYYVEwPx2FZsikHQyymnOTvyRR+0Zc7vTsscdsssfdr1g7658r8T8NNd6TXBHSGucjqJfgtW7bcunVrxowZxsbGGRkZvXv31k6CV04PotScmezKaomCapwksVo5wglE1b8z5K7mtK7mtNk9jOQEyiiV3iuWJjwRrbomoFEw3050b0u6lwXN04Jur5WFBwBQocYOfVpa2uvXr4cMGdKcqannzJmjUpKamtrYXFj5VfiivyoUc3fzGJQqKTHjTPlET/bpp6KDIRbNz+7thUROSnGSQUNVEj27f6DHYke9mycu0J4ZaK+pOVHaF9UELxQKlZNmczgcvVoeQ0VhNR5xmj/UiZXxRirDySWXKn8cbqq3V7NoFNTbhtH7f7NEva7Gs8pkj8pkhx7XPuHLq6VEFzOamxnN1ZTmbEJz4lGdTGiKGw1nX4jtuFQfKzpCSE6g39JrIv2NNPTwPYnQ03K5h/k/34pqKVklIewMovOB47hejSAjSfKNELf732BMnETlIsLqfbPhqp26OvQtnZq6RQI6M/471PSz5PJPfY33PKg5ON5i6/2anek1B0MsDK9nrPhZi/QzHu3GijjNRwjN7wULwIBWUk3wERERISEhxcXFe/fujYuL07dFrOvKLJV93tP4eYV8uDNrjp/Rd9erdPIT2Tr2XKo9lzra9Z++VJWEeFYhf1Ypf1mJn84V5Qnk+VU4k4bZc6lMKvborWyKF6e7Ff1Idi2dgk3rbmSime6pSE7OTi5f2Ns4zJNTKSamJ5aHerBn+jR3TT+9ZWRkZGZmNnLkSF1X5B1vb++IMxXzevPCPDk4gb64WGHGoihWK9YmdXXoNT019RBHZpgX58fbVf8danqnWPrkrSzK33jJxUrDy/EpL8Sf9zSe6sVBCP0+zjzmiqDuku0AtIhqgl+zZk1SUtL169cLCws3bNigtVkvWjHIbowbCyE0/HDZ1pGmTCq2/gO9W8mj+XhMSk8bhsos0HwRUViNFwvx668l8dm1ic8oOEGasymDD76R4siMRbFgU8xZFDMWxZSF8ZgUEyaFx8B4TIoxHTNhUozoGIeOcegULqO5vw4cGnZ4gsW00/xqCXnmmWigHaP9ZvffHwqDXFiKsQ5yuvHSI2mze+jXZ3ldjU87zZfIUfobabWU/GGYDkawq6tDr+mpqc++EP+ZXfv1QN6aGwJjBuX4RAsbIyqXTpmZWJ442ZJNwxKeioJdWYrL9RVi4laRVNl7bl/qfkutjah7x8CNZNB6DQwfHTt27NixYxXb1dXVml4kRuHJkycqc1M3Z7nYF5VysZz0qrd8iGGwYFMs2BRfRB/uxMrmyx+VSvePt+hry0AISXCyXESUiwm+iKgUE5USQiAhS2rwp1KySkLUSMkqKVEjJWtlRK2crJGSHBrGpGHGDIxNwxhUzIiO0SgYl4FRMGRMp1AoSFFOQYjLwEY4M/97q8rFhOZsQjuWU8uh/3NRxJiBKU8kjBmUuicVbDrG+PddAwYVsRq5OWpMp2hhtIScQB+fLj8UYk6nYNPO8IP07+fenkvdP95ifHyZLZd6OtRSJ2dp6urQt3Rq6tu3b+fn59ctKS8vb2zMza1C6errgv1jzbtZ0qkY+uVejWLGizl+Rj2s6Yox9PeKpUdzaveMMRfLyWlnyoP1778bAO17N1Xt/fv3Y2JiysvLx4wZs3DhwpiYmPz8/MzMzJKSEp3UrDlTV+7KEOZVydcNbsfn7u8lJ9CCvypkOBnhzYm5LNgRbO5v3eIOTa2MlOBkjZQUyUkpTtZISZwkq6UkQaJqKUGSqFZOynASJxFfTCQ9E5uxsMIa3MuC7mxCq5X9M9KnRkoqn8itkRJ1n84VyUgp8a8h0BI5KWlkxZYaGdGch48VvZCWftJ/1QEnpTjCEKJR/ultUClIV49QNogvInCCNGFS5vfiTvNWHS+thalqVbSlQ9+iqaljYmKeP39et+TmzZs9e/Y8ffp0/Z3fCHGxnFROHvWwTOZqSlNZN5JE6Jurguy3cpGcHOzIWN7PwB/oB4ZHE+393Rn8p59+OnHixBEjRuzfv9/X1zcyMjIiIsLZ2bnVhyYIQtE/kMvldnZ2fn5+FIp6TtzOPBP9mV27ciBvd2bN90NNRxwuiw0yUw4NMzD/d6+aJNGOYDM6BfvvUCzqbPm1aZ1aerbHoWMcOmb2vrMaKU5OSuB/5MFeFsDNE8gjzpRP9+aM6/L+p5M1QSgj5USbpvioEBNhJ/mK1TA7G1MRQjiBhDJ9mTZkzfUqey7xXaAJhqEZieUcGvaRh/b+qdXeoR8+fLhym0qlhoeHh4eHN7bzxo0bVUqWLVtmZWXV4M4qz5Q2uGwrhtAXfbijjpSxqNiiXtq46AiA/nuXFCsqKlavXo0Q8vLyOnXqlGK71VJTU+fOnWtra2tnZ4cQKioqKigo2LNnz+DBg9tUX4QQQuO6sP8ulIYm8Of6Ga+5URXmyTbU7I4Q+tzfmEX759r4EEfmhalWmruWS8Gwhb2MR7mwEEJOJrTDEywqRDp7UMeIjiHU+k9aLiLmnauY5MGx5FA+P1uhHI1lrpvuSgMCHRgfd+MorjAfCrHIKnvPDSn1UnuHvq4hQ4ZcvnxZLYdqpnIRMSOxPNSTLZSSnyaV7xtrztGnSzUA6MS7vEil/tNNtrCwaM46rU1btGhRUlJS3SUyCwoKQkNDG1sOr0WD7DCEKsWELYey7X7N5/7Gn2tm+Tg9oXJJWaPT4dEoaFSdOZwduFSHdjtEecmlyhEurMV9uAghgkDRFyqPTdT9JEh1fVJnAKMDj+rA0+o/tXo79CpwvJHbMxrz+bmKYU7MJX25BImWX65cflnwy0i9m8QGAC3T1IkvjuO2trZ1S6ytrRubqQo1NMiORqNxudzGnl2mV9UIRab2DHnyo1qP6nQWReMnmhKJhEqlNr2+p9oJhcK2d7ZapLa2ls1mY5hWz3408THD2BROLXHlCkIIuSNkb0K5cuXdl0T7/7AEQbDZ7E6dOjX/LRod/qLeDr2KGTNmtOJd5eXljbX3ly9fNt30phjTLUUyxX/3aAxVYPQrV9p0RQTau+Zo/2N22Pb+7uubl5enHF8jFAqV29XV1a047oIFC3r16jVhwgR7e3sMw4qKik6fPr1gwYLG9l+yZIlKyfbt2zdv3tzYs8vFTHs6y0j06BKn14RJb55JC7JaUckWwXEcwzB1DSNoDpIkcRzX8k9MB/mYcrmcSqVq83eNIAgqlWpvb9+id/n6+mqoPhpVf6K693Jxcdm8eXN8fHyDf33x4oWWJ93qIA2hg3zMjtvelQvHihvR6pVo8/Pzf/vtt1WrVq1atWrHjh35+fltWde2LsX/lrqO1kzR0dFbtmzRZsS8vDxHR0dtRiRJMjQ09OjRo9qMeOPGjQEDBmgzIkmS/fr1+/vvv7UZ8ciRI2FhYdqM2DQMw4z/p+62ruvVAGjvmgPtXUP0pL2/60YxmWqeHc3BwSEyMlK9xwQAqIVIJNJ1Ff6/vbsLaeqP4zh+TF1W+DQMdLhJuNxcKiOIBikkFUGEQVEadJMXBUUQRFBGJBh1YeBFEKFBu4gigqCbiTQrH+gqWWloMYJytK1E5krZltr+FwOLjP5I53dO++39utoZwuec7Xz5uO08ABBL2oPPAfyB6v/QA/jXpMeV2wEAwIpQ8AAASCgtCz4rK8tgMPz/36lK+3NmcnJyls5l0gybKYgumykH5l0cNlOQf2Tef1yLPr18/PgxdY08zUQiEYPBoPHJlNpv5qdPn4xGo5anJCWTyVAoZDKZNEtUFCUYDJaVlWl52sz8/HwkElnRebFYwrwLwrwL8o/Me7oWPAAA+IO0/IoeAAD8GQUPAICEKHgAACREwQMAICEKHgAACVHwAABIKP0K/saNGw6Ho66u7unTp6KzOjo6HA6H2Wy+evWqlumdnZ0ej0ezxL6+vi1btlRUVHR3d2sTeu7cuQ0bNlRWVt65c0eDxLm5uZs3by4tLs9SPf2XRL12JAkw76pj3jNo3vW9md1KTU5O2u32ubk5v99vs9kWFxfFZXm93s2bN8disampKYvFMjIyok368PCwwWBwu91JTbY3Eols3LhxamoqGo3abLZoNCo6dGBgwOl0xuPxUChUWFg4OzsrNHF0dLS1tbWlpSW1uDxL9fRfEvXakSTAvKsex7xn1Lyn2Sf43t7effv2rV271mq1lpaWvnr1SlzW9PT08ePH8/LySkpK6uvrA4GABulfvnw5f/58S0tLalGDxEePHjU1NZWUlBQUFIyNjeXn54sOzc7ONhgMubm5eXl5qetHCk28d+/ezMzM0uLyLNXTf0nUZUeSA/OueiLznlHznmYFHwqFysvLU4/Ly8vD4bC4rEOHDqXuZz82Nvb8+fPt27drkH7q1KlLly4ZjcbUogaJgUDg/fv3tbW1Fovl2rVrWVlZokO3bdtWVVVlMpnMZnN7e/u6deuEJl65cuXEiRNLi8uzVE//JVGXHUkOzLvqicx7Rs17mhX89+/ff76e8MLCgtC4ZDLZ1dV14MCBhw8fFhYWik6/f/++0WjcsWPH0jMabG8sFvP7/YODgy9fvuzp6RkZGREd+uTJE7/f/+zZM4/H09XVFQwGtXxbl2dpkK7xjiQN5l3dRIV5z7B51/QGO3/PZDJNTk6mHgeDQaF3LFhcXDx48GBRUdGLFy8KCgo0SHe73e/evfN6vaFQ6MGDB4lEQoPtXb9+/e7du4uLixVFaWhoePPmjehQj8dz5MgRu91ut9tdLtfg4KCWb+vyLNHp2u9I0mDe1U1UmPdMm3fNfu1XxYcPH2pqahKJRCAQsFqtQo9WuHv3bnNzsy7pp0+fTh10o0HixMTEpk2bpqenP3/+bLFYxsfHRYd2d3fv2bNn6SAUn88nOtHr9S4dArM8S0T6z4k67kjpjnlXPYt5z6h5T7NP8BaL5eTJk/X19Yqi3Lp1a9UqgT8xDA0N9fb2lpWVpRZ7enr27t2rWXqKBttrt9uPHTu2devWZDLZ1tZWXV2tKIrQ0NbWVp/P53A4FEU5e/as0+kUnfiz5S+p6Bf5X9iR0hTzrnoE855R887tYgEAkBAfHQAAkBAFDwCAhCh4AAAkRMEDACAhCh4AAAlR8AAASIiCBwBAQhQ8AAASouABAJAQBQ8AgIQoeAAAJETBAwAgIQoeAAAJUfAAAEiIggcAQEIUPAAAEqLgAQCQUI7eKwDd7N+/3+fzxePxmZmZ0tJSRVEOHz789evX69ev671qAFTGvGegrGQyqfc6QE8DAwOXL19+/PixoijxeDwejxcVFem9UgCEYN4zCl/R44f+/v6LFy/29/fv3LmztrbWbDZ3dnY2NTVVV1d3dHSk/qa9vd1qtTqdTrfbrevKAvgrzLv0+Ioev/H27dvx8fGJiQmXy/X69euKiorKysq2tra+vr7h4eHR0dFEIuFyuZxOp9Pp1HtlAfwV5l1WFDx+o7GxMT8/32azVVVVORwORVGKi4sXFhaGhobC4XBzc7OiKLFYzOfzMfBAumPeZUXB4zeys7NTD3Jzc39+fs2aNWfOnDl69KiiKLOzs6tXr9Zh5QCoinmXFb/BYwUaGxtv37797du3aDRaU1MTDof1XiMAojDv6Y5P8FiBhoaGXbt21dXVzc/PX7hwwWw2671GAERh3tMdp8kBACAhvqIHAEBCFDwAABKi4AEAkBAFDwCAhCh4AAAkRMEDACAhCh4AAAlR8AAASIiCBwBAQhQ8AAASouABAJAQBQ8AgIQoeAAAJETBAwAgIQoeAAAJ/QcEZbboguXgAQAAAABJRU5ErkJggg==" /><!-- --></p> +<p>As expected, the residual plots for Z0 and Z3 are more random than in +the case of the all SFO model for which they were shown above. In +conclusion, the model is proposed as the best-fit model for the dataset +from Appendix 7 of the FOCUS report.</p> +<p>A graphical representation of the confidence intervals can finally be +obtained.</p> <pre class="r"><code>mkinparplot(m.Z.mkin.5a)</code></pre> <p><img src="data:image/png;base64,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" /><!-- --></p> <p>The endpoints obtained with this model are</p> @@ -1809,8 +1875,8 @@ FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)</code></pre> ## 1.00000 0.53656 0.46344 1.00000 ## ## $SFORB -## Z0_b1 Z0_b2 Z3_b1 Z3_b2 -## 2.4471322 0.0075125 0.0800069 0.0000000 +## Z0_b1 Z0_b2 Z0_g Z3_b1 Z3_b2 Z3_g +## 2.4471322 0.0075125 0.9519862 0.0800069 0.0000000 0.9347820 ## ## $distimes ## DT50 DT90 DT50back DT50_Z0_b1 DT50_Z0_b2 DT50_Z3_b1 DT50_Z3_b2 @@ -1818,14 +1884,21 @@ FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)</code></pre> ## Z1 1.5148 5.0320 NA NA NA NA NA ## Z2 1.6414 5.4526 NA NA NA NA NA ## Z3 NA NA NA NA NA 8.6636 Inf</code></pre> -<p>It is clear the degradation rate of Z3 towards the end of the experiment is very low as DT50_Z3_b2 (the second Eigenvalue of the system of two differential equations representing the SFORB system for Z3, corresponding to the slower rate constant of the DFOP model) is reported to be infinity. However, this appears to be a feature of the data.</p> +<p>It is clear the degradation rate of Z3 towards the end of the +experiment is very low as DT50_Z3_b2 (the second Eigenvalue of the +system of two differential equations representing the SFORB system for +Z3, corresponding to the slower rate constant of the DFOP model) is +reported to be infinity. However, this appears to be a feature of the +data.</p> </div> <div id="references" class="section level1"> <h1>References</h1> <!-- vim: set foldmethod=syntax: --> -<div id="refs" class="references hanging-indent"> -<div id="ref-FOCUSkinetics2014"> -<p>FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> +<div id="refs" class="references csl-bib-body hanging-indent"> +<div id="ref-FOCUSkinetics2014" class="csl-entry"> +FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for +Estimating Persistence and Degradation Kinetics from Environmental Fate +Studies on Pesticides in EU Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>. </div> </div> </div> @@ -1859,7 +1932,7 @@ $(document).ready(function () { $(document).ready(function () { $('.tabset-dropdown > .nav-tabs > li').click(function () { - $(this).parent().toggleClass('nav-tabs-open') + $(this).parent().toggleClass('nav-tabs-open'); }); }); </script> @@ -1874,6 +1947,9 @@ $(document).ready(function () { <script> $(document).ready(function () { + // temporarily add toc-ignore selector to headers for the consistency with Pandoc + $('.unlisted.unnumbered').addClass('toc-ignore') + // move toc-ignore selectors from section div to header $('div.section.toc-ignore') .removeClass('toc-ignore') diff --git a/vignettes/web_only/benchmarks.R b/vignettes/web_only/benchmarks.R new file mode 100644 index 00000000..6c9b133e --- /dev/null +++ b/vignettes/web_only/benchmarks.R @@ -0,0 +1,114 @@ +## ---- include = FALSE--------------------------------------------------------- +library(knitr) +opts_chunk$set(tidy = FALSE, cache = FALSE) +library("mkin") + +## ----------------------------------------------------------------------------- +if (packageVersion("mkin") > "0.9.48.1") { + mmkin_bench <- function(models, datasets, error_model = "const") { + mmkin(models, datasets, error_model = error_model, cores = 1, quiet = TRUE) + } +} else { + mmkin_bench <- function(models, datasets, error_model = NULL) { + mmkin(models, datasets, reweight.method = error_model, cores = 1, quiet = TRUE) + } +} + +## ----include = FALSE---------------------------------------------------------- +cpu_model <- benchmarkme::get_cpu()$model_name +# Abbreviate CPU identifiers +cpu_model <- gsub("AMD ", "", cpu_model) +cpu_model <- gsub("Intel\\(R\\) Core\\(TM\\) ", "", cpu_model) +cpu_model <- gsub(" Eight-Core Processor", "", cpu_model) +cpu_model <- gsub(" CPU @ 2.50GHz", "", cpu_model) + +operating_system <- Sys.info()[["sysname"]] +mkin_version <- as.character(packageVersion("mkin")) +R_version <- paste0(R.version$major, ".", R.version$minor) +system_string <- paste0(operating_system, ", ", cpu_model, ", mkin ", mkin_version, ", R ", R_version) + +benchmark_path = normalizePath("~/git/mkin/vignettes/web_only/mkin_benchmarks.rda") +load(benchmark_path) + +# Used for reformatting the data on 2022-06-30 +# mkin_benchmarks[, "R"] <- NA +# mkin_benchmarks <- mkin_benchmarks[c(2, 1, 15, 3, 4:14)] +# mkin_benchmarks[, "CPU"] <- gsub("AMD.*", "Ryzen 7 1700", mkin_benchmarks[, "CPU"]) +# mkin_benchmarks[, "CPU"] <- gsub("Intel.*", "i7-4710MQ", mkin_benchmarks[, "CPU"]) +# rownames(mkin_benchmarks) <- gsub("AMD Ryzen 7 1700 Eight-Core Processor", "Ryzen 7 1700", rownames(mkin_benchmarks)) +# rownames(mkin_benchmarks) <- gsub("Intel\\(R\\) Core\\(TM\\) i7-4710MQ CPU @ 2.50GHz", "i7-4710MQ", rownames(mkin_benchmarks)) +# rownames(mkin_benchmarks) <- gsub(" version", "", rownames(mkin_benchmarks)) + +mkin_benchmarks[system_string, c("CPU", "OS", "mkin", "R")] <- + c(cpu_model, operating_system, mkin_version, R_version) + +## ----parent_only, warning = FALSE--------------------------------------------- +FOCUS_C <- FOCUS_2006_C +FOCUS_D <- subset(FOCUS_2006_D, value != 0) +parent_datasets <- list(FOCUS_C, FOCUS_D) + + +t1 <- system.time(mmkin_bench(c("SFO", "FOMC", "DFOP", "HS"), parent_datasets))[["elapsed"]] +t2 <- system.time(mmkin_bench(c("SFO", "FOMC", "DFOP", "HS"), parent_datasets, + error_model = "tc"))[["elapsed"]] + +## ----one_metabolite, message = FALSE------------------------------------------ +SFO_SFO <- mkinmod( + parent = mkinsub("SFO", "m1"), + m1 = mkinsub("SFO")) +FOMC_SFO <- mkinmod( + parent = mkinsub("FOMC", "m1"), + m1 = mkinsub("SFO")) +DFOP_SFO <- mkinmod( + parent = mkinsub("FOMC", "m1"), # erroneously used FOMC twice, not fixed for consistency + m1 = mkinsub("SFO")) +t3 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_D)))[["elapsed"]] +t4 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_D), + error_model = "tc"))[["elapsed"]] +t5 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_D), + error_model = "obs"))[["elapsed"]] + +## ----two_metabolites, message = FALSE----------------------------------------- +m_synth_SFO_lin <- mkinmod(parent = mkinsub("SFO", "M1"), + M1 = mkinsub("SFO", "M2"), + M2 = mkinsub("SFO"), + use_of_ff = "max", quiet = TRUE) + +m_synth_DFOP_par <- mkinmod(parent = mkinsub("DFOP", c("M1", "M2")), + M1 = mkinsub("SFO"), + M2 = mkinsub("SFO"), + use_of_ff = "max", quiet = TRUE) + +SFO_lin_a <- synthetic_data_for_UBA_2014[[1]]$data + +DFOP_par_c <- synthetic_data_for_UBA_2014[[12]]$data + +t6 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a)))[["elapsed"]] +t7 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c)))[["elapsed"]] + +t8 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a), + error_model = "tc"))[["elapsed"]] +t9 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c), + error_model = "tc"))[["elapsed"]] + +t10 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a), + error_model = "obs"))[["elapsed"]] +t11 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c), + error_model = "obs"))[["elapsed"]] + +## ----results, include = FALSE------------------------------------------------- +mkin_benchmarks[system_string, paste0("t", 1:11)] <- + c(t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, t11) +save(mkin_benchmarks, file = benchmark_path, version = 2) +# Hide rownames from kable for results section +rownames(mkin_benchmarks) <- NULL + +## ---- echo = FALSE------------------------------------------------------------ +kable(mkin_benchmarks[, c(1:4, 5:6)]) + +## ---- echo = FALSE------------------------------------------------------------ +kable(mkin_benchmarks[, c(1:4, 7:9)]) + +## ---- echo = 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) 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ke2C8vfjgV/Y9kkRQOJIn/xM9INZSpiBnqX0Q9GlQPpPKAyO5y+W5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W/N6l54qOynCqD3DpWQ+mpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE+7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv/8lbTIkkYpqKM9VOhp65ktYk+Q46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI+ejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5+7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu+vtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY/iOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v+6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ/qDQK+bfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam+WpHG+0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3+J1eCBay8IYZ0wQRKGAqvCuZ/UgbQPyllosq+XtfKIZOzmeJqRazpmmoP/76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t/I4Jktu0XSgifO2ozFOiwd/0SssJDn0dn4xqk4GDTTKX73/wQyBLdqgJ+Wx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy/qqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh/wkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml/R4yrzow1Q2A5G+kzo/RhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj/UMMc34IBqTKLDTp76WzL/dMjCxK7MjhiGjeYAC/kj/jY/Rde7hpSM1xChrog6yZ7OWTuD56xBJnGFE+pT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G+9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36/dBySis4m9/DR8izaLJW6bWCkVgm5T+ius3ZXq4xI+GnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te/r4dPYMCl5qtiHNTn+TPbh1jCBHH+dMJNhwNgs3nT+OhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF+uRIWyXjQMAs2chdpaKPNaB+kSezYt0+CA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6/TzoA1/ZBG9bIUVHLAbi/kdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh+epgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF+zkJHIA7PwCDk1gGVmGUZSSoPhNf+Tklauz98QofOlCIQ/tCD4dosHYPqtPCXB3agggQQIqQJsSkB+qn0rkQ1toJjON/OtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU+TQ6NIw3ej+AtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb++W6Uk4q6F7/rg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK+EfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l+wM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l+DMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg+EWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb+Mw76Qy29iQ5up/X7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa/xnsMYcIO/vEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz/Chp/Vlpj2P7jJQmQRwGnltkTV5dbF9fE3/fxoSqTROgq9wFUlbuYzYcasE0ouzBo+dDCDzxKAfhbAZYxQiHrLzV2iVexnDX/QnT1fsT/xuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR+CtGdkPwYN2o7DOw/VGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO/f9Qua+pDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD/prQ84B1pVGkIpVUAHCG+iz3Bn3qm2AVrYcYWhock4jso5+J7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk+UILT7+VoX5mdxxA5fS42gISQVi/HTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8/6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk/wdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt+Su9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ/BfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk/gc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B//lHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO+CvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd/p1gi/07h8qfwHrByuSxglc9cI4QIg2oqvC/qm0i7tjPLTgDhoWTAKDO2ONW5oe+/eKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2/j4ODUwRkqrtBBCrDsDpt8jhZdXoy/1BCqw3sSGhgGGy0a5Jw6BP/TExoCmNFYjZl248A0osgPyGEmRA+fAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd/ocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE/EZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ/Ugd/JZQK8lvAm43uDRAbyW8gZ+ZGq0EVerVGUKUSm/Idn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ+QATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm+oOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724+zUQ7+vkCpZB+pGA562hYQiDxHVWOq0oDQl/QsoiY+cuI7iWq/ZIBtHcXJ7kks+h2fCNUPA82BzjnqktNts+RLdk1VSu+tqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy/44XYXdI5noQoRcvjZ1RMPACRqYg2V1+OwOepcOknRLLFdYgTkT5UApt/JhLM3jeFYprZV+Zow2g8fP+U68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr/A1SY9dXFz4RjzoU9ExfJCmx/I9FKEGT3n2cmzl2X42L3Jh+AbQq6sA+Ss1kitoa4TAYgKHaoybHUDJ51oETdeI/9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB+Un44zExFE4vLytcu5NwpWrUxO/0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL+BDqMyaN3RJPh/anbx+Iv+qgQdAa3M9Z5JmvYlh4qop+Ho1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs+gs37sFvi0PPVvA5dnCBgILTwoKd/+DoL9F6inlM7H4rOTzD79KJgKlZO/Zgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno+gBoKVXgIL/VI8dB1O5o/R3Suez/gD7M781ShjKpIIORM/nxG+jjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4/QftDbEn+AucIr1oxrLabRj9q4ae0+fXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd+eNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz/6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT+mAUmiYbV3YQVqFVp9dorv+TsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp+xJyYLv1OsxQCZwTB4a8BZ/5EdxTBJthApqyfd9u3ifr/WILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj/qn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9+W8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4/YtowhEmTs0vrvlD/CrisnoBNDAcUi/teY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O/4DcgV/dZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk+tgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb/n7qwhvGnrHuf5bX6Vh/n3xffU3PeHdR+FA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E+YbfL0adwNtHP7dT7t7b46DVZIkzaRJOM+S6KcrzYVg+T3wSRFRQashjfU18NutrKa/7PXbtuJvpIjbgPeqd+pjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir/8tNXJ/OsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG+FZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx/GdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD/Qi/EmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4/asthNMK5UQKCOhU97oaOYNGsTah+jfCKsZnTRn5TbhFX8ghg8CBYt/BjeYYYUrtUZ5jVij/op7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM+3SW6Opll/wgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy+QbSKVJcqkia+GvDefFwMOmgnD7h81TUtMn+mRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d/QK7Cr4uoCeOQ7/8JfKT77KiDzLImESHw/0wf73QeHu74hxv7uihi4fTX+XEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo/oVH5ww5OzLFsiTPDns7fS6EURSSWd/92BxMYQ8sBaH+j+wthQPdVgDGpTfi+JQIWMD8xKqULliRH01rTeyF8x8q/GBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE+/7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV/yTDRRP8Y2ww5RO6d2D94o+6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt+kqdae76ViWe3STan69yaF9+fESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw/A1zEdjWquIsQXXGIVEH0thC5M+W9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF+RVmI8L4HUYk4x+67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8/p5qIQrEo/H+1l/0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud+tJUfdaZ4CWNijzZtlRa8+CkmO/EwHYfPZFU/hzjFWH7vnzHRMo+aF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce/+/9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs/GWJS6SwEN/ULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e+G2zor8l+YaMxSEomDdLHGcD6YVQPegTaA74H8+V4WvJkFUrjMLGLlvSZQWvi8/QA7yzQ8GPno//5SJHRP/OqKObPCo81s/+6WgLqykYpGAgQZhVDEBPXWgU/WzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M+GWn6ASobIWC+LbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg/kpf3+CnAXKiMgIE8Jk/Mhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo+RJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B+SkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE+VMd3b1fhCynD0pQNhCG6/WCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp/YbHjo++7/Wj8S4YNa+ZdqAw1hDrKWFXv9+zaXpf8ZTDSbiqsxnwN/CzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m/NCW/HILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO+5EJ7Z6bCiRoPedRZ/P0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn/LvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl+11PoFYnNv2HwAODeNRkHj+8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij+bsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur/eWHjiB7SOVdqMAVmpBvfRiebsFjger7DC+8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l+kXRZ0KLZaGsFSIdQz/HXDxf3/TE30+DgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH+RptvRMVRaahu4cySjS3P5wxAUCPkmn+rhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik/zbrTQk5KmgxCg/f45L0jywebOWUYFJQAJia7XzCV0x89rpp/f3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl+5tfUWZNwBpEPXghzbBggYCw/dhy0ntds2yeHCDKkF/YxQjNIL/F/37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD+qNOhwMlfARQUdJ2tUX+MNJqOwIciWalZsmEjt07tfa8ma4cji9sqz+Q9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe+jGDiNJQLWnfx+drTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf/bkvo8PLVBsZl152y5S8+HRDfZIMCbYZ1WDp4yrdchOJw8k6R+/2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB+M4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5/iYp3ZdrCf7fL+en+sIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv+NRiJc8JAKqqgCA/PNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN/hCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj+bYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5/zBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8/i+jHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2+JrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk+Mz7wwstg6RFZb+TZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm/7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk+9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx+whVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC+XYuqMBMUun5YezKbRKmEPwuK+CLzijPEQgfhahQswBBLfg/GBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX+RCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk/4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug/AbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM+Zu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX/pcsfwXbLze2+D+u33OGBoJyAAL3jn3RuEcdp5If8O+a4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT++tm+haBCikRUUMrMhYKZJKYoVuv/bsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV/DMUxd9uFZmBfKXMCn/SqkWJyKPnT6lq+4zBZni6fYRByJn6OK+OgPBGRAJluwGSk4wxjOOzyce/PKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO/cEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm+bddRQu5F9s0XftGEJ9JSuSk+ZachCbdU45fEqbugzTIUokwoAKvpUQF/CvLbWW5BNQFqFkJg2f30E/48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J+1pT0tLkvFVZaNzfhs/Kd3+A9YsImlO4XK4vpCo/elHQi/9gkFg07xxnuXLt21unCIpDV+bbRxb7FC6nWYTsMFF8+1LUg4JFjVt3vqbuhHmDKbgQ4e+RGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB/KVijd1ARWkFEf3yiUw1v/WaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G+ZManTqDLPjyrOse7WiiwOJCG+J0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj/6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j/N46f+S2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb/0jQbaHJ2YRK8A+ls6WMhWmpCXYG5jqapGs5/eOJErxi2/2KWVHiPellTgh/fNl/2KYPKb7DUcAg+mCOPQFCiU9Mq/WLcU1xxC8aLePFZZlE+PCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh/nFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW+oKFLvpyvTBMM69tN1Ydwv1LIEhHsC+ueVG+w+kyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw+H/AuOx+aH+tBL88H57D0MsqyiZxhOEQkF/8DR1d2hSPMj/sNOa5rxcUnBgH8ictv2J+cb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd/PjMMtQfyDNZsOPd6XcAsnBE/mRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl/XPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG/VanIvcwycVA7+BE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP/MVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX/5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c/F1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J/5wkjpkre727p5PTRX5FGrSBIfJqhJE/IS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug+oRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U/5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7/BQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN+nA7pvF78/RII5ZHA09OAiE/66MF6HQ+qVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe+hXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz+JV/4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB+K3wBP/ineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q/9uocGsx41O4IZhViw/2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY/cQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE/om7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi/caHSM3FPRGRf7dB7YC+cD2ho6oL2zGDCkjlf/DFoQVl8GS/56wur3rdV6ggtzZW60MRB3g+U1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg/gBQIZMG/YcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK/G7F3mUc3GOAKqh60zM0v34v+ELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND+/GTk6M56Ig4yMsU6LUW1EWE+fIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP/IoRIZ/F6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg/RrPD/d3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl+Mu4xf0ezqeXD2PtPDnwMPo86sbwDV+9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD/OwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d/UfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH+14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC+OA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6+vAUUBoGhY3CT2tgwehdPqU/4Q7ZLYvhRl/ogOvR9O2+wkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn/dFSWBWzQ/VYk+Gezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n/yJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET/Hh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j+DXfpi12m1RbzYLg9J2wFergEwOxFyD0/JstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT/cUP6pE/mujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB+HEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu+vqQ02+KpJBjaLt9ye1Ab+BbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC+UwkXOoAjneU/xHplMQo2cXUlrVNqJYczgYlaOEczVCs/OCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38+xsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9+b8fH6+b8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj/0Q7PFUcC8hDrxESWdfgFRm+7vvWbkEppHB4T/1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y+g3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR/Rrs/JLKXgEx+qkmeDlFOD1/yTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW+xz+5FElFxWB28VjYIGZ0Yd+5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ+lT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ+2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1+JpI9psyNYIFuJogZa0/1AhOWdlHQxdAgbwacsHqPZo8u/ngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2+RdM+MAaYaZ0Y/ADkbNCZuAyAVQa2OcXOeICmDn9Q/eFkDeFQg5MgHEDXq/tVjj+jtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2/Bc0UxvseQCO2pQ2i+Krfhu/WeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt/U0Wf+phiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7+ExseJauyqo30hs+1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j/e1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la/QCTiVelFnU6O/GCvykqS/wZJDhKN9gBtSOp/1SP5VRgJcoVj+kmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn+8p6+vBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H+gDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D/GvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P+jAgN5TB3haSifDcApp6yymEi6Ij/GsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x/pChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11/yxyw0UnGig3MFdZklN5FI/qiT65T+jOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5+bqWiAYiAv6Jsf79/VUs4cIl+n6+WOjcgB/2l5TreoAV2717JzZbQIR0W1cl/dEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW/PhoawJDrGAP0JYWHgAVUByo/bGdiv2T2EMg8gsS14/rAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq+SCcTSx5NDtbFwNlh8VhjGGDu7JG5/TAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9+ogD8Kk/W7QoRN1UWPqM4+xdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c+4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS+o3F8YVVeikw13w+OEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX++7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9/O53DYi/5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD+P8sNh6e+XYHJXT/lkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp+pT0KYV5dGtknU64JhabdqcVQbG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yr6EIj+LzNj/mfR2vhhRlx0BILZoAYruF0caWQ7YxO66UmeguDREAFHYuC7HJviRgVO6ruJH59h/C/PkgSle8xNzZJULLWq9JMDTE2fjGE146a1Us6PZDGYle6ldWRqn/pdpgHKNGrGIdkRK+KPETT9nKT6kLyDI8xd9A1FgWmXWRAIHwZ37WyZHOVyCadJEmMVz0MadMjDrPho+EIochkVC2xgGiwwsQ6DMv2P7UXqT4x7CdcYGId2BJQQa85EQKmCmwcRejQ9Bm4oATENFPkxPXILHpMPUyWTI5rjNOsIlmEeMbcOCEqInpXACYQ9DDxmFo9vcmsDblcMtg4tqBerNngkIKaFJmrQAPnq1dEzsMXcwjcHdfdCibcAxxA+q/j9m3LM/O7WJka4tSidVCjsvo2lQ/2ewyoYyXwAYyr2PlRoR5MpgVmSUIrM3PQxXPbgjBOaDQFIyFMJvx3Pc5RSYj12ySVF9fwFPQu2e2KWVoL9q3Ayv3IzpGHUdvdPdrNUdicjsTQ2ISy7QU3DrEytIjvbzJnAkmANXjAFERA0MUoPF3/5KFmW14bBNOhwircYgMqoDpUMcDtCmBE82QM2YtdjVLB4kBuKho/bcwQdeboqfQartuU3CsCf+cXkgYAqp/0Ee3RorAZt0AvvOCSI4JICIlGlsV0bsSid/NIEALAAzb6HAgyWHBps6xAOwkJIGcB82CxRQq4sJf3FzA70A+TRqcqjEMETCoez3mkPcpnoALs0ugJY8kQwrC+JE5ik3w9rzrvDRjAQnqgEVvdGrNwlanR0SOKWzxOJOvLJhcd8Cl4AshACUkv9czdMkJCVQSQhp6kp7StAlpVRpK0t0SW6LHeBJnE2QchB5Ccu8kxRghZXGIgZIiSj7gEKMJDClcnX6hgoqJMwiQDigIXg3ioFLCgDgjPtYHYpsF5EiA4kcnN18MZtOrY866dEQAb0FB34OGKHGZQjwW/WDHA60cYFaI/PjpzquUqdaYGcIq+mLez3WLFFCtNBN2QJcrlcoELgiPku5R5dSlJFaCEqEZle1AQzAKC+1SotMcBNyQUFuRHRF6OlimSBgjZeTBCwLyc6A+P/oFRchXTz5ADknYJHxzrJ5pGuIKRQISU6WyKTBBjD8WozmVYWIsto1AS5rxzKlvJu4E/vwOiKxRtCWsDM+eTHUrmwrCK5BIfMzGkD+0Fk5LzBs0jMYXktNDblB06LMNJ09U8pzSLmo14MS0OMjcdrZ31pyQqxJJpRImlSvfYAK8inkYU52QY2FPEVsjoWewpwhRp5yAuNpkqhdb7ku9Seefl2D0B8SMTFD90xi4CSOwwZy9IKkpMtI3FmFUg3/kFutpQGNc3pCR7gvC4sgwbupDu3DyEN+W6YGLNM21jpB49irxy9BSlHrVDlnihGKHwPrbVFtc+h1rVQKZduxIyojccZIIcOCmhEnC7UkY68WXKQgLi2JCDQkQWJRQuk60hZp0D3rtCTINSeY9Ej2kIKYfGxwOs4j9qMM7fYZiipzgcf7TamnehqdhsiMiCawXnz4xAbyCkLAx5EGbo3Ax1u3dUIKnTxIaxwQTHehPl3V491H0+bC5zgpGz7Io+mjdhKlPJ01EeMpM7UsRJMi1nGjmJg35i6bQBAAxjO/ENJubU2mg3ONySEoWklCwdABETcs7ck3jgiuU9pcKKpbgn+3YlzV1FzIkB6pmEDOSSyDfPPlQskznctFji0kpgZjW5RZe6x9kYT4KJcXg0bNiCyif+pZACCyRMmYsfiKmN9tSO65F0R2OO6ytlEhY5Sj6uRKfFxw0ijJaAx/k3QgnAFSq27/2i4GEBA+UvTJKK/9eISNvG46Em5RZfjTYLdeD8kdXHyrwId/DQZUaMCY4gGbke2C8vfjgV/Y9kkRQOJIn/xM9INZSpiBnqX0Q9GlQPpPKAyO5y+W5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W/N6l54qOynCqD3DpWQ+mpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE+7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv/8lbTIkkYpqKM9VOhp65ktYk+Q46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI+ejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5+7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu+vtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY/iOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v+6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ/qDQK+bfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam+WpHG+0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3+J1eCBay8IYZ0wQRKGAqvCuZ/UgbQPyllosq+XtfKIZOzmeJqRazpmmoP/76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t/I4Jktu0XSgifO2ozFOiwd/0SssJDn0dn4xqk4GDTTKX73/wQyBLdqgJ+Wx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy/qqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh/wkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml/R4yrzow1Q2A5G+kzo/RhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj/UMMc34IBqTKLDTp76WzL/dMjCxK7MjhiGjeYAC/kj/jY/Rde7hpSM1xChrog6yZ7OWTuD56xBJnGFE+pT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G+9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36/dBySis4m9/DR8izaLJW6bWCkVgm5T+ius3ZXq4xI+GnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te/r4dPYMCl5qtiHNTn+TPbh1jCBHH+dMJNhwNgs3nT+OhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF+uRIWyXjQMAs2chdpaKPNaB+kSezYt0+CA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6/TzoA1/ZBG9bIUVHLAbi/kdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh+epgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF+zkJHIA7PwCDk1gGVmGUZSSoPhNf+Tklauz98QofOlCIQ/tCD4dosHYPqtPCXB3agggQQIqQJsSkB+qn0rkQ1toJjON/OtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU+TQ6NIw3ej+AtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb++W6Uk4q6F7/rg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK+EfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l+wM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l+DMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg+EWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb+Mw76Qy29iQ5up/X7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa/xnsMYcIO/vEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz/Chp/Vlpj2P7jJQmQRwGnltkTV5dbF9fE3/fxoSqTROgq9wFUlbuYzYcasE0ouzBo+dDCDzxKAfhbAZYxQiHrLzV2iVexnDX/QnT1fsT/xuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR+CtGdkPwYN2o7DOw/VGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO/f9Qua+pDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD/prQ84B1pVGkIpVUAHCG+iz3Bn3qm2AVrYcYWhock4jso5+J7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk+UILT7+VoX5mdxxA5fS42gISQVi/HTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8/6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk/wdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt+Su9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ/BfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk/gc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B//lHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO+CvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd/p1gi/07h8qfwHrByuSxglc9cI4QIg2oqvC/qm0i7tjPLTgDhoWTAKDO2ONW5oe+/eKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2/j4ODUwRkqrtBBCrDsDpt8jhZdXoy/1BCqw3sSGhgGGy0a5Jw6BP/TExoCmNFYjZl248A0osgPyGEmRA+fAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd/ocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE/EZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ/Ugd/JZQK8lvAm43uDRAbyW8gZ+ZGq0EVerVGUKUSm/Idn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ+QATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm+oOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724+zUQ7+vkCpZB+pGA562hYQiDxHVWOq0oDQl/QsoiY+cuI7iWq/ZIBtHcXJ7kks+h2fCNUPA82BzjnqktNts+RLdk1VSu+tqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy/44XYXdI5noQoRcvjZ1RMPACRqYg2V1+OwOepcOknRLLFdYgTkT5UApt/JhLM3jeFYprZV+Zow2g8fP+U68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr/A1SY9dXFz4RjzoU9ExfJCmx/I9FKEGT3n2cmzl2X42L3Jh+AbQq6sA+Ss1kitoa4TAYgKHaoybHUDJ51oETdeI/9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB+Un44zExFE4vLytcu5NwpWrUxO/0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL+BDqMyaN3RJPh/anbx+Iv+qgQdAa3M9Z5JmvYlh4qop+Ho1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs+gs37sFvi0PPVvA5dnCBgILTwoKd/+DoL9F6inlM7H4rOTzD79KJgKlZO/Zgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno+gBoKVXgIL/VI8dB1O5o/R3Suez/gD7M781ShjKpIIORM/nxG+jjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4/QftDbEn+AucIr1oxrLabRj9q4ae0+fXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd+eNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz/6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT+mAUmiYbV3YQVqFVp9dorv+TsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp+xJyYLv1OsxQCZwTB4a8BZ/5EdxTBJthApqyfd9u3ifr/WILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj/qn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9+W8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4/YtowhEmTs0vrvlD/CrisnoBNDAcUi/teY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O/4DcgV/dZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk+tgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb/n7qwhvGnrHuf5bX6Vh/n3xffU3PeHdR+FA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E+YbfL0adwNtHP7dT7t7b46DVZIkzaRJOM+S6KcrzYVg+T3wSRFRQashjfU18NutrKa/7PXbtuJvpIjbgPeqd+pjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir/8tNXJ/OsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG+FZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx/GdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD/Qi/EmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4/asthNMK5UQKCOhU97oaOYNGsTah+jfCKsZnTRn5TbhFX8ghg8CBYt/BjeYYYUrtUZ5jVij/op7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM+3SW6Opll/wgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy+QbSKVJcqkia+GvDefFwMOmgnD7h81TUtMn+mRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d/QK7Cr4uoCeOQ7/8JfKT77KiDzLImESHw/0wf73QeHu74hxv7uihi4fTX+XEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo/oVH5ww5OzLFsiTPDns7fS6EURSSWd/92BxMYQ8sBaH+j+wthQPdVgDGpTfi+JQIWMD8xKqULliRH01rTeyF8x8q/GBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE+/7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV/yTDRRP8Y2ww5RO6d2D94o+6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt+kqdae76ViWe3STan69yaF9+fESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw/A1zEdjWquIsQXXGIVEH0thC5M+W9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF+RVmI8L4HUYk4x+67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8/p5qIQrEo/H+1l/0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud+tJUfdaZ4CWNijzZtlRa8+CkmO/EwHYfPZFU/hzjFWH7vnzHRMo+aF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce/+/9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs/GWJS6SwEN/ULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e+G2zor8l+YaMxSEomDdLHGcD6YVQPegTaA74H8+V4WvJkFUrjMLGLlvSZQWvi8/QA7yzQ8GPno//5SJHRP/OqKObPCo81s/+6WgLqykYpGAgQZhVDEBPXWgU/WzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M+GWn6ASobIWC+LbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg/kpf3+CnAXKiMgIE8Jk/Mhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo+RJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B+SkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE+VMd3b1fhCynD0pQNhCG6/WCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp/YbHjo++7/Wj8S4YNa+ZdqAw1hDrKWFXv9+zaXpf8ZTDSbiqsxnwN/CzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m/NCW/HILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO+5EJ7Z6bCiRoPedRZ/P0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn/LvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl+11PoFYnNv2HwAODeNRkHj+8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij+bsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur/eWHjiB7SOVdqMAVmpBvfRiebsFjger7DC+8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l+kXRZ0KLZaGsFSIdQz/HXDxf3/TE30+DgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH+RptvRMVRaahu4cySjS3P5wxAUCPkmn+rhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik/zbrTQk5KmgxCg/f45L0jywebOWUYFJQAJia7XzCV0x89rpp/f3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl+5tfUWZNwBpEPXghzbBggYCw/dhy0ntds2yeHCDKkF/YxQjNIL/F/37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD+qNOhwMlfARQUdJ2tUX+MNJqOwIciWalZsmEjt07tfa8ma4cji9sqz+Q9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe+jGDiNJQLWnfx+drTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf/bkvo8PLVBsZl152y5S8+HRDfZIMCbYZ1WDp4yrdchOJw8k6R+/2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB+M4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5/iYp3ZdrCf7fL+en+sIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv+NRiJc8JAKqqgCA/PNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN/hCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj+bYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5/zBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8/i+jHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2+JrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk+Mz7wwstg6RFZb+TZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm/7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk+9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx+whVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC+XYuqMBMUun5YezKbRKmEPwuK+CLzijPEQgfhahQswBBLfg/GBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX+RCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk/4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug/AbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM+Zu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX/pcsfwXbLze2+D+u33OGBoJyAAL3jn3RuEcdp5If8O+a4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT++tm+haBCikRUUMrMhYKZJKYoVuv/bsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV/DMUxd9uFZmBfKXMCn/SqkWJyKPnT6lq+4zBZni6fYRByJn6OK+OgPBGRAJluwGSk4wxjOOzyce/PKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO/cEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm+bddRQu5F9s0XftGEJ9JSuSk+ZachCbdU45fEqbugzTIUokwoAKvpUQF/CvLbWW5BNQFqFkJg2f30E/48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J+1pT0tLkvFVZaNzfhs/Kd3+A9YsImlO4XK4vpCo/elHQi/9gkFg07xxnuXLt21unCIpDV+bbRxb7FC6nWYTsMFF8+1LUg4JFjVt3vqbuhHmDKbgQ4e+RGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB/KVijd1ARWkFEf3yiUw1v/WaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G+ZManTqDLPjyrOse7WiiwOJCG+J0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj/6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j/N46f+S2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb/0jQbaHJ2YRK8A+ls6WMhWmpCXYG5jqapGs5/eOJErxi2/2KWVHiPellTgh/fNl/2KYPKb7DUcAg+mCOPQFCiU9Mq/WLcU1xxC8aLePFZZlE+PCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh/nFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW+oKFLvpyvTBMM69tN1Ydwv1LIEhHsC+ueVG+w+kyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw+H/AuOx+aH+tBL88H57D0MsqyiZxhOEQkF/8DR1d2hSPMj/sNOa5rxcUnBgH8ictv2J+cb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd/PjMMtQfyDNZsOPd6XcAsnBE/mRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl/XPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG/VanIvcwycVA7+BE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP/MVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX/5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c/F1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J/5wkjpkre727p5PTRX5FGrSBIfJqhJE/IS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug+oRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U/5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7/BQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN+nA7pvF78/RII5ZHA09OAiE/66MF6HQ+qVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe+hXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz+JV/4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB+K3wBP/ineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q/9uocGsx41O4IZhViw/2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY/cQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE/om7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi/caHSM3FPRGRf7dB7YC+cD2ho6oL2zGDCkjlf/DFoQVl8GS/56wur3rdV6ggtzZW60MRB3g+U1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg/gBQIZMG/YcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK/G7F3mUc3GOAKqh60zM0v34v+ELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND+/GTk6M56Ig4yMsU6LUW1EWE+fIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP/IoRIZ/F6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg/RrPD/d3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl+Mu4xf0ezqeXD2PtPDnwMPo86sbwDV+9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD/OwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d/UfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH+14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC+OA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6+vAUUBoGhY3CT2tgwehdPqU/4Q7ZLYvhRl/ogOvR9O2+wkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn/dFSWBWzQ/VYk+Gezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n/yJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET/Hh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j+DXfpi12m1RbzYLg9J2wFergEwOxFyD0/JstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT/cUP6pE/mujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB+HEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu+vqQ02+KpJBjaLt9ye1Ab+BbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC+UwkXOoAjneU/xHplMQo2cXUlrVNqJYczgYlaOEczVCs/OCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38+xsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9+b8fH6+b8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj/0Q7PFUcC8hDrxESWdfgFRm+7vvWbkEppHB4T/1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y+g3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR/Rrs/JLKXgEx+qkmeDlFOD1/yTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW+xz+5FElFxWB28VjYIGZ0Yd+5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ+lT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ+2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1+JpI9psyNYIFuJogZa0/1AhOWdlHQxdAgbwacsHqPZo8u/ngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2+RdM+MAaYaZ0Y/ADkbNCZuAyAVQa2OcXOeICmDn9Q/eFkDeFQg5MgHEDXq/tVjj+jtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2/Bc0UxvseQCO2pQ2i+Krfhu/WeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt/U0Wf+phiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7+ExseJauyqo30hs+1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j/e1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la/QCTiVelFnU6O/GCvykqS/wZJDhKN9gBtSOp/1SP5VRgJcoVj+kmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn+8p6+vBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H+gDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D/GvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P+jAgN5TB3haSifDcApp6yymEi6Ij/GsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x/pChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11/yxyw0UnGig3MFdZklN5FI/qiT65T+jOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5+bqWiAYiAv6Jsf79/VUs4cIl+n6+WOjcgB/2l5TreoAV2717JzZbQIR0W1cl/dEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW/PhoawJDrGAP0JYWHgAVUByo/bGdiv2T2EMg8gsS14/rAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq+SCcTSx5NDtbFwNlh8VhjGGDu7JG5/TAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9+ogD8Kk/W7QoRN1UWPqM4+xdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c+4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS+o3F8YVVeikw13w+OEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX++7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9/O53DYi/5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD+P8sNh6e+XYHJXT/lkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp+pT0KYV5dGtknU64JhabdqcVQbGZiAxQAnvN1u70y1AnmvOSPgLI6uB4AuDGhmAu3ATkJSw7OtS/2ToPjqkaq62/7WFG8advGlRRqxB9diP07JrXowKR9tpRa+jGJ91zxNTT1h8I2PcSfoUPtd7NejVoH03EUcqSBuFZPkMZhegHyo2ZAITovmm3zAIdGFWxoNNORiMRShgwdYwFzkPw5PA4a5MIIQpmq+nsp3YMuXt/GkXxLx/P6+ZJS0lFyz4MunC3eWSGE8xlCQrKvhKUPXr0hjpAN9ZK4PfEDrPMfMbGNWcHDzjA7ngMxTPnT7GMHar+gMQQ3NwHCv4zH4BIMYvzsdiERi6gebRmerTsVwZJTRsL8dkZgxgRxmpbgRcud+YlCIRpPwHShlUSwuipZnx9QCsEWziVazdDeKSYU5CF7UVPAhLer3CgJOQXl/zh575R5rsrmRnKAzq4POFdgbYBuEviM4+LVC15ssLNFghbTtHWerS1hDt5s4qkLUha/qpZXhWh1C6lTQAqCNQnaDjS7UGFBC6wTu8yFnKJnExCnAs3Ok9yj5KpfZESQ4lTy5pTGTnkAUpxI+yjEldJfSo4y0QhG4i4IwkRFGcjWY8+EzgYYJUK7BXQksLxAww/YYWBMhJILB9e8ePEJ4OP7z+4/wOQDl64iOYDp26DaONPxpKtBxq/aTzRGarm3VkPYTLJKx6Z/Mw2YbBGseJhPMwhhNswrIkyvV2BYzrvZbxLpKwcWJhYmFtVZ+lPEq91FzVp1HlQY1bZVLqeNR9SAUn6n0E28k/UuGkNpP1DBI5ch/EehZfjUQ9aE41NhETExoPT2gGQz0IhWJbEOvTQ4wgcXCHHFBhewYUiFHuhRSAUVmEHeCRQHQkXGFwkAgyzREJCVN7TRnTon36Zw3tPhx4EALwNdwDv+J41YSP4B2CQqz0EFgARZ4ESgBHQgROwAVn9GTI+HYexTUevLUeta4/DqKrbMVS+Yqb8hUwYCrlgKtmAq1YCrFgKrd4qpXiqZcKn1oqdWipjYKpWwVPVYqW6xUpVipKqFR3QKjagVEtAqHpxUMTitsnFaJOKx2cVhswq35RVpyiq9lFVNIKnOQVMkgqtYxVNxiqQjFS7GKlSIVIsQqPIhUWwioigFQ++KkN8VHr49HDw9Ebo9EDo9DTo9Crg9BDg9/Wx7gWx7YWwlobYrOGxWPNisAaAHEyALpkAVDIAeWAArsABVXACYuAD5cAF6wAKFQAQqgAbVAAsoAAlQAUaYAfkwAvogBWQACOgAD9AAHSAAKT4GUdMiOvFngBTwCn2AZ7Dv6B6k/90B8+yRnkV144AIBoAMTQATGgAjNAA4YABgwABZgB/mQCwyAVlwCguASlwCEuAQFwB4uAMlwBYuAJlQAUVAAhUD2KgdpUDaJgaRMDFJgX5MC1JgWJEAokQCWRAHxEAWkQBMRADpEAMkQAYROAEecC484DRpwBDTnwNOdw05tjTmiNOYwtswhYFwLA7BYG4LA2BYGOLAwRYFuLAsxYFQJAohIEyJAMwkAwiQC0JAJgkAeiQBkJAFokAPCQA0JABwcD4Dgc4cDdDgaYcDIDgYgUC6CgWgUClCgUYUAVBQBOFAEYMALgwAgDA9QYAdIn8AZzeBB2L5EcWrenUT1KXienEsuJJ7x5U8XlTjc1NVzUyXFTGb1LlpUtWlTDIjqwE4LsagowoCi2gJLKAkpoBgJQNpAIhNqaEoneI6kiiqQ6Go/n6j0cS+a2gEU8gIHJ+BwfgZX4GL+Bd/gW34FZ+BS/gUH4FN6BTegTvoEv6BJegRnYEF2A79gOvYDl2BdEjCkqkGtwXp0LNToIskOTXzh/F062yJ7AAAAEDAWAAABWhJ+KPEIJgBFxMVP7w2QJBGHASQnOBKXKFIdUK4igKA9IEaYJg) 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g5NLLIEXi8otcDQcUts0D8eQ1iVDRAMBTsYiNdRIxE09EIBJO9A2xqgERTaW86BUFn0OD2xFO97FAgFhF6OoQ7prYt4XwSeUgQHiJyDbeke9IdQntciLQ1FlJMaYcUNvZBg+FB1ubjlnRNvl3o6IEU2w7fdNPhm/hh+FLysUu6++DLHkOkrSHYEjH0tEPe7WdD3uyDgvAgK/m4szFFR7ch0toUgBTdWHr7EpaWru6+6dmbbnqWEbV2EtxAsXiZAPTtGPSbHsotI2leoM8TePEqgSQprs7AGFf8kuOkPdZPXGb55POAW1d/jLST9v5YflasP6v/CO7+GNAPC2BMZWmsOjp2NNbfHwMCJD+LPVL+D/OYlWEEI/9jpPddOFkB5d1GSuKZYggmCCd7JUxD7EXAzxyirYnNDLdDZoFdx14kivkvGc3579Jm36reTTvDgBnaO6vzyQ6chQmlsMoIkIQ2+bBDWBud1Va4pcCn8CPqxlh/fgtG8IPaPH8C5wk6/nZDv69jurV5QhtwE0x2iqOsj9Mx8B9/0EaUdiPfOYYDCi/q9jhWRuupMDEU0+CtX0sDFxv07T/K5niBPqN9+tQjgEc31NGCXFeMcCEuQBIc/BK4CO78u7EPYvl3yaEfK3vcb6qP1R2tI7vUjVDDUdKubsSrNjYKY1qBEa2P50SJoaXiksIoLiCwnxS6EBuBde87botNfdEWwYvF/R0/u5yCqhGeEOR2ynSeyXjt6ka7neyye8kryBSWE52y+RBgogrXPZ8E1yIHoHIFUM+AbJhE7lbMtt8ApL+xmZW7PwbjAO0fAVoXQOuiSP/ksIVdFZ0aulsamKUzwPZ/NYDMJRBPCxsBqLzqHyneXF6Ej9HlIFo7+pg+jUb3unRmGpstGkm6etOuDBGA5wCMefp1gTHcdZlvPBXlOslvYTp1cd8UjYLVd/J5awNrIOKLnIt9MD9qdrKrWCvA6ALm3QV9VrsPm60Q7+RHJHP+2hqfugo/MvI2H/mqr4b9tFnKSRY1Y5Ek80Nm/WIhr1ikKnxGz9TWXrokf9xwujfvcOTtNTWnxd0F37Y2W79tteBqZ4G5qLCuomw+nSr28QESCRVLTyYKILGJOPfcnaIFOsewhRdvv+rWa/Wih0vlbX6Zb75T5C0qNKVFvH1QL/vazSWgC2s6oWXXIuUxQelKiJbowuJDQViatLmLijg9CQBMg8WiPgiw3LEeYRmm5f+XdnvkDnxLLjMLxtvX74C3OlwPQqx4xwIdpPx38LrlDphiyWUWHWKAzzxurS/xTo+P5wGFak62ap1PVFFN4v/y+xuR39WnIO7lsWfwgVsK17wxrs9K8ltIKuhkw7f/6dhK6gQokFKhWX3urrjk/rnI0pgfpGMeuQIUaEM7+GF5q2iMkCaMQwxxOzcvU0eXbsnS9XknXvP7Gtw5dwPXlFu2ecvSHEZgNDsU6x/GdXBYXyOQjzZReSedeEPY6nEv9gJR4oBQJtFO6Kd0fwC6BO4LNHDeBujB6dSNcUQC9zIv2LnAzGk99bUDrdFY+9yGFQtEo0GQPNv6vS2drj4+1jHbv3aJSMUWP+QTZrmbNTjU8wyG/iXNNpskybLcJ3CiTF5Ir+JYzmJwE0mSVhlxbtbmvweB3ulB6Til5UuUZydpgiFVeobhU0WaBqpJ198d+/XeNRTZ9/1OPfG7+2hwzd5W3D+hmyjsRcUg/+Cavb++Vh2ls3L7zT/etOnHNxeerv313vzLVqPai4nJv+K1FC6040/4udw7sAb3laSg0XCkAAs0npBO6VJabS4Elk/U+D4gTXW+j0wnrMlqNamq4tMIYB87tE10i0FR3LZNhJsb7/R561btmes8YBCRkhYNByRtKd55mqTas9FYhJnbRGHuOh3M4QTdgQSqmgRxuzGdSvZGcbMxNQGk5C3ebLjoXIOFM4l+WKHmLTJwRv9E8GWJ6dYvf/FmEyEGr+gyrr1p5zrgkz0Cw2j94Hv8Jdx7dIVegBSNtgsqGsRQEYiIBoXwD0LNvQ5d7s5Z00QzwNhqZA0b+tMG1tQq5nd84uq8R0zPvX35G8uRaze4jcOHzz0w1+Q2BIRvf6J6Kgatnrbiem+CFvAxfkrndzD9MFPP1GWTUHclpASUkCNAQkpCCcCgDSUDAhDZ+CuEkgn8J7i9nMA7pA4lISappxILKfAeSAbIcSDuN2bJcfZILqeO5rLs0MnngSHYRdrHjmaz7JEsEPw51ZqDJDmUIOZIe34WaQeegNsJn1qz8AIpT3yCjyEih/xELkuJ0lEMYTLVCiWpo5oYMleMH6USyYJcD+uOe+kWKpn1Qns34iyYDjkSLvgnZXcgVQNeqINXr48m3iS7cjm8tedyY0f1QvTnHHdsrKby/+SSbPY8/NH6vpl/Esq3Ae4ZU1HC44KFiI9o7CEgab/RqHbj7s5KAg06s39ZP/zxI/mVuF/TbTSy+3Fb8If9/cv7+wt91yy8RfP1QXtW5RzQn7qIiZyuFM5QfJ5E9uVnqT85TanFx0lkP3ukBAMprvsRyi/C8NAJL1xbIIirSvnSj4O5netb4JxmNANHPssHAcHMHsFRgEug816gDBeMbdfiuRcghqYcm0+Xxx/5IAEtN3fqFF3LzAXqwoT0PN0OVTNqxo8sxMkd5Ig6k79Zk7VxxX6gMLOZFQgvpW2RrMW1D0BDihaXQ9wVRoBxPLfpknmkeMtoB/qM9cRc9IqmMD2XUmdZ7GSRKPUZvChf8BoykriM2MnKYbOHX8R7cLdNCxSFFVQqoYswnlWtlFS2mNkhswVpZiQW1J/UKFfipHGlUkM6UKBhMz1istELIHJLMSctu3ugzfaVSOjKvUgc/THK4Sdg2Wscz69leKIkkrwuuWiOe9yGYKQXRumkC3qbRcMwrvhjNXgdZk3RxAUEhuSPvn3nnd++U/3vlVOmrJzCD8JLxV1OHRjrZifbcFDOuRNTGqdgQm1tSNJ2OcQ04YiEXuxtII1ECSQRoQGYioEsgCfchB4ghAtw7FfJre4WZ9hkVi9MtjuWqtdNDlpMrfEG9fOT6q21okg+e4As38MfGquNt7oUws6Ysarj1/efE+yst86YUVNvDdts3Pv5c8m/aP0C+f8/Qb+IMnGq09BgwN01oIOAnAdagI8mBSrqk1gxTDUBOtk2ousEtBH2z4Ir2d3f6k8PXXVlt2qN9RODxRuoJT/v27wm09jRYVc/e++iyx2tyzJb/n3J0htXP87eSsQaf2Ly0s6Zmxela88REy1cf4273mI3iXNJ7KxrZibOm9xm6rl4fqy/t27smU8tOfdW2ucBzg2UfmOIVyLIl3kpYlwphDISTXJXsctmiDtN7fNV6zelgxwnWxsVr83Aj/S5ki1jL/a0GC6+2L6Um+aoddlNFuj+bJ8mH/iaLh8I0/U51NspIEfq0dohwyFXKgm4NggwQ4rRhCOUFtxxo8XnitT4cnGfT93IS8FaT85XE3H5LMY4zIEPL1hw443wz+1UmhTJyJGxZzw+wsKkKZgUiVtKOKMEb2AKHTv61FNc01PQFwKnvsZ/9pPA4RKTASWahmh+8MxwzHxKy74IRn5LGRjsPUUwTu64UYNY38caqd7HKucZ/tHnODtENw/2UfHRMaq1UUPDJQ0OKkWCeet5fYOhII1VRz8+/Elg5j4Gxur3J8o2PJ4rg+2d08T/fwEzSVbyZ9XPro95T477lRKqUSRXQnauHNsISAl27oWi6Fv9z48JMv8r/aMMj8onCP/DuDZOuN+GPPr/+p7bx+7JlbYdppcNhzKU/1Px5aiaGDn/s1iGMaBcleKUo/v9rcxkZj7DBEKOfrayytXNLYiUdBY+pleQXdnscKlQcpzuWluxsieeyuXIK6SdxozitWyGOV3vOHHjguyCQ6fpIYy2JwvrQEF/Qa9Pdf/QqOSqCiE/EE1/XIVKTc2tzWbHnimrEd+Vyz311Ml3P0GVTj7PD5aDnsvCvH36alEaPMePcMegXs7x8igTu4B9v7G9vTHvhCu/kzIdx+BxC0ay9zRSvoS0F2lIxI+X7klU63I40gLQ3w5ep5na+SFnba3z5D64zv+QtM4n4ffG3tq4aNHGRfxgrXPMim+5487abL7xhdseIRn1KDl+7aINixdv0OD+JSPwKf5+xoP6aiTeQIDVlIhMcL1H5R9PYXvprs3fv2bO7MOplCmweuiq2JRZ1zz+9a/v2PH1Hfz9236w+ZrPXvWfAxlj4NLLHpq3c/PQ3uvmvbrjG7fe+o2y/cLdtE6VUlXi0ASb1VLUBVSUWSU4HdvAraTyS8xzM8NxvxFkXV6pUVRiJwcgC5zEeht4rwcp7ki0k41G0qlQhG1Vzlq8alEmnFi58caB5Q9vn988MLhqyVlHvLEWjtQFeupdiocF/tkkOGPW2ibWaBTkeZ/dvPWazXfOnnvL6jkRXpi85sFzZt+55ZptW3bl1cCCHZPD06MhySha7UFzjcjbp8fOecFCirzAG/yVjBX6OFIaadSjQq1nNhyIe8tVbaaSdHlXIWKacMeuZA1uxS95zILhyrxAdsXTL6m7kNQlx2P9uZf2qhufePFFbpI6/OU0WcP99RrCsrwseVot5mtytpf6Y0gm9sdeyKnPQ7onyK4nXlR/rg7H95M1upzu89DH6pgUcikoiihJ6NJKmRxV1x+MJiOA3YwhDRQrWU0u/0rvq0VYXnyCwsLeTJYBq3dAtJDavuzyoVpzZ99Z0+a0uoiFH/xcqgDR7rUFeOrUn6Cywb8ZeNMbhLV5ugP9l0zv9UN5b5mFkjzxUcpPJCn3V402pRxtJd2GrnLdhtVk9ZSZh9W91fCSH5B7ofxPiWL+j3D/uwhBRdyAyozeZwvQzs79soi+BKSnafLviZCcfrpBpLyimfLfTyJtbyruIQKD01tUwJyKEo/ybaxkSNFUMdMkhQoJyRBQFhnUkDQSXhTM+3NmY0EDM7ffLIjqWEGt8lCO6mLia3PukFnghosJD5p5SIho/VDkzQfLE+IrYoJXkD19pdP7OwG/voIUtagiWiZ4PAFTHHlTVhRZ7dYmPar+NJ+8JhmR6DFK5DV1foHoLNO/pHrvZfmWZ15RQlwvoVDKhCWNK3CCch9lfFBuAqUgpFSShmNaPj+i5++WZfKeViJfW5HnUakVL4UCNVkA4+ETfIqx4B5xSaP2L1yn0zn2ltPn4+OqZGmwwEVCaCSqG53ldtL1oLGAhdMLd09MpCCF6tD6ZnAZBY9hDaYsP0jzZ0j5ZjKsF4i1UmLuhbJMCnYJPt5VwFNvmZawXjEvLJqIH8STonZjq7BZ8gKgR20C9MDFqJAX1H64QW2NEup6qgzLP8cvppL/NNTOBTCJABOHeWoXzLhw4Wuy7gaBtjKr9kgKq8ZlRYBS32Lpxc8vIhpNDTfyNXWybMJbn2RyQ5EmWc2QF9wmSZ0KYCE+cPuYO6b15Uotj2Kd4MItLS7gtFbkTdrFND6pvEZqv5Yv7jXAus7Pg7avo7KDot50NX3CPkP+Kps8J9/3mGQIteY/LGPC+L7872SPR2br5fy8MtKBMHedGuM28/MZmPJMrGgi3Gb1S+Si1/L/zrZwO9XH1ce/z7ZQ1WSoY/+pMb5FT4ua0Wm+Jf/298nFmChEQ+Ti71est4mq9VYI6RsymoRJKYidElT2FGnDTZvqtfhGAFTbeqEw68GqtfmbVa/1IFO1/jdWr/8BDRRtQh9XNjubEm4aWVpVonpTGR7PVGc+KJNoBIWF7kYi4gUV3r1U6723i6TxUl3n3/tM27aZfKb7THiHW9VzFSwHJ05VfK6Ar7kaB0XgPPE0BSkSFKsBUpaLihEWoA9wBt8qirh2VSOkZwXEwyrxZ5jyt2rJmSo9gX7cg6jsEUGJU9z9xJPOEM3uQQxKgkh35DNATnVyrmJ3mbCNyIB/yox4wH1bg2DwN7q9kov4pFqny8oSm3RQbGgJ1QQTs6ZMLilOVYJ9v6Wha3HcJ9jddsXp9YhGUXLXt/qMDnvLpPNTXfNa60z5/yjXQOMq+lNmwh5egpYrdfZQZV9rI47xlRkuyTjpzsmCBSWNkAXVoK8sgYWqQJWbo1RLo6QH0YW6pxqfCnRgkd+RiFjUQUQ7poIaYoakgXxwFd9BuuI38H1xBxXSFb/pBDIKQFn7YB3dB36l7sG1FLaKiBdp1KxLvfswap/30lnVESgNnvjbUoT6w9N+Xoio0qcYOIM+heg940YimsucQVvli9NEcft2UZwGQwLuilj1fFr1i3NP94X+PE7Hpvtj6lBJfJ4R6NvWiaL6MgzWHxiN66DExa+dAdAbMYX6HVF8A+7rjEZIXAVbDe7PVI9rmN69JOLV1DOSvRPxWNPZBZf/Nf+Ny65BhYxxxV+77XJ2wfQ389/IQPgajXbwMsuAz/0IaQcXJavKbRqR2IqyZruXjVC2+hdee/5vdnYOedpmVtR3NGXldxSzDSIiBVpkGb9by89UpEPKrSLZmyFDzMab/wXl2CNe7s/qCtTvWgG5kpBmCBlSzDS/r8N4uwBwohRW63JTS1y32f0TQsPfXVGEHQrV8/NCfiOUVirYcBbIeA2+iF68rQIo3B/S628vYESr79ehzS7Q9LEL9UXmik9XVHb1yBO3Ngvt5935+k1efkV51mzzrM0LL3/20avnwMeKuWyOUZg2TasSqZ+KcZQiOn1Iu2Vh497ALUVZiCKt/gh6IvTIj1ZLRjWAkpHKOKovNwp00eqPROiAbiNEKieXwMLcXhVJ1/uzmLP4tfxaHR59cBdJVG1kTAgl9ze9QKUEQ946Hkb+okJ5JRDyf54Axur1D+WS49cLr0tTPEu7UmXrxcSr3XNvumv4yXzInXKH4F7Tc7p17Zt+t/qW2+93k063X7VW6lALxTY7i1nBXMxcxmzQbabxz+tJo+wijYaIGMNS8AoSMgAPt84DdHOoMPfjXhF+kuH1tZvuFQrRCN07xGcXRX9MYxYchDe5BcHj+Z4i+42WyPc8Xofi7bbZJN5nJLJ5qr6IqRtzqNlM17SpFsnkEyTWoABEjz4JXOQvzWYuwdnV5LNGOwTM5v9r4RpQ8ZXsYodks3o31JBlzbYtNotisnm22MxiwGFXam5oN1n0TA/hRvshvTSDwHff4nNzRo9Dum6PaJbMXzDz+x+Fkj4L4bFNBb1asqsgH7Dyh4DvbkPtf5yMDKzEwyoaESMSNS9P9gJVA3/RTlwoMwZvxECFWxIPNw9gi01nOHjP32esZTtmXHnxvZd8ZtakqQ7ekajbXetpNa6ocTVxJtY+uSe69OLz77zh5bDR3xjZMzUz6fxrz1nqrZGcHQHfPVefN+fiK86LeXj+Sc5lPKy+k/vCUI/DaLFYCWHr6nbXuILTIsb5imNKY/rCm28fSMxPhkN1XbNMNZGuqwOBhtTSxWuTk6bw0ZaG86b1hKddePOKuBvmiguYBn4T/yOqOyGRBt7bKUI1GjioBC8aUKwF7Q319UgcmtFGIzCJGBqwQij0ynDsfdFGc3TS3BlNfJ25xmzniMkpXXTPvCaD3ZaZvyzjmZdudBostmhb0ORZNN2sJBeed1HXkrUsywueQH+L0eCPxmsa5ZpgRJSDZ11yDv+jmbd86vxZfc1WcZJ3UkMq1BOOOVtvu/+pB+en186d3GTwWAw2jheaJs09/+LNfZft37DALyrNj1wABMuUKbODyTVnT/KYbJ3Tpq8IrNh92dkxOj5P/YpZx4/ycyiVcDYdn4JbEoKdQi9054iBKsygLW46FRGxAb0NPNCm8BSNCPjoKcj6EAus4SuP3rB+cV99/eTF6294dA8+TK6v74MHVpYNRt/I30e8QGTOOdfGWzzxcy+87a7bLjw37rHw1nPzp0KyyRSeZO+QQhInt3dYgvycjrPOv+T8s1rptaP84VeywdWX2T4ysr0/7TLIs6+x9zib56ye1dM9e/XsZmePY3NDs9zlnNVt4+WgHJbbz3Livg4P9WWgviOMm4kCRT6I8vw0NbUUEnFvOuFKoxQW1gTsvFirsF5pb7qTUCx4i7VmtToveaDxvK9uOaedVvPRpVOnNz0Q6bry7uiSdQ8t7Vy4JQKVS+XPplV2ts4bvCwZu+KzgITtxepaPRzWdpv74muvv6RO0SorX6cu/dqKn/XWnrtp/Zragz13DUCl5myiFW2Ycvb0PtsXnU+tx8pvLFbUspLX68mdegwmOif/NPDONajTGoUh6tU56HBJCTBASVvNUB5VIiKpc9kd7kludodSFz7xQbiOmMk5dOYk56gzL6uaf7N8a6MQOHm0ae6snZpFDfuT3/jdYzjzwkXXIVHoXNuCfQslQZqBZjTsoHMqrkE4jaYdgkGz2ATOgB3cPkSukD01DnV3ttb1wx+6arPqbkcNAHoFPzKUUQ+qL0k97pjbZv1I/egC9zTFbrrlFpNdmea+gIgfWW3wqkcis8ky5FAcRd1If5nNZrl2FFpungc8wpoCl1BpQV/ScS+zjlASyUTVv/AJ46gkJI4bHX4lTnloctxPZE1ckS3+jG2fKIjkQFyzuo8jvYQG1OrGvJPSTu/nSp9PHNTl4z5hK/8gtXVKF6gEKiglgcKiRlCESsQCV5QIlKWKpr34lt/wkSx/JCmP5/cBKQfl/5gd+rOS/+p91/+YCg5CXK2W4M9fu+/6xxX+vnelVuldIDCG0VQTpU9Dw4pRfei+6zWx0MLie0gPbyrkmRU7OwT16JGeyXLHqOLqAfVN1GPlBzWtFNzj0TRTCjogtP1NjIvu5habN5Aoa1k66wGpqriVetJgiGdwDZtKhnN0y4n9sXYnsqGmZfDSR15+5NLBlhoDaedEm7sxmpqRija6ZEEg2EAnTiAC8IrmFbGz1q08P9PSkjl/5bqzYqT9hMmptEXDgTqP3Wiye+sD4Wir4jCeoHbbp5hRfpB7BakUIppIlPCD30dR1GtslDz8OsqbXmejFC/v8wu5X2myq7SJ8Avzv9DFUJySf5uNvq4+Ti7W9D/OZrLChdwxmPNiBRqVjnpK/aGxRCDspVYKAW9AN1JANoo8wP4BJUlGqdgw6m1qPQ2QW3+OfU5/ieLS/NuKpDU3uf8bcAXyBal5jMR2NEAbPAZt0K3hvxHBEDlUxfIGcD+N2gNSNx36nfqlAYow0puatNpRz0e4W2oahKzQHsjf2c16ad/3t2KTtPobnX6D8C8pd0MDP+Kx7wnXqGGlLQcvikMErm6TmfsuxJXbSAxqNjOogJLQBLiKEHAE+JGTS3JoEhTrz8/CB+5YlupJ58aOat8Kv4JvregxwcU5Cp8GFAFm1FyOfto6GS2m1NGTS6CPNKkbsTdCBlnN9onMho55BX8IJZtEQ35lk+htwN5A0V3RCPoD/yXAcv6pAtbZczRUA64JmcUf4q7Q89ZHLeJVZ5D1Ps/t+0iCT3AHVtZC7JDCXfR7OSb/Xja5H3zQbZL1B+ULX1BMTEk3AseSpmnKEK4T9ekMIidUCRQFfcbj7z8gNLvzF7mbhQN8h6ZbRset+nQWdS/ZX3k7WpS8P9sfo0iGS64wV516pOhjI6TZ2dApgI5+LhxywYoWxKUrykKJsIoDsR4mSrCTg0egMPnLW/3Q5Nn8BZEuzqEI7HK3n0+zFmuO3TtWQ5WJoG9YqCD6Gc32SxnbnVPfsxvrFXK2dILl7bLthDp6glhcsfp4bYvbSmj/mQ94uBTw0E73x2jbNRCvC6VL6GCFDwU7eWQDcC5FY5s0slieRDwtAbRsbLXbaXAuu14e2OJw1dc6jQ3ZdY8v7rv2/BWZLqvFWVvvcmwZkK9f5jS4muO9yR5res4kfkRxhV03L1RfPOiPtYi8pd7jNEsOpyTwxpaY/yCZu/Amd5Or9uS3DYaeqVOhH7gZN/8I/wi1fEuLXvyNivibjuKvN+1Nc01HF/3h+ef/sOhox8MPd5SFucPjorQwXT+ytA8EmA5mamHNFDVhBI5pjZbQpugBNkO8MvRub8KVDKST1Wag7D3xlin1ZF7LFP/79nbvCXFOY+PUjrT7/otsPXXZ4exdPzuhZuL5LUXVAn7k7PbhG89uz3b41X01gbjP1xwlu5rrvvf9+pbs6E/Vu7Nk642/PYRaAiUBdrmO6CDTBLPQFA1ur0uXoBR1INDMkypKpoTqnSMx5GiEdTEaSHLs0Alvu/19/5QW9Rv1U1ridT22i+53pzumbs+XFFXYC++CGsTj5JUT/GCgRt3n78i2n71FHG4/u6X++9+raya7os3ZbDmgWfXun44e+u2NZKuGZ0HiF8M4TlMPR+EU6rPKRJ8wOU2RFUFLex3egEsz3YqEAq0cqhAAW19dBZIlVzR61tuIdTnpXH7l+uXrbjPUyep+8cl6aXKWhPHpDcXl9KiTWDNr4mBQc8Tq+NzK/OKSbsfl79o9G20R+brBXYvUg0rLHhtrc4TN81TTOWSZ0gL1ZVlOYH2ery/7XVUjFMbzYpg7UswcqJPQwBd0LKLabJ8IaCr2otcjSkIrGwootKECaUd4XH1+SdazRrfddkBU98t1htvWrbjqSqjaCguxrffM/5zDCpBALUycmajhd+R6ww4SWafuZ5eU+tPid4lgd3gt+b/Y9rQoZNmiXYPXyRHbRs8zX/f4WIFjWZJtUdSD55AP3xtXH+ZipC0EqdBGDA4CoYEU6gRLGPU11QhkLTBiEYPiqOeQgwTCl9aok1Qr5pFf71qEeNxjy/8F0GoqYPv75Yh9j3x4DuJ+uEzHRpAq2lMqb+qfTdiq6kGtzfOWsv0c7lSeMXDHBDe1MT+LUgx0Pg/p87u2UicdIvqQi8DkxhcUwUXCedMpb4NQjwY3npTmgsURJavLwCRyEcN2HfWsDVGfv/u9ZUWUx+PYFueUKwaNvbtu+Xps3eVWbN1GcgVrdMnWJ7WmJz9SD66EBidag0NF1Ukep0t5A7sFCWdhzvYwHv6L/BehXuHqfaBwBEU7hfVLcXvS4VQv+T/vaSIl7cbeMc7ekv9i8S3e1L5xxpvMGcu1EYPbKyCiijjGXcDKckm43PqU2qNWlXusZMiqF82cuVzolUHN9NNR0HZPxFPV9V0wLtvq+k4DqOwVWDlzuQLVdqFiP08cRX7aRlBVfR8cb55bWe5LExnlcsDp1vAP8Q9BucPMk1Ulh4GnN0SAdxcNHv3q9ohx1Ati4S/tkWjIDe3hQdkUGrGRaFBiUdiTSkI41UkMuuQHP+EaSQYlPQTFWJF03BNPpTu5KFAdkWgDukzsZKMG0Q1TAQQglScOaP/dsZ8+fP75D/9Uu5Gs3FY/2SxPld0DHOciXI9gqjcEidXjE+3BLosy0OcX3T7O5g65ROGyzQ2BZs7WbZVnO5ydLe32hMwTQ4wnnKXW6XW5LAa7oaXOIHoUl0FgLQLH2by8wSTWeAx2Y5PDazK3BqZbeJZwXGPaYhX87ZNszoDdaRxotXO1nNlpdvAPFWHDm8PqEE0sZxDEqGzxisFNnuCWetPcGrObN0p23tTZwMuRVodSV8+LTrOV3eRvzjQZiSjaLYS1WEJe0kNsJlZu9LFun7++wW4gRDRbaxw2nrOGm+xOj9cmtbp9ZqeTM1m8UXfQQCSTVSQox6pvtjot/FpHvIUjJovFEoYvHYV9C5Y/xN9OfcalvII37UEhTbTg/AQIaPb4Vz6j5u8/aViycMod/fkDcpu8QZbZoeBi/vbzP3XPsZvOubMtaPHkD9jt6+U2O7vqU/9C9SMvgrXpQNG/E0oJxun+CiElUa0IKQSUwERxOntKSV7ekcuh9VBZBBo3VUcB58ofKBHCwLyf9qFosz9Ibf8dGqwaBMjRig4SGOZ2UkWI7UiO9OfUPdxOYFApUZyfpY7mgEc5rtNGGk2H1lPhAk1Hp/VAMqQEHEUfEYkkUQq1JMdzsX7kklRrTrUi1wMcDjmu1YYfATj7Y+pGpPEBXuoQIj8rR9mgCl4C9yqmF7xnVWxGVniNqtpVmXBvQ6iwni5YQ8a1jYrXtc2J13HvgkvqWxuva1sbr+P2S5ceKGyBwDv2DbrToe1u6BkAJV7xnVLUaq0sJB8pFqcUIPi3yuwxi4JuLr+P30f3OkPQ72aO0xYo3/EsmO3QO5qEF8S0qQH0UsKXv0brnl9+8M7jF174+DsfvPOl1au/RL5/9DsbNnwHL2pHR1NTRxMZhJtHktOOxLxErPF6YlLvpC9YP73x+4ofw+3xVdrHcDE0dQQCmCRgvt9b35xINDf1CDcRSfJ+pYl+Sf8YcurfmXP5F/kj6J82jNsrkWiEuhVlgFfyNkB3S5MUzLhoNiwSCYcxQ7Ui4J0Xh7fmqRbaPa1tzujxkBRlsEHy0/OM4pYLPb7g9O6BQJN6l9zQ0OGyCaZz0vMTbHOzXfQ7a2tsterTcqxeInODoemdktw+1SbVhKwtW9ffe8VKadK0OVuC3bWzyKm5LeddsWTeorWyY9IMtUFutdu5g+Rn533qkocdvLs2HmhU75br/MmWtD8zA3OP2t1ea636jEzqYxJZGAwFiDEd61oTsrRuW3/3pYNi3bS+Rd+GjOfVpAPNd6y64Gsz1GaZleWIPoYL/v9mTeQBENVEguiF1aC4YeXxFETw6QyPfn0m9g8IrMFAvKM1EI11DARnbqibHk/Iojy5rSdgCyZi06y8sS024PeuO4MfwQ5Y9yKRZCqyYaF30vzeHlmUprR21tR0t0yz8KZY66zWuGvxVQB/36kP+K38t2Hu6NQ9SFJfw0AdpqPEK2qTMpf2VCqJwqPoJezTL824b8akoL+x03nhh+oNo5e77psxg9Q5LzebIKD+fsY34f2MtB9fk9v5b8PT6tYrgv4kRPwd0q9z3gdJSJ0653KjCYPwCaR5aUY63eW48O/kdo33yxX9wCiMv2QTrk8eGSI6Ag6moG9t2P/F7GRNlDjl0gw7pJ5aOXXqyqn8SENnXBmbSwUYLyqJjv3UmY1nKr4t80no0faXsaIEiF/BRaIBnItSce4OUif7W6Vm9T9H1X9Vj71BEm+RdmIJQST/ZfVdudUvh9S/qqNvqT98g9SQ3lHibZY0mRVHooyDN/FHmTgzjdozKw28NwQ0hwN6BCoPKaEk3YtKwNhwRLXuk076CGoZNXDQcRwZvreTZY9EZi+d0s4+ztv8iei04JQl6ZbDD2eHV7X4uHuFVfPrOmcs6m6Kr7hssr+1VZFcEZ/PdJkn1hOs8SXS/NFFgqt94PIZzZ3tdaL6Q5vo6piSzdy737pwsX1VyxUrF15iJ4uNkq+rbyg1Z+O8VsNC1UmcvORPRfxtPrfRwL2p/oA1eZp6Z/aGffoewaXcA/xBlKlQLfhQL/oPgBGP3qsA7IQS8qDVNswHKRSheDUvA3Q7MZoRcJMxlEygujn1QdyzfPfq3dEp/bXh5e5YXW2Ngfvza0ZF6UgFL/E0fTq4LBlvTE2qb/KuuzYSXVnjTfM1osvqMHVbm9950quIZlbqaL6YP7jk3kUtA0GnX2nvq53f3WoSsvEdDRnULgo2fN7lNZJgI8/VWi33c3bBZnGY05+dm+3qc7fNmj4YGKLj2nfqFP+g7jdDlxEV5XsJQZP6hYrS1l0VQr4c69Xueixp90gnZPmE5OF22j+SYEWHlZ0K/Hgsh/Ztsbh6h2DNRlvv6jJh9XaJaHCZDiUDKNTMkvb8vsqCyf3ZNdSmO0fa0Y4baJTtpbKzuVzeeSI7fCKr2Z0WypapnXJ4gnoWy3PoUIlIQ1TXdqhQJIXp9Wx5fYdpeWh2TY5D+YVyKd0jw3iumwi/BC3cEy4o83QlZnW79MrCgCjbhWXBlRZVVZZv4rIKpXC01HFlHdHLoeWVl6UVc/J5uGm6CViW5mulYMk+HqNYr0AyUPivLg2oMs2MPqtuhHyRyiwvNJej1Br+fcLyoAyu8D9B7bgmzUqfFobF5nKnK4+t8MPJkI/xHUNWk117jugWF+xazTAALQn6+UE9lhoI5ApGA/iuJOsrlNP28SVVuBVajXmircLel46w2bJS1Q0Ft0KDuikDFL/3pYrid1Q4FvofwRIo4R9h2ftSwc6jHAMqLcCql8YPHtlzGoByNXYN6v8hXnRaOhUvx0sVLCexwupGDR4NOYC7PePa5keIPACnuAdD7dEadRuTIiS6Lb7uskb381My5yjzF8lGCjBRqdwrWJCagfB3yCy7XT1i92hbcZ5Ci1FJkgYMDf6n+jspIsHFjJrTOdzSMuOa9DbDcj/nH9N9bIoGVgzHPWIQuFuYtaMRaq8eCKI0gEF6lPOZjBz3EEvaaxwSUT9U/8JbJZPJJLBLolH1La/RbF9AbC8JJjv/mMnssKjLRBJyqj9QXxNko0Ux/X79epfiXkm6fmKwF/en1HLc6LxloXWKvGa5rVCVL83VuiPcDEX/K5pTXOxHfx6HHB0t2FI0qI2rCZFTrvPWU67zVuS/kTsLnc7IKhFg30e4FOkqNSfH5PtkmUy6Cpiv/36k2sbqCeCFNa+URpoY0sZoYmCgCr3qgZz6s8I0gP1bYiR+D79H56NOz0EVWCTy2/fffvSCCx59W7uRV9995eqrX8GLesOXNm360iZ+T/El3uZqL+FyzSZ8XxpTiI/G0nkT4zznFZ0t4ipMz5v4q9ssqbdKUZt6u82knPCrt6PZwsnn0XySVnyPR1ZXAn72yx48bWJsu7apnI3Hy8bygUK5Js32qcytapqgmn95uexccj205vGgJ+euOeG2SORmKZr/qKzcx9SFctMJdwMUFZDJITs7dnOp1EKZCxg304Cevyfya+vlKqv6aXK1qIj3imL+L6hL+yvUlFfE0VKZ7E8gBY3M/8VoJCFgizH1W6VyC76nH6b7jiibYVxUmVIEspry/LgZIlCeP11Z4zs/AwvVwtGFEut5S1JY4lfyT0N/evOLo+rUEgjcqc9IkGpQbv3iW7Co5b+KgjvpzYdH85PLcc4X21ouwEGl/S4qnUAvoSlXUUhR1eKr2VWFTB+GMl6FsiQsVD1R3urlAAIoSn7JQkmiVVCHSpCwDH/qPepXQ0Db77CJOAImohB+RPWr31ev5g/kE+zTa4lbvZo8xdWPffQu9yJTPCNB66s+zXoJt/0L6hSoCuBIoK8fnBGG87OoRckJpLqyWe4YbpGi50g0+3I3UD85Oa0fzubfoXxPLbW3FDWzigmyJeM0tQkax7PqTy80+UxfUHPlBZIRVNQ+v0xRm8REKPoLmNr0+Uo48v9GFbXPKylqQ2IKm00QddgyWGMROCTxdLB9nCY8P7j2DjlsV/+mfr0C0r/NkeXbbpPlOTBBwT0mVz1zx9S/wJecBF9Wgv3p032iP2v4VSgfgW2G+HUEdEXU6iq4CtpLJfIN9XQG8dwa1VoO8XC2SrPDDyCOQptXgbcPvlAgBfxBoGwftQKeKFrNTASPt3pGGqDt/QRasn2kri+H6L80MJRsmVYJrAKyDItpJUy3/15WYIJqcJ9Q5N/LFJ4c3dc1URpWl9hW6mu50MUIelg4ucTPf15zs5DFo1c0VSp1tKB9jkwIyuM45kb+IP8gHed+6jO3v0KbIknzLy636E8KPTdCuUpB0wLo9JKnAO6pv0vS31EtBha/fJemkgLVVnd8KCk4qBTpQ5m7FbifBKrPJcq0pZAFVG/XbOFz+Tcq2MLrcmV28Nmi/OHskh82bau0k8eWCaPijQPWQ5lUvslwVCfHkXBMIehqUgtDNLeauH1huvZTbYmw+luPjyWoNGEuxRLR7LK5fSyXFUyK7PURQv2v8D3XOt2NJ6liBbmPGOsakw1kbeOs+31Wm5qpH+iJWSzqdPr2O7zc2TmtnrzCig6bBd/vgQmzOlz0STWIlmZEQfupogOZFHUZ7EkUnMn0RrpIMqAgHRJAOjIJ3yGw1I/MAp9q9S3Q/clADNm1wEeO+xbwg5OIYHZLY3ehG5lJk2xhco+6JWybpEVz2wrR6hZyD0QXZbeDVB+onmlimpkWprdAs4WEZDSQppsDlcdCBJJESIYFuAtUnC4GIF2C3Uu2Kv7L1bdz6FxtqxpG4TqQOqOUNAJ2HLvPWA2GgDy4O4vaDrtyl6P+1fAll+SyFcQ28GHqh7fvvf37udylf0fNwhzgz87Y+cf5x9GnF6ygHu18sAbipWeF0YPBgp2GaKeQduxxdEr3SgbH1kvH7tvqSLhedomOvZyts2dw8acu3dY/f+ucuMtCuP/e4zC4XnH3OLZ8ZuxTWxy8dJfU5dhDeKPSlJy5pn/+7u3XrJhmr9C5CuleGflGQocKnlAUaRKp0BAHV0ZwUt9VCqk6zYOgRIuMfePJzdmBdpPJ7/6B23+f+sp9NMDZevovvfYHG5dGPISQq1DojqNckchVrCcCYz/Q0hI0m3NKDRfkgsrnamo+p0CAq1FyvC3a3Nak/s5VX282x9Ufy3E39VAx6o7LpCvO2wK+ch9jNqpJCutcIOooKnYWtDK8gTRVYygRQfwgzKM5+jP2jOZdx3r32Py7rQUPOzAnoRs95NvRAR0qLGU11Taqu1bUYSzMcWjMEir067JQQHfIrLBHsrgv00/Wavd8HRLMEEYFSW3HCSNQehnrHztKqHcDyo4VfZ6gPKCR+gufwA8GegxUEo4A+gd0BASHiH6jYMLIsUdQJTs/C641KN4oCHWolCMLlMfIdtWKScjx7SM5LD9HnfmhrGI0S139UWfUnxgOXdJFW+AMcGjKr6eHAttHF5sUoeArYKDcxMSYcKA/xUDhPiEOEAPafSIUFArN0r24ynI91EPARDXvIDYyvqZaWeroBOUABQA/E+DXC7PWafDLQY2oiwpUEyj4RQtVlUp1GrM7In2p2A7VuiOW6otMiGOo5Mrp05ejVuTy6dNX/k/7mybZQ0nUmfrbx3U4KueDnlHm5wdh8FFeKnoaKKh/TK18StOPhwG9Xo5mqXAxvw/79YQwwDR+nAKQQ4izVXioB84qcppWB7IqjU45z4CE17OvF1Dw+oTFqxtz8dxwtogBnF9MjIl/in+K8s3hM9laIn0TiCbTAXL0T798bPXqx36p3chrv0O+GC9Xaj48Ecv8U8UEeBvUEsDlTepiU5OvlpeNGvpnKF0RvUooWhIjnx6GeBapXCQYTw9DNg6/OC3gZjp76oNTj9Kz6Jqobxb9NDqc08vcKReOpcsQV2K8InXFaXW3aI6Ofr1k48rp7CX7rx+v1UKPsfvzQU0Kc83i2VdILmd2/yX55zT9luN2+Cu4nKfwPcK/CvDVU+pHh8+LaldIf1fA5h3ndT6Fln9/W/9Ce1vndfvJtnPVO2xhm3qbafHVCN1X363UXHq9xuVD8OSD29Z8pZ5cZrern9cAdGW/uib/ud+VK0L9a42r6C90kL8KzxwLQw9NkIQJL0ASU8M+VG0KsUdgdvpgP/6NqqP0/gHZFUfGEijZLHpiIgvV5/Bltrj8Qd7XQd5p4P+7tJo30NMO6VGBwahSPMYiaaBYoLY6uEnciyhhh1Z/vvacG/rjpsvnpzs0B1Id6fmX8119l88XnOxe/uGrzzHcdu7UtY3+2vmXN5zUyj3ZcPl8p1sZSs6/nGXtwrV7Ka0XZdz83fwjjINpZWYw85lL8BRK4nGyIir2RiOsEyipuEcIakpGjWgBjLiHWOgj0Yi34gW1kKPxHt2Na5q+lwg1RdRSpFDNzosb44YJXnAfoEOpZW//6u1lhYA6leevezbI26zNHO811M2dc5HFxpk4i1jPC0s21/BWW5DnPQbn2X1WK43/aM2n18DfSoybbNHijFpamzXI31eRibGUOxSu/lT96YZlq1Yt20DaSBuG6knw2eusHs5EPBfNmVvHKdaQzcDfz9ZsXmLDWGXy2U5OsYSsIn8CS12jQIyD12KKqZrLPy7mSPdICmd6WGHG8NDZkkHuE4h9TU8FpmUO/VjC/EinToFyoNDz2p9XD6g78WgQdPG7Z3R0T/Z5dTM9lsL8Ktek7szl2L+gQwGgwkZHc2g5Su7NvVqwGy2Ua4KSXUwt1X4PaM5paaEu6jQ5zVFyNabxvUksVt2T/4VeamYPlLtffdQsk+2sUTY/zDXl/05W53/Bz9UK3p7LjapZ2ZxOm+UlZXrL3HHGqO8+wVroDaCTTnTxitMxmiAAYQzVJQH+nj3oIHnPaN6Zq6sNSLjBl8tKgVr2mj/9CWi9dnKca8rBQBsd5R1tzVlgrl5pbnPw6kZclCr2CHxMnHohLz+3KRQokzALyeIKFU1TNCiayJdoHvDYe7K6mZLm8S3uJ9dojuaJ62/qN/tjQxnSnhnKPw+LNrLi8ZKyJ3x1YhiI1aNAtP6NzCGzYv3DmaGh/LvQZnt0evgIhTFV0kE/PYxAnOHhCQUZdCWY5JWJwMzlAGl1mpNbDU7yyGnhRMILsYhH3VRAijrPcBU8/Cj1Y9NY6cnGVW0CjTLaz7E3epvaT/LtTV72Rs+0WVVmd0dz/MGTI5F0OsIviaqDlbbO5X6xT3PeXbXHRtf/z+fdka+eKPr8KF7IF4vBsT9MFPuPJMBTBMq9hQxXelQ+bewnf18ap4Ib+mSMrtDU5zqlD8QANa5MBGh/OwOvSDfcV2d66mfEWsbGWmIz6nsyZDWQSmqmxDneYyvjHPmRXHZxeueyRGLZzvRioKnGto9nIPkibAJA16adcOZRQr1iAP3bUyBR7T4RgAWTKxhkCYFwshq+7iV9r0whk50cmRcTg4fy5x4OmmNkHndIA2+YuMbmE9dwGYB4KFTsvnDE6Ah47r/fE3AYI+oXADpkdlENcZ8OZEEf8FFGZNxMs6ZLpG3SUFLL7Q2kcFU/A/Jsw+vWDa/7emewLaoeibaF1B9qUNnuqWK3+UfXYVL1v/omD15xxeDkPnXTOKSVcCbDGtOu0YQNpGAP7U1HU58UrqGu8xIbHtkQ3LVhb7Dx46ET3Ffcm1q0YcOizNmf3bC3VjWfAcpSv3MyTlgJ23FHQgmgvk+gk8pL0mcCDOn08MDAQlf+/SlTZ1z12fnqntOhbOTL9/ZdevbAPN+yby1f/uUtC/ixm8ZBo59LTXEW060hGrTDplNprWd58fwB/b/E27BdS/s7U+rGVCeQ46nzaw9QccnmZerGZZs3Yw9aVHt+Kh6HN4ti6lxIhT/wahnZtWwzlY9QHQ2c79C+dxzvVDKy8GqKWQERO9YAKbpsDUTLdWV5dE8PVPjvj9pqw7ah/PFVtkit7aj6G5xY9mfJrCz1j1e0BcnPol4UjtrCdbahIVtd2HaURujnFJR8CuOuUUfhrGhgKKgjCYNSvCc1WKlEp8wHUaAYynFNyzZn+2MnYv36dbMDBTonl/T/ma5IKAyEGz+4eRnVtaX6tss2o34u8mWorFtuFgm4A6qK/yp/gLEBVat5WnPDdKA574ubuFJ/IUfZ/Y2Nt6mN+ZNNTSTaeI56gKwkXerTe9DDHUw8/H35FY3nNN7GGuBKWhrV9ep+0k1WjNWVaHkW1yA+QHWNu8rtBw2a5YXuE40rs7/GA+j09V3hA98yRnFPOGr8ltGlsFdD/7tRce3LH6Trcneuiy7K7J3khKu+3qUaXPWaX7T6/Kfj9BX2eZq2XAcZT79u1ClJzUtHUqfqSMWBcZS43Ena0cUGLgpkKxB1QM+0Fxz10wgg6r5rltnFpH05pepUq3Y2HfYqeKRntmUFNz+XmcOs1H31U6cC6RTVLfCg7RNBF1UF2/wBgu0fFQtPEU1sSg3VcNsR7dWq3af87tUFn1l3ltXpaJxpNvtcZkH2WmMst3JqRpxUH+WC0E1qOGtP66s1MYv+VLu8/XFXvV/ZbunYYBeVN64ls0ur6NzpV9xzlmQwB5qC4Tq70WC0tk8dWJXeHvkD0h9zJOM0vD86/1NJMaIAolctvlByferCsqOKDKceOfUu1PsmoFCamV5mCrMUOCi6V6FJosMF22AcrKJgQDVhfYh6tepp/lYgvnCEAbJQ1L0rOpajEmRcasMiPfxhgGoVo4rwreQpV6fUJHH2e8fa1s2c13Apl1b89a58ozdoap2sjgLN9uISl7P1DrulyeIkt0zr6JjWocoPOZsaXPb6jtqBblsgsaRre2xHi4nELm0MhG1+x1SXwLpFi53b+aHRYo/IrbZtuWAKu5cSEXfybnnmUCaXGTpQr0xK2O2WWY76f+nAjNVf7nCZHU5XqIkTnpt6VtvsFlPXg1031g/VRdpkkyVpD7jnmax88QwDvg/66NnMRdRXTcGTmQc3cuINwN5IQqi0yzb+YFVHuVqI5s4ADfg5oE4ybDLd28mFSFmYvRoomsWXEdLU2Wl3GJy93ZNb/d5gqmNaqJZSO1l6PVRy0nZIj/45EetjLguh1rLqR+SK0hO6NrsqcNX8zoUdjQYDJ7tb4os6+i+Y0qpY2AWlnLRDWdGFTfGY1gV0zNAtJ7pdo24se0D88AwLY/gZmE9iuP4V5v7CSR/RThaHLh+UeBkXwU6BC7lGOevK65udTv+tS/PfW7qj3ljTcj3b9OkbV85t8xsMj7Ddj7DGpthZKwKPvso/c/1K9aLE12fMWLV1y1D9ua8lyJdWXr/bG+noCFutf/mLILe39ITUV4igr3876fpX5g2zeB52sWnIL4fXHlgeUzOx5QfIvJQyrKQE9wHUqVq+PEaOrz0wVvNbJZVSfsuMzxN4l9PkedFzw9V5Dj+nzpgoT4ZxCxJfC5RWLc74YVHxKlExCYt0JAOMatREhHBSCAtSfod6x6Ls8HCWECLwXZ9nd5Dz1T24JUdWs6fU3++fcnT49Qe+kBs+wdsMZgPXMp3U5S958snPP/EE7bvkOPCuTUDTUQ/UzirLhML9yPahoe1D5Fj5jWsaoveyP00PehdUAHk/seDVWsvDWXXXsyn/4wfpXc2V3/Qxli3jl/5hj/83avSCfpTNxOEKLmTjxOEKuxgNlsQn0xgct724mhynupNW1Ph6o3RYS3/+2TJrzLlkFz+ip3qCHKf6eqW02QJLjBYuuj4sobhCWqa/YHGEHpcnumuWSOhxeaL7sOakNR6vvmo+YcfFA8UFXEPZf9UjyudIOyNwx/i90DdsujS/FX2UAwvWSVK4NxaMhAGw3oowp/uc8CTi7D2rBgZWwb/60faR7SPsEbjkXy4G0XaqhXPwe2cePjxjxuHD6ssQuR1fq6PF0E+o2t1nePTn8TUmxz/A3crMoCc7egESuoTHYc7mYdg6etORoOhR7BBGD+qJopELrl4S6cJNRtEAsLP/OdvnJq0Wo0GolY2Et9VFB2Kf+4bZvVyxfOMz3WdFfSIryj6DwWghre7aQbdiDrkTL3A3vNDuDpk93HqXwam+bWmUJZfNn5ozKV5Pmmq8PF/jVY+2Tlk2M2RzSXKjmbQ4RZcQavEYrN/9rlXwtIQqzxQNMzPPfHYLvuPoO9TbT8bpGw5CQPGd+SyX/Cyf0Vxjd2R9NmsunnXYa8xGHzn+sSfM5J0y0DZEXWWxkXjcR75KBLNLHi7XvX2G8VOrf4Ykg0AMdBESIpo7MgAfyakA6rkqpI6UjNs0px7cMV+D5BF49Tez1VGnYmq0WIijp985m4Sn2gJR9b07riPPFo97OYbUZbxJCpot7H/lpZBicglCPN7WOfJkcHqc3ElWqvvz/1E6bIQrG+tz6WkM1SM9FBTR7FSs8KyBBytSmNEoquJNFN5EQyTiCrnKDx1h58yxCepPHU5nxGoxEQeeOZi2m80DxNxncVhr6BmEfUarxejw+WSiHhWk19bSY7aKR5MsteblJpfTLtjimBouXsm3d3djjYM+wEW0El9dM/ueVRWIsXwe43R7SgbVZqrnqoJ1X/kuF7pcgf8duv4q6vayV5U9zMV91GxO59UUjW8rHV6u799WzKMT7umRCXbYUKM+foaCcwgaoqZUtmodV3p+X7akb4dnU9B9La38RPFUG2SCC90tVA4XwEFhyOpZZrUCsgWYHsczLFBBVGNtstoN1bw0Z+O4fYIbvZVt4EUcJEKOhHeincWqONw+q6w5Go+WGOSR7LhKV+KBqbBPpfUvOf9QqkpDyVhBeyyZQGMsdA5FBUqvFMtUyGq9vjnsAJU4UcrxldP1CCaofyDkSAifoP5QwWx+SyUGxp75BzGAvtG7uQ38LehlyEQMeh0TeE6Bm7tYdXqdkt0uOb3kfYlNwmOdDyacOq/qlFo1v+PTmTi3E/glC9W11b34A22zmLzvb231Q0L2Bgg60OTW4YdstO+YOJnO38TtpH7zy9ymokWyA79qlVSn38HtpFlImFnhu3b4boNWXklOXV0Iwo7lQ1hrZyPFcwtjwFP7iEKSHSSJw509kh8kj6pr+H1jR7km9vcvqN9657vffefkv+fKxge1X+7RdjYUPIESN7gTvRkB/RMYtEkaVkdHApmdBPpnKmz0n1xSWFOyVIuLrinZwpoCRe6kyiVZoHX088F+UX4+WKS4iBTP0IWxGtZgOdMaV4KTayqHQF/VihBwTbgDXTCmKoOBJeNhwJMzEVjtjIFLuU38fPR7hqNG1JS7g/qRCuy3vmQ3W9Vu8qbVbP+SzazGRJH83MzP90Ck2m31mMjP8TiLn5uwD2Ugr2PFvPQjB5BnSJvQxGQZZEB+LopqzGzDbMmbkAPkZVJjeO5FzOSBKCgJze2ZS4Gemc9twrwY6u9H61iUQTcRvtdT9RW3tRxAWwFs2tcuJRnI6xjmBdWjbgFNRHMHiF1uHYBfUR/ut5Ug2jXAaT96+9RH/FToRwIzGbKmVJ1AZQnoabSB1yyIg7ByAridHApPMjyw0OiV6RjSbCuzwLAvFizBliWJua1tsuAgvNPbmljYbpt8lkWam7b3XZiOiKJskMOtmfScnsbPW208knwjuXrXK4Q1iKIgNyYXXDVT9C2Ye/78GQ5BEEXfFdde2RwauOysdJNL5AzCy84ard/nGAVN8alecnFdgu5Gbd5DJTL+hHZK0vApVy3OfU8XTSJg1TlssivsPYUlIqvn66PzrVTymCc4wgF6SDNR0pDf+9Gp+VnsUH5WtpHYsuhOaey8zdwLN47V8MTbm78g687+P3cx6tcAeNpjYGRgYGBk8s0/zBIfz2/zlUGeZQNQhOFCWfF0GP0/8P8c1jusIkAuBwMTSBQAYwQM6HjaY2BkYGAV+d8KJgP/XWG9wwAUQQGLAYqPBl942n1TvUoDQRCe1VM8kWARjNrZGIurBAsRBIuA2vkAFsJiKTYW4guIjT5ARMgTxCLoA1hcb5OgDyGHrY7f7M65e8fpLF++2W/nZ2eTmGfaIJi5I0qGDlZZcD51QzTTJirZPAI9JIwVA+wT8L5nOdMaV0AuMJ+icRHq8of6LSD18fzq8ds7xjpwBnQiSI9V5QVl6NwPvgM15NXn/AtWZyj3W0HjEXitOc/dIdbetPdFTZ+P6t+X7xU0/k6GJtOe1/B3arN0/pmz1J4UZc+D6ExwjD7vioeGd5HvhvU+R+DZcGZ6YBPNfAi0G97iBPwFXqph2cW8+D7kjMfwtinHb6kLb6Wygk3cZytSEoptGrlScdHtLPeri1JKueACMZfU1ViJG1Sq5E43dIt7SZZFl1zuRhb/GOs44xFVDbrJzB5tYs35OmaXTrEmkv0DajnMWQB42mNgYNCCwk0MLxheMPrhgUuY2JiUmOqY2pjWMD1hdmPOY+5hPsLCwWLEksSyiOUOawzrLrYiti/sCuxJ7Kc45DiSOPZxmnG2cG7jvMelweXDNYXrEbcBdxf3KR4OngheLd443g18fHwZfFv4NfiX8T8TEBIIEZggsEpQS7BMcJsQl5CFUI3QAWEp4RLhCyJaIldEbURXiJ4RYxEzE0sQ2yD2TzxIfJkEk4SeRJbENIkNEg8k/klqSGZITpE8InlL8p2UmVSG1A6pb9Jx0ltkjGSmyDySlZF1kc2RnSK7R/aZnJ5cmdwB+ST5SwpuCvsUjRTLFHcoOShNU9qhzKespGyhXKV8SPmBCpOKgUqcyjSVR6omqgmqe9RE1OrUnqkHqO9R/6FholGgsUZzgeYZLTUtL60WbS7tKh0OnQydXTpvdGV0O3S/6Gnopekt0ruhz6fvpl+nv0n/h4GdQYvBJUMhwwTDdYYvjFSM4oxmGd0zVjK2M84w3mYiYZJgssLkkqmO6TzTF2Z2ZjVmd8ylzP3MJ5lfsRCwcLJoszhhyWXpZdlhecZKxirHapbVPesF1ndsJGwCbBbZ/LA1sn1jZ2XXY3fFXsM+z36V/S8HD4cGh2OOTI51ThJOK5zeOUs4OzmXOS9wPuUi4JLgss7lm2uU6zY3NrcSty1u39zN3Mvct7l/8xDzMPLw88jyaPM44ynkaeEZ59niucqLyUvPKwgAn3OqOQAAAQAAARcApwARAAAAAAACAAAAAQABAAAAQAAuAAAAAHjarZK9TgJBEMf/d6CRaAyRhMLqCgsbL4ciglTGRPEjSiSKlnLycXJ86CEniU/hM9jYWPgIFkYfwd6nsDD+d1mBIIUx3mZnfzs3MzszuwDCeIYG8UUwQxmAFgxxPeeuyxrmcaNYxzTuFAewi0fFQSTxqXgM11pC8TgS2oPiCUS1d8Uh8ofiSczpYcVT5LjiCPlY8Qui+ncOr7D02y6/BTCrP/m+b5bdTrPi2I26Z9qNGtbRQBMdXMJBGRW0YOCecxEWYoiTCvxrYBunqHPdoX2bLOyrMKlZg8thDETw5K7Itci1TXlGy0124QRZZLDFU/exhxztMozlosTpMH6ZPge0L+OKGnFKjJ4WRwppHPL0PP3SI2P9jLQwFOu3GRhDfkeyDo//G7IHgzllZQxLdquvrdCyBVvat3seJlYo06gxapUxhU2JWnFygR03sSxnEkvcpf5Y5eibGq315TDp7fKWm8zbUVl71Aqq/ZtNnlkWmLnQtno9ycvXYbA6W2pF3aKfCayyC0Ja7Fr/PW70/HO4YM0OKxFvzf0C1MyPjwAAeNpt1VWUU2cYRuHsgxenQt1d8/3JOUnqAyR1d/cCLQVKO22pu7tQd3d3d3d3d3cXmGzumrWy3pWLs/NdPDMpZaWu1783l1Lpf14MnfzO6FbqVupfGkD30iR60JNe9KYP09CXfvRnAAMZxG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content: ""; +.tabset-dropdown > .nav-tabs > li.active:before, .tabset-dropdown > .nav-tabs.nav-tabs-open:before { + content: "\e259"; font-family: 'Glyphicons Halflings'; display: inline-block; padding: 10px; @@ -1459,16 +1459,9 @@ pre code { } .tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before { - content: ""; - border: none; -} - -.tabset-dropdown > .nav-tabs.nav-tabs-open:before { - content: ""; + content: "\e258"; font-family: 'Glyphicons Halflings'; - display: inline-block; - padding: 10px; - border-right: 1px solid #ddd; + border: none; } .tabset-dropdown > .nav-tabs > li.active { @@ -1599,14 +1592,20 @@ div.tocify { <h1 class="title toc-ignore">Benchmark timings for mkin</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 14 July 2022 (rebuilt 2022-11-15)</h4> +<h4 class="date">Last change 14 July 2022 (rebuilt 2023-01-05)</h4> </div> -<p>Each system is characterized by the operating system type, the CPU type, the mkin version, and, as in June 2022 the current R version lead to worse performance, the R version. A compiler was available, so if no analytical solution was available, compiled ODE models are used.</p> -<p>Every fit is only performed once, so the accuracy of the benchmarks is limited.</p> -<p>The following wrapper function for <code>mmkin</code> is used because the way the error model is specified was changed in mkin version 0.9.49.1.</p> +<p>Each system is characterized by the operating system type, the CPU +type, the mkin version, and, as in June 2022 the current R version lead +to worse performance, the R version. A compiler was available, so if no +analytical solution was available, compiled ODE models are used.</p> +<p>Every fit is only performed once, so the accuracy of the benchmarks +is limited.</p> +<p>The following wrapper function for <code>mmkin</code> is used because +the way the error model is specified was changed in mkin version +0.9.49.1.</p> <pre class="r"><code>if (packageVersion("mkin") > "0.9.48.1") { mmkin_bench <- function(models, datasets, error_model = "const") { mmkin(models, datasets, error_model = error_model, cores = 1, quiet = TRUE) @@ -1672,11 +1671,22 @@ t11 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c), </div> <div id="results" class="section level2"> <h2>Results</h2> -<p>Benchmarks for all available error models are shown. They are intended for improving mkin, not for comparing CPUs or operating systems. All trademarks belong to their respective owners.</p> +<p>Benchmarks for all available error models are shown. They are +intended for improving mkin, not for comparing CPUs or operating +systems. All trademarks belong to their respective owners.</p> <div id="parent-only" class="section level3"> <h3>Parent only</h3> -<p>Constant variance (t1) and two-component error model (t2) for four models fitted to two datasets, i.e. eight fits for each test.</p> +<p>Constant variance (t1) and two-component error model (t2) for four +models fitted to two datasets, i.e. eight fits for each test.</p> <table> +<colgroup> +<col width="9%" /> +<col width="48%" /> +<col width="9%" /> +<col width="13%" /> +<col width="9%" /> +<col width="10%" /> +</colgroup> <thead> <tr class="header"> <th align="left">OS</th> @@ -1829,24 +1839,43 @@ t11 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c), <td align="left">Ryzen 7 1700</td> <td align="left">4.2.2</td> <td align="left">1.2.0</td> -<td align="right">2.129</td> -<td align="right">3.784</td> +<td align="right">2.140</td> +<td align="right">3.774</td> </tr> <tr class="odd"> <td align="left">Linux</td> <td align="left">Ryzen 7 1700</td> <td align="left">4.2.2</td> -<td align="left">1.3.0</td> -<td align="right">2.046</td> -<td align="right">3.693</td> +<td align="left">1.2.2</td> +<td align="right">2.187</td> +<td align="right">3.851</td> +</tr> +<tr class="even"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X 16-Core Processor</td> +<td align="left">4.2.2</td> +<td align="left">1.2.2</td> +<td align="right">1.308</td> +<td align="right">1.793</td> </tr> </tbody> </table> </div> <div id="one-metabolite" class="section level3"> <h3>One metabolite</h3> -<p>Constant variance (t3), two-component error model (t4), and variance by variable (t5) for three models fitted to one dataset, i.e. three fits for each test.</p> +<p>Constant variance (t3), two-component error model (t4), and variance +by variable (t5) for three models fitted to one dataset, i.e. three fits +for each test.</p> <table> +<colgroup> +<col width="8%" /> +<col width="44%" /> +<col width="8%" /> +<col width="12%" /> +<col width="8%" /> +<col width="9%" /> +<col width="8%" /> +</colgroup> <thead> <tr class="header"> <th align="left">OS</th> @@ -2017,26 +2046,49 @@ t11 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c), <td align="left">Ryzen 7 1700</td> <td align="left">4.2.2</td> <td align="left">1.2.0</td> -<td align="right">1.559</td> -<td align="right">6.097</td> -<td align="right">2.841</td> +<td align="right">1.554</td> +<td align="right">6.193</td> +<td align="right">2.843</td> </tr> <tr class="odd"> <td align="left">Linux</td> <td align="left">Ryzen 7 1700</td> <td align="left">4.2.2</td> -<td align="left">1.3.0</td> +<td align="left">1.2.2</td> +<td align="right">1.585</td> +<td align="right">6.335</td> +<td align="right">3.003</td> +</tr> +<tr class="even"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X 16-Core Processor</td> +<td align="left">4.2.2</td> +<td align="left">1.2.2</td> +<td align="right">0.783</td> +<td align="right">2.364</td> <td align="right">1.230</td> -<td align="right">4.333</td> -<td align="right">2.187</td> </tr> </tbody> </table> </div> <div id="two-metabolites" class="section level3"> <h3>Two metabolites</h3> -<p>Constant variance (t6 and t7), two-component error model (t8 and t9), and variance by variable (t10 and t11) for one model fitted to one dataset, i.e. one fit for each test.</p> +<p>Constant variance (t6 and t7), two-component error model (t8 and t9), +and variance by variable (t10 and t11) for one model fitted to one +dataset, i.e. one fit for each test.</p> <table> +<colgroup> +<col width="6%" /> +<col width="35%" /> +<col width="6%" /> +<col width="9%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +<col width="7%" /> +<col width="6%" /> +<col width="7%" /> +</colgroup> <thead> <tr class="header"> <th align="left">OS</th> @@ -2261,24 +2313,36 @@ t11 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c), <td align="left">Ryzen 7 1700</td> <td align="left">4.2.2</td> <td align="left">1.2.0</td> -<td align="right">0.911</td> -<td align="right">1.328</td> -<td align="right">1.519</td> -<td align="right">2.986</td> -<td align="right">1.957</td> -<td align="right">2.769</td> +<td align="right">0.913</td> +<td align="right">1.345</td> +<td align="right">1.539</td> +<td align="right">3.011</td> +<td align="right">1.987</td> +<td align="right">2.802</td> </tr> <tr class="odd"> <td align="left">Linux</td> <td align="left">Ryzen 7 1700</td> <td align="left">4.2.2</td> -<td align="left">1.3.0</td> -<td align="right">0.693</td> -<td align="right">0.996</td> -<td align="right">1.121</td> -<td align="right">2.174</td> -<td align="right">1.427</td> -<td align="right">2.026</td> +<td align="left">1.2.2</td> +<td align="right">0.935</td> +<td align="right">1.381</td> +<td align="right">1.551</td> +<td align="right">3.209</td> +<td align="right">1.976</td> +<td align="right">3.013</td> +</tr> +<tr class="even"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X 16-Core Processor</td> +<td align="left">4.2.2</td> +<td align="left">1.2.2</td> +<td align="right">0.442</td> +<td align="right">0.582</td> +<td align="right">0.658</td> +<td align="right">1.171</td> +<td align="right">0.801</td> +<td align="right">1.093</td> </tr> </tbody> </table> diff --git a/vignettes/web_only/compiled_models.R b/vignettes/web_only/compiled_models.R new file mode 100644 index 00000000..2af1df1c --- /dev/null +++ b/vignettes/web_only/compiled_models.R @@ -0,0 +1,65 @@ +## ---- include = FALSE--------------------------------------------------------- +library(knitr) +opts_chunk$set(tidy = FALSE, cache = FALSE) + +## ----check_gcc, eval = FALSE-------------------------------------------------- +# pkgbuild::has_compiler() + +## ----Rprofile, eval = FALSE--------------------------------------------------- +# Sys.setenv(PATH = paste("C:/Rtools/bin", Sys.getenv("PATH"), sep=";")) + +## ----HOME, eval = FALSE------------------------------------------------------- +# Sys.getenv("HOME") + +## ----create_SFO_SFO----------------------------------------------------------- +library("mkin", quietly = TRUE) +SFO_SFO <- mkinmod( + parent = mkinsub("SFO", "m1"), + m1 = mkinsub("SFO")) +FOCUS_D <- subset(FOCUS_2006_D, value != 0) + +## ----benchmark_SFO_SFO, fig.height = 3, message = FALSE, warning = FALSE------ +if (require(rbenchmark)) { + b.1 <- benchmark( + "deSolve, not compiled" = mkinfit(SFO_SFO, FOCUS_D, + solution_type = "deSolve", + use_compiled = FALSE, quiet = TRUE), + "Eigenvalue based" = mkinfit(SFO_SFO, FOCUS_D, + solution_type = "eigen", quiet = TRUE), + "deSolve, compiled" = mkinfit(SFO_SFO, FOCUS_D, + solution_type = "deSolve", quiet = TRUE), + "analytical" = mkinfit(SFO_SFO, FOCUS_D, + solution_type = "analytical", + use_compiled = FALSE, quiet = TRUE), + replications = 1, order = "relative", + columns = c("test", "replications", "relative", "elapsed")) + print(b.1) +} else { + print("R package rbenchmark is not available") +} + +## ----benchmark_FOMC_SFO, fig.height = 3, warning = FALSE---------------------- +if (require(rbenchmark)) { + FOMC_SFO <- mkinmod( + parent = mkinsub("FOMC", "m1"), + m1 = mkinsub( "SFO")) + + b.2 <- benchmark( + "deSolve, not compiled" = mkinfit(FOMC_SFO, FOCUS_D, + use_compiled = FALSE, quiet = TRUE), + "deSolve, compiled" = mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE), + replications = 1, order = "relative", + columns = c("test", "replications", "relative", "elapsed")) + print(b.2) + factor_FOMC_SFO <- round(b.2["1", "relative"]) +} else { + factor_FOMC_SFO <- NA + print("R package benchmark is not available") +} + +## ----sessionInfo, echo = FALSE------------------------------------------------ +cat(utils::capture.output(utils::sessionInfo())[1:3], sep = "\n") +if(!inherits(try(cpuinfo <- readLines("/proc/cpuinfo")), "try-error")) { + cat(gsub("model name\t: ", "CPU model: ", cpuinfo[grep("model name", cpuinfo)[1]])) +} + diff --git a/vignettes/web_only/compiled_models.html b/vignettes/web_only/compiled_models.html index 31d062bb..6001a2a0 100644 --- a/vignettes/web_only/compiled_models.html +++ b/vignettes/web_only/compiled_models.html @@ -11,18 +11,28 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2020-05-12" /> +<meta name="date" content="2023-01-05" /> <title>Performance benefit by using compiled model definitions in mkin</title> -<script>/*! jQuery v1.11.3 | (c) 2005, 2015 jQuery Foundation, Inc. | jquery.org/license */ -!function(a,b){"object"==typeof module&&"object"==typeof module.exports?module.exports=a.document?b(a,!0):function(a){if(!a.document)throw new Error("jQuery requires a window with a document");return b(a)}:b(a)}("undefined"!=typeof window?window:this,function(a,b){var c=[],d=c.slice,e=c.concat,f=c.push,g=c.indexOf,h={},i=h.toString,j=h.hasOwnProperty,k={},l="1.11.3",m=function(a,b){return new m.fn.init(a,b)},n=/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g,o=/^-ms-/,p=/-([\da-z])/gi,q=function(a,b){return 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) 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ke2C8vfjgV/Y9kkRQOJIn/xM9INZSpiBnqX0Q9GlQPpPKAyO5y+W5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W/N6l54qOynCqD3DpWQ+mpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE+7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv/8lbTIkkYpqKM9VOhp65ktYk+Q46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI+ejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5+7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu+vtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY/iOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v+6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ/qDQK+bfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam+WpHG+0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3+J1eCBay8IYZ0wQRKGAqvCuZ/UgbQPyllosq+XtfKIZOzmeJqRazpmmoP/76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t/I4Jktu0XSgifO2ozFOiwd/0SssJDn0dn4xqk4GDTTKX73/wQyBLdqgJ+Wx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy/qqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh/wkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml/R4yrzow1Q2A5G+kzo/RhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj/UMMc34IBqTKLDTp76WzL/dMjCxK7MjhiGjeYAC/kj/jY/Rde7hpSM1xChrog6yZ7OWTuD56xBJnGFE+pT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G+9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36/dBySis4m9/DR8izaLJW6bWCkVgm5T+ius3ZXq4xI+GnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te/r4dPYMCl5qtiHNTn+TPbh1jCBHH+dMJNhwNgs3nT+OhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF+uRIWyXjQMAs2chdpaKPNaB+kSezYt0+CA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6/TzoA1/ZBG9bIUVHLAbi/kdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh+epgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF+zkJHIA7PwCDk1gGVmGUZSSoPhNf+Tklauz98QofOlCIQ/tCD4dosHYPqtPCXB3agggQQIqQJsSkB+qn0rkQ1toJjON/OtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU+TQ6NIw3ej+AtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb++W6Uk4q6F7/rg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK+EfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l+wM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l+DMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg+EWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb+Mw76Qy29iQ5up/X7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa/xnsMYcIO/vEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz/Chp/Vlpj2P7jJQmQRwGnltkTV5dbF9fE3/fxoSqTROgq9wFUlbuYzYcasE0ouzBo+dDCDzxKAfhbAZYxQiHrLzV2iVexnDX/QnT1fsT/xuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR+CtGdkPwYN2o7DOw/VGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO/f9Qua+pDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD/prQ84B1pVGkIpVUAHCG+iz3Bn3qm2AVrYcYWhock4jso5+J7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk+UILT7+VoX5mdxxA5fS42gISQVi/HTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8/6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk/wdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt+Su9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ/BfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk/gc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B//lHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO+CvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd/p1gi/07h8qfwHrByuSxglc9cI4QIg2oqvC/qm0i7tjPLTgDhoWTAKDO2ONW5oe+/eKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2/j4ODUwRkqrtBBCrDsDpt8jhZdXoy/1BCqw3sSGhgGGy0a5Jw6BP/TExoCmNFYjZl248A0osgPyGEmRA+fAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd/ocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE/EZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ/Ugd/JZQK8lvAm43uDRAbyW8gZ+ZGq0EVerVGUKUSm/Idn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ+QATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm+oOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724+zUQ7+vkCpZB+pGA562hYQiDxHVWOq0oDQl/QsoiY+cuI7iWq/ZIBtHcXJ7kks+h2fCNUPA82BzjnqktNts+RLdk1VSu+tqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy/44XYXdI5noQoRcvjZ1RMPACRqYg2V1+OwOepcOknRLLFdYgTkT5UApt/JhLM3jeFYprZV+Zow2g8fP+U68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr/A1SY9dXFz4RjzoU9ExfJCmx/I9FKEGT3n2cmzl2X42L3Jh+AbQq6sA+Ss1kitoa4TAYgKHaoybHUDJ51oETdeI/9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB+Un44zExFE4vLytcu5NwpWrUxO/0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL+BDqMyaN3RJPh/anbx+Iv+qgQdAa3M9Z5JmvYlh4qop+Ho1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs+gs37sFvi0PPVvA5dnCBgILTwoKd/+DoL9F6inlM7H4rOTzD79KJgKlZO/Zgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno+gBoKVXgIL/VI8dB1O5o/R3Suez/gD7M781ShjKpIIORM/nxG+jjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4/QftDbEn+AucIr1oxrLabRj9q4ae0+fXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd+eNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz/6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT+mAUmiYbV3YQVqFVp9dorv+TsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp+xJyYLv1OsxQCZwTB4a8BZ/5EdxTBJthApqyfd9u3ifr/WILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj/qn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9+W8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4/YtowhEmTs0vrvlD/CrisnoBNDAcUi/teY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O/4DcgV/dZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk+tgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb/n7qwhvGnrHuf5bX6Vh/n3xffU3PeHdR+FA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E+YbfL0adwNtHP7dT7t7b46DVZIkzaRJOM+S6KcrzYVg+T3wSRFRQashjfU18NutrKa/7PXbtuJvpIjbgPeqd+pjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir/8tNXJ/OsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG+FZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx/GdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD/Qi/EmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4/asthNMK5UQKCOhU97oaOYNGsTah+jfCKsZnTRn5TbhFX8ghg8CBYt/BjeYYYUrtUZ5jVij/op7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM+3SW6Opll/wgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy+QbSKVJcqkia+GvDefFwMOmgnD7h81TUtMn+mRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d/QK7Cr4uoCeOQ7/8JfKT77KiDzLImESHw/0wf73QeHu74hxv7uihi4fTX+XEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo/oVH5ww5OzLFsiTPDns7fS6EURSSWd/92BxMYQ8sBaH+j+wthQPdVgDGpTfi+JQIWMD8xKqULliRH01rTeyF8x8q/GBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE+/7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV/yTDRRP8Y2ww5RO6d2D94o+6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt+kqdae76ViWe3STan69yaF9+fESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw/A1zEdjWquIsQXXGIVEH0thC5M+W9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF+RVmI8L4HUYk4x+67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8/p5qIQrEo/H+1l/0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud+tJUfdaZ4CWNijzZtlRa8+CkmO/EwHYfPZFU/hzjFWH7vnzHRMo+aF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce/+/9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs/GWJS6SwEN/ULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e+G2zor8l+YaMxSEomDdLHGcD6YVQPegTaA74H8+V4WvJkFUrjMLGLlvSZQWvi8/QA7yzQ8GPno//5SJHRP/OqKObPCo81s/+6WgLqykYpGAgQZhVDEBPXWgU/WzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M+GWn6ASobIWC+LbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg/kpf3+CnAXKiMgIE8Jk/Mhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo+RJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B+SkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE+VMd3b1fhCynD0pQNhCG6/WCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp/YbHjo++7/Wj8S4YNa+ZdqAw1hDrKWFXv9+zaXpf8ZTDSbiqsxnwN/CzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m/NCW/HILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO+5EJ7Z6bCiRoPedRZ/P0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn/LvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl+11PoFYnNv2HwAODeNRkHj+8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij+bsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur/eWHjiB7SOVdqMAVmpBvfRiebsFjger7DC+8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l+kXRZ0KLZaGsFSIdQz/HXDxf3/TE30+DgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH+RptvRMVRaahu4cySjS3P5wxAUCPkmn+rhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik/zbrTQk5KmgxCg/f45L0jywebOWUYFJQAJia7XzCV0x89rpp/f3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl+5tfUWZNwBpEPXghzbBggYCw/dhy0ntds2yeHCDKkF/YxQjNIL/F/37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD+qNOhwMlfARQUdJ2tUX+MNJqOwIciWalZsmEjt07tfa8ma4cji9sqz+Q9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe+jGDiNJQLWnfx+drTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf/bkvo8PLVBsZl152y5S8+HRDfZIMCbYZ1WDp4yrdchOJw8k6R+/2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB+M4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5/iYp3ZdrCf7fL+en+sIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv+NRiJc8JAKqqgCA/PNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN/hCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj+bYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5/zBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8/i+jHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2+JrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk+Mz7wwstg6RFZb+TZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm/7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk+9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx+whVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC+XYuqMBMUun5YezKbRKmEPwuK+CLzijPEQgfhahQswBBLfg/GBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX+RCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk/4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug/AbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM+Zu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX/pcsfwXbLze2+D+u33OGBoJyAAL3jn3RuEcdp5If8O+a4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT++tm+haBCikRUUMrMhYKZJKYoVuv/bsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV/DMUxd9uFZmBfKXMCn/SqkWJyKPnT6lq+4zBZni6fYRByJn6OK+OgPBGRAJluwGSk4wxjOOzyce/PKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO/cEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm+bddRQu5F9s0XftGEJ9JSuSk+ZachCbdU45fEqbugzTIUokwoAKvpUQF/CvLbWW5BNQFqFkJg2f30E/48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J+1pT0tLkvFVZaNzfhs/Kd3+A9YsImlO4XK4vpCo/elHQi/9gkFg07xxnuXLt21unCIpDV+bbRxb7FC6nWYTsMFF8+1LUg4JFjVt3vqbuhHmDKbgQ4e+RGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB/KVijd1ARWkFEf3yiUw1v/WaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G+ZManTqDLPjyrOse7WiiwOJCG+J0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj/6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j/N46f+S2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb/0jQbaHJ2YRK8A+ls6WMhWmpCXYG5jqapGs5/eOJErxi2/2KWVHiPellTgh/fNl/2KYPKb7DUcAg+mCOPQFCiU9Mq/WLcU1xxC8aLePFZZlE+PCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh/nFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW+oKFLvpyvTBMM69tN1Ydwv1LIEhHsC+ueVG+w+kyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw+H/AuOx+aH+tBL88H57D0MsqyiZxhOEQkF/8DR1d2hSPMj/sNOa5rxcUnBgH8ictv2J+cb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd/PjMMtQfyDNZsOPd6XcAsnBE/mRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl/XPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG/VanIvcwycVA7+BE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP/MVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX/5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c/F1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J/5wkjpkre727p5PTRX5FGrSBIfJqhJE/IS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug+oRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U/5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7/BQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN+nA7pvF78/RII5ZHA09OAiE/66MF6HQ+qVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe+hXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz+JV/4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB+K3wBP/ineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q/9uocGsx41O4IZhViw/2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY/cQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE/om7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi/caHSM3FPRGRf7dB7YC+cD2ho6oL2zGDCkjlf/DFoQVl8GS/56wur3rdV6ggtzZW60MRB3g+U1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg/gBQIZMG/YcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK/G7F3mUc3GOAKqh60zM0v34v+ELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND+/GTk6M56Ig4yMsU6LUW1EWE+fIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP/IoRIZ/F6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg/RrPD/d3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl+Mu4xf0ezqeXD2PtPDnwMPo86sbwDV+9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD/OwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d/UfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH+14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC+OA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6+vAUUBoGhY3CT2tgwehdPqU/4Q7ZLYvhRl/ogOvR9O2+wkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn/dFSWBWzQ/VYk+Gezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n/yJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET/Hh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j+DXfpi12m1RbzYLg9J2wFergEwOxFyD0/JstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT/cUP6pE/mujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB+HEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu+vqQ02+KpJBjaLt9ye1Ab+BbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC+UwkXOoAjneU/xHplMQo2cXUlrVNqJYczgYlaOEczVCs/OCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38+xsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9+b8fH6+b8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj/0Q7PFUcC8hDrxESWdfgFRm+7vvWbkEppHB4T/1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y+g3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR/Rrs/JLKXgEx+qkmeDlFOD1/yTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW+xz+5FElFxWB28VjYIGZ0Yd+5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ+lT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ+2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1+JpI9psyNYIFuJogZa0/1AhOWdlHQxdAgbwacsHqPZo8u/ngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2+RdM+MAaYaZ0Y/ADkbNCZuAyAVQa2OcXOeICmDn9Q/eFkDeFQg5MgHEDXq/tVjj+jtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2/Bc0UxvseQCO2pQ2i+Krfhu/WeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt/U0Wf+phiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7+ExseJauyqo30hs+1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j/e1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la/QCTiVelFnU6O/GCvykqS/wZJDhKN9gBtSOp/1SP5VRgJcoVj+kmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn+8p6+vBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H+gDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D/GvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P+jAgN5TB3haSifDcApp6yymEi6Ij/GsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x/pChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11/yxyw0UnGig3MFdZklN5FI/qiT65T+jOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5+bqWiAYiAv6Jsf79/VUs4cIl+n6+WOjcgB/2l5TreoAV2717JzZbQIR0W1cl/dEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW/PhoawJDrGAP0JYWHgAVUByo/bGdiv2T2EMg8gsS14/rAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq+SCcTSx5NDtbFwNlh8VhjGGDu7JG5/TAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9+ogD8Kk/W7QoRN1UWPqM4+xdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c+4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS+o3F8YVVeikw13w+OEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX++7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9/O53DYi/5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD+P8sNh6e+XYHJXT/lkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp+pT0KYV5dGtknU64JhabdqcVQbG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) 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BkBAXAcIVHfzgDwr+ogoPDwoBXgoPDwqvrwoPAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgUzMhYVETMyFgcDBiInAyY2OwERNDYB4+rWm1tbm9bq1ptbW5sBv+jFcnLF6MVycv58lgoPiRUKDd8NJg3fDQoViQ8EmVub1urWm1tbm9bq1ps7csXoxXJyxejFDQ8K/u0XEP7tEBABExAXARMKDwAAAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JiUTFgYrAREUBisBIiY1ESMiJjcTNjIB4+rWm1tbm9bq1ptbW5sBv+jFcnLF6MVycv7n3w0KFYkPCpYKD4kVCg3fDSYEmVub1urWm1tbm9bq1ps7csXoxXJyxejFAf7tEBf+7QoPDwoBExcQARMQAAAAAAIAAAAABLAEsAAZADkAABMhMhYXExYVERQGBwYjISImJyY1EzQ3Ez4BBSEiBgcDBhY7ATIWHwEeATsBMjY/AT4BOwEyNicDLgHhAu4KEwO6BwgFDBn7tAweAgYBB7kDEwKX/dQKEgJXAgwKlgoTAiYCEwr6ChMCJgITCpYKDAJXAhIEsA4K/XQYGf5XDB4CBggEDRkBqRkYAowKDsgOC/4+Cw4OCpgKDg4KmAoODgsBwgsOAAMAFwAXBJkEmQAPABsAJwAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgUXFhQPAQYmNRE0NgHj6tabW1ub1urWm1tbmwG/6MVycsXoxXJy/ov9ERH9EBgYBJlbm9bq1ptbW5vW6tabO3LF6MVycsXoxV2+DCQMvgwLFQGQFQsAAQAXABcEmQSwACgAAAE3NhYVERQGIyEiJj8BJiMiDgEUHgEyPgE1MxQOAiIuAjQ+AjMyA7OHBwsPCv6WCwQHhW2BdMVycsXoxXKWW5vW6tabW1ub1nXABCSHBwQL/pYKDwsHhUxyxejFcnLFdHXWm1tbm9bq1ptbAAAAAAIAFwABBJkEsAAaADUAAAE3NhYVERQGIyEiJj8BJiMiDgEVIzQ+AjMyEzMUDgIjIicHBiY1ETQ2MyEyFg8BFjMyPgEDs4cHCw8L/pcLBAeGboF0xXKWW5vWdcDrllub1nXAnIYHCw8LAWgKBQiFboJ0xXIEJIcHBAv+lwsPCweGS3LFdHXWm1v9v3XWm1t2hggFCgFoCw8LB4VMcsUAAAAKAGQAAASwBLAADwAfAC8APwBPAF8AbwB/AI8AnwAAEyEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0NgczMhYdARQGKwEiJj0BNDYzITIWHQEUBiMhIiY9ATQ2BzMyFh0BFAYrASImPQE0NjMhMhYdARQGIyEiJj0BNDYHMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0Nn0EGgoPDwr75goPDwPA/K4KDw8KA1IKDw/9CDIKDw8KMgoPD9IBwgoPDwr+PgoPD74yCg8PCjIKDw/SAcIKDw8K/j4KDw++MgoPDwoyCg8P0gHCCg8PCv4+Cg8PvjIKDw8KMgoPD9IBwgoPDwr+PgoPDwSwDwr7ggoPDwoEfgoPyA8K/K4KDw8KA1IKD2QPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKDwAAAAACAAAAAARMBLAAGQAjAAABNTQmIyEiBh0BIyIGFREUFjMhMjY1ETQmIyE1NDY7ATIWHQEDhHVT/tRSdmQpOzspA4QpOzsp/ageFMgUHgMgyFN1dlLIOyn9qCk7OykCWCk7lhUdHRWWAAIAZAAABEwETAAJADcAABMzMhYVESMRNDYFMhcWFREUBw4DIyIuAScuAiMiBwYjIicmNRE+ATc2HgMXHgIzMjc2fTIKD2QPA8AEBRADIUNAMRwaPyonKSxHHlVLBwgGBQ4WeDsXKC4TOQQpLUUdZ1AHBEwPCvvNBDMKDzACBhH+WwYGO1AkDQ0ODg8PDzkFAwcPAbY3VwMCAwsGFAEODg5XCAAAAwAAAAAEsASXACEAMQBBAAAAMh4CFREUBisBIiY1ETQuASAOARURFAYrASImNRE0PgEDMzIWFREUBisBIiY1ETQ2ITMyFhURFAYrASImNRE0NgHk6N6jYw8KMgoPjeT++uSNDwoyCg9joyqgCAwMCKAIDAwCYKAIDAwIoAgMDASXY6PedP7UCg8PCgEsf9FyctF//tQKDw8KASx03qP9wAwI/jQIDAwIAcwIDAwI/jQIDAwIAcwIDAAAAAACAAAA0wRHA90AFQA5AAABJTYWFREUBiclJisBIiY1ETQ2OwEyBTc2Mh8BFhQPARcWFA8BBiIvAQcGIi8BJjQ/AScmND8BNjIXAUEBAgkMDAn+/hUZ+goPDwr6GQJYeAcUByIHB3h4BwciBxQHeHgHFAciBwd3dwcHIgcUBwMurAYHCv0SCgcGrA4PCgFeCg+EeAcHIgcUB3h4BxQHIgcHd3cHByIHFAd4eAcUByIICAAAAAACAAAA0wNyA90AFQAvAAABJTYWFREUBiclJisBIiY1ETQ2OwEyJTMWFxYVFAcGDwEiLwEuATc2NTQnJjY/ATYBQQECCQwMCf7+FRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcDLqwGBwr9EgoHBqwODwoBXgoPZAEJgaGafwkBAQYXBxMIZ36EaggUBxYFAAAAAAMAAADEBGID7AAbADEASwAAATMWFxYVFAYHBgcjIi8BLgE3NjU0JicmNj8BNgUlNhYVERQGJyUmKwEiJjURNDY7ATIlMxYXFhUUBwYPASIvAS4BNzY1NCcmNj8BNgPHAwsGh0RABwoDCQcqCAIGbzs3BgIJKgf9ggECCQwMCf7+FRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcD7AEJs9lpy1QJAQYiBhQIlrJarEcJFAYhBb6sBgcK/RIKBwasDg8KAV4KD2QBCYGhmn8JAQEGFwcTCGd+hGoIFQYWBQAAAAANAAAAAASwBLAACQAVABkAHQAhACUALQA7AD8AQwBHAEsATwAAATMVIxUhFSMRIQEjFTMVIREjESM1IQURIREhESERBSM1MwUjNTMBMxEhETM1MwEzFSMVIzUjNTM1IzUhBREhEQcjNTMFIzUzASM1MwUhNSEB9GRk/nBkAfQCvMjI/tTIZAJY+7QBLAGQASz84GRkArxkZP1EyP4MyGQB9MhkyGRkyAEs/UQBLGRkZAOEZGT+DGRkAfT+1AEsA4RkZGQCWP4MZMgBLAEsyGT+1AEs/tQBLMhkZGT+DP4MAfRk/tRkZGRkyGTI/tQBLMhkZGT+1GRkZAAAAAAJAAAAAASwBLAAAwAHAAsADwATABcAGwAfACMAADcjETMTIxEzASMRMxMjETMBIxEzASE1IRcjNTMXIzUzBSM1M2RkZMhkZAGQyMjIZGQBLMjI/OD+1AEsyGRkyGRkASzIyMgD6PwYA+j8GAPo/BgD6PwYA+j7UGRkW1tbW1sAAAIAAAAKBKYEsAANABUAAAkBFhQHAQYiJwETNDYzBCYiBhQWMjYB9AKqCAj+MAgUCP1WAQ8KAUM7Uzs7UzsEsP1WCBQI/jAICAKqAdsKD807O1Q7OwAAAAADAAAACgXSBLAADQAZACEAAAkBFhQHAQYiJwETNDYzIQEWFAcBBiIvAQkBBCYiBhQWMjYB9AKqCAj+MAgUCP1WAQ8KAwYCqggI/jAIFAg4Aaj9RP7TO1M7O1M7BLD9VggUCP4wCAgCqgHbCg/9VggUCP4wCAg4AaoCvM07O1Q7OwAAAAABAGQAAASwBLAAJgAAASEyFREUDwEGJjURNCYjISIPAQYWMyEyFhURFAYjISImNRE0PwE2ASwDOUsSQAgKDwr9RBkSQAgFCgK8Cg8PCvyuCg8SixIEsEv8fBkSQAgFCgO2Cg8SQAgKDwr8SgoPDwoDzxkSixIAAAABAMj//wRMBLAACgAAEyEyFhURCQERNDb6AyAVHf4+/j4dBLAdFfuCAbz+QwR/FR0AAAAAAwAAAAAEsASwABUARQBVAAABISIGBwMGHwEeATMhMjY/ATYnAy4BASMiBg8BDgEjISImLwEuASsBIgYVERQWOwEyNj0BNDYzITIWHQEUFjsBMjY1ETQmASEiBg8BBhYzITI2LwEuAQM2/kQLEAFOBw45BhcKAcIKFwY+DgdTARABVpYKFgROBBYK/doKFgROBBYKlgoPDwqWCg8PCgLuCg8PCpYKDw/+sf4MChMCJgILCgJYCgsCJgITBLAPCv7TGBVsCQwMCWwVGAEtCg/+cA0JnAkNDQmcCQ0PCv12Cg8PCpYKDw8KlgoPDwoCigoP/agOCpgKDg4KmAoOAAAAAAQAAABkBLAETAAdACEAKQAxAAABMzIeAh8BMzIWFREUBiMhIiY1ETQ2OwE+BAEVMzUEIgYUFjI2NCQyFhQGIiY0AfTIOF00JAcGlik7Oyn8GCk7OymWAgknM10ByGT+z76Hh76H/u9WPDxWPARMKTs7FRQ7Kf2oKTs7KQJYKTsIG0U1K/7UZGRGh76Hh74IPFY8PFYAAAAAAgA1AAAEsASvACAAIwAACQEWFx4BHwEVITUyNi8BIQYHBh4CMxUhNTY3PgE/AQEDIQMCqQGBFCgSJQkK/l81LBFS/nk6IgsJKjIe/pM4HAwaBwcBj6wBVKIEr/waMioTFQECQkJXLd6RWSIuHAxCQhgcDCUNDQPu/VoByQAAAAADAGQAAAPwBLAAJwAyADsAAAEeBhUUDgMjITU+ATURNC4EJzUFMh4CFRQOAgclMzI2NTQuAisBETMyNjU0JisBAvEFEzUwOyodN1htbDD+DCk7AQYLFyEaAdc5dWM+Hy0tEP6Pi05pESpTPnbYUFJ9Xp8CgQEHGB0zOlIuQ3VONxpZBzMoAzsYFBwLEAkHRwEpSXNDM1s6KwkxYUopOzQb/K5lUFqBAAABAMgAAANvBLAAGQAAARcOAQcDBhYXFSE1NjcTNjQuBCcmJzUDbQJTQgeECSxK/gy6Dq0DAw8MHxUXDQYEsDkTNSj8uTEoBmFhEFIDQBEaExAJCwYHAwI5AAAAAAL/tQAABRQEsAAlAC8AAAEjNC4FKwERFBYfARUhNTI+AzURIyIOBRUjESEFIxEzByczESM3BRQyCAsZEyYYGcgyGRn+cAQOIhoWyBkYJhMZCwgyA+j7m0tLfX1LS30DhBUgFQ4IAwH8rhYZAQJkZAEFCRUOA1IBAwgOFSAVASzI/OCnpwMgpwACACH/tQSPBLAAJQAvAAABIzQuBSsBERQWHwEVITUyPgM1ESMiDgUVIxEhEwc1IRUnNxUhNQRMMggLGRMmGBnIMhkZ/nAEDiIaFsgZGCYTGQsIMgPoQ6f84KenAyADhBUgFQ4IAwH9dhYZAQJkZAEFCRUOAooBAwgOFSAVASz7gn1LS319S0sABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyAlgVHR0V/agVHR0VA+gVHR0V/BgVHR0VAyAVHR0V/OAVHR0VBEwVHR0V+7QVHR0ETB0VZBUdHRVkFR3+1B0VZBUdHRVkFR3+1B0VZBUdHRVkFR3+1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYDITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NgMhMhYdARQGIyEiJj0BNDb6ArwVHR0V/UQVHR2zBEwVHR0V+7QVHR3dArwVHR0V/UQVHR2zBEwVHR0V+7QVHR0ETB0VZBUdHRVkFR3+1B0VZBUdHRVkFR3+1B0VZBUdHRVkFR3+1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AAAE1NDYzITIWHQEUBiMhIiYBNTQ2MyEyFh0BFAYjISImEzU0NjMhMhYdARQGIyEiJgE1NDYzITIWHQEUBiMhIiYB9B0VAlgVHR0V/agVHf5wHRUD6BUdHRX8GBUdyB0VAyAVHR0V/OAVHf7UHRUETBUdHRX7tBUdA7ZkFR0dFWQVHR3+6WQVHR0VZBUdHf7pZBUdHRVkFR0d/ulkFR0dFWQVHR0AAAQAAAAABLAETAAPAB8ALwA/AAATITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2MgRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dBEwdFWQVHR0VZBUd/tQdFWQVHR0VZBUd/tQdFWQVHR0VZBUd/tQdFWQVHR0VZBUdAAgAAAAABLAETAAPAB8ALwA/AE8AXwBvAH8AABMzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2MmQVHR0VZBUdHQFBAyAVHR0V/OAVHR3+6WQVHR0VZBUdHQFBAyAVHR0V/OAVHR3+6WQVHR0VZBUdHQFBAyAVHR0V/OAVHR3+6WQVHR0VZBUdHQFBAyAVHR0V/OAVHR0ETB0VZBUdHRVkFR0dFWQVHR0VZBUd/tQdFWQVHR0VZBUdHRVkFR0dFWQVHf7UHRVkFR0dFWQVHR0VZBUdHRVkFR3+1B0VZBUdHRVkFR0dFWQVHR0VZBUdAAAG/5wAAASwBEwAAwATACMAKgA6AEoAACEjETsCMhYdARQGKwEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2BQc1IzUzNQUhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2AZBkZJZkFR0dFWQVHR0VAfQVHR0V/gwVHR3++qfIyAHCASwVHR0V/tQVHR0VAlgVHR0V/agVHR0ETB0VZBUdHRVkFR3+1B0VZBUdHRVkFR36fUtkS68dFWQVHR0VZBUd/tQdFWQVHR0VZBUdAAAABgAAAAAFFARMAA8AEwAjACoAOgBKAAATMzIWHQEUBisBIiY9ATQ2ASMRMwEhMhYdARQGIyEiJj0BNDYFMxUjFSc3BSEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyZBUdHRVkFR0dA2dkZPyuAfQVHR0V/gwVHR0EL8jIp6f75gEsFR0dFf7UFR0dFQJYFR0dFf2oFR0dBEwdFWQVHR0VZBUd+7QETP7UHRVkFR0dFWQVHchkS319rx0VZBUdHRVkFR3+1B0VZBUdHRVkFR0AAAAAAgAAAMgEsAPoAA8AEgAAEyEyFhURFAYjISImNRE0NgkCSwLuHywsH/0SHywsBIT+1AEsA+gsH/12HywsHwKKHyz9RAEsASwAAwAAAAAEsARMAA8AFwAfAAATITIWFREUBiMhIiY1ETQ2FxE3BScBExEEMhYUBiImNCwEWBIaGhL7qBIaGkr3ASpKASXs/NJwTk5wTgRMGhL8DBIaGhID9BIaZP0ftoOcAT7+4AH0dE5vT09vAAAAAAIA2wAFBDYEkQAWAB4AAAEyHgEVFAcOAQ8BLgQnJjU0PgIWIgYUFjI2NAKIdcZzRkWyNjYJIV5YbSk8RHOft7eCgreCBJF4ynVzj23pPz4IIWZomEiEdVijeUjDgriBgbgAAAACABcAFwSZBJkADwAXAAAAMh4CFA4CIi4CND4BAREiDgEUHgEB4+rWm1tbm9bq1ptbW5sBS3TFcnLFBJlbm9bq1ptbW5vW6tab/G8DVnLF6MVyAAACAHUAAwPfBQ8AGgA1AAABHgYVFA4DBy4DNTQ+BQMOAhceBBcWNj8BNiYnLgInJjc2IyYCKhVJT1dOPiUzVnB9P1SbfEokP0xXUEm8FykoAwEbITEcExUWAgYCCQkFEikMGiACCAgFD0iPdXdzdYdFR4BeRiYEBTpjl1lFh3ZzeHaQ/f4hS4I6JUEnIw4IBwwQIgoYBwQQQSlZtgsBAAAAAwAAAAAEywRsAAwAKgAvAAABNz4CHgEXHgEPAiUhMhcHISIGFREUFjMhMjY9ATcRFAYjISImNRE0NgkBBzcBA+hsAgYUFR0OFgoFBmz9BQGQMje7/pApOzspAfQpO8i7o/5wpbm5Azj+lqE3AWMD9XMBAgIEDw4WKgsKc8gNuzsp/gwpOzsptsj+tKW5uaUBkKW5/tf+ljKqAWMAAgAAAAAEkwRMABsANgAAASEGByMiBhURFBYzITI2NTcVFAYjISImNRE0NgUBFhQHAQYmJzUmDgMHPgY3NT4BAV4BaaQ0wyk7OykB9Ck7yLml/nClubkCfwFTCAj+rAcLARo5ZFRYGgouOUlARioTAQsETJI2Oyn+DCk7OymZZ6W5uaUBkKW5G/7TBxUH/s4GBAnLAQINFjAhO2JBNB0UBwHSCgUAAAAAAgAAAAAEnQRMAB0ANQAAASEyFwchIgYVERQWMyEyNj0BNxUUBiMhIiY1ETQ2CQE2Mh8BFhQHAQYiLwEmND8BNjIfARYyAV4BXjxDsv6jKTs7KQH0KTvIuaX+cKW5uQHKAYsHFQdlBwf97QcVB/gHB2UHFQdvCBQETBexOyn+DCk7OylFyNulubmlAZCluf4zAYsHB2UHFQf97AcH+AcVB2UHB28HAAAAAQAKAAoEpgSmADsAAAkBNjIXARYGKwEVMzU0NhcBFhQHAQYmPQEjFTMyFgcBBiInASY2OwE1IxUUBicBJjQ3ATYWHQEzNSMiJgE+AQgIFAgBBAcFCqrICggBCAgI/vgICsiqCgUH/vwIFAj++AgFCq/ICgj++AgIAQgICsivCgUDlgEICAj++AgKyK0KBAf+/AcVB/73BwQKrcgKCP74CAgBCAgKyK0KBAcBCQcVBwEEBwQKrcgKAAEAyAAAA4QETAAZAAATMzIWFREBNhYVERQGJwERFAYrASImNRE0NvpkFR0B0A8VFQ/+MB0VZBUdHQRMHRX+SgHFDggV/BgVCA4Bxf5KFR0dFQPoFR0AAAABAAAAAASwBEwAIwAAEzMyFhURATYWFREBNhYVERQGJwERFAYnAREUBisBIiY1ETQ2MmQVHQHQDxUB0A8VFQ/+MBUP/jAdFWQVHR0ETB0V/koBxQ4IFf5KAcUOCBX8GBUIDgHF/koVCA4Bxf5KFR0dFQPoFR0AAAABAJ0AGQSwBDMAFQAAAREUBicBERQGJwEmNDcBNhYVEQE2FgSwFQ/+MBUP/hQPDwHsDxUB0A8VBBr8GBUIDgHF/koVCA4B4A4qDgHgDggV/koBxQ4IAAAAAQDIABYEMwQ2AAsAABMBFhQHAQYmNRE0NvMDLhIS/NISGRkEMv4OCx4L/g4LDhUD6BUOAAIAyABkA4QD6AAPAB8AABMzMhYVERQGKwEiJjURNDYhMzIWFREUBisBIiY1ETQ2+sgVHR0VyBUdHQGlyBUdHRXIFR0dA+gdFfzgFR0dFQMgFR0dFfzgFR0dFQMgFR0AAAEAyABkBEwD6AAPAAABERQGIyEiJjURNDYzITIWBEwdFfzgFR0dFQMgFR0DtvzgFR0dFQMgFR0dAAAAAAEAAAAZBBMEMwAVAAABETQ2FwEWFAcBBiY1EQEGJjURNDYXAfQVDwHsDw/+FA8V/jAPFRUPAmQBthUIDv4gDioO/iAOCBUBtv47DggVA+gVCA4AAAH//gACBLMETwAjAAABNzIWFRMUBiMHIiY1AwEGJjUDAQYmNQM0NhcBAzQ2FwEDNDYEGGQUHgUdFWQVHQL+MQ4VAv4yDxUFFQ8B0gIVDwHSAh0ETgEdFfwYFR0BHRUBtf46DwkVAbX+OQ4JFAPoFQkP/j4BthQJDv49AbYVHQAAAQEsAAAD6ARMABkAAAEzMhYVERQGKwEiJjURAQYmNRE0NhcBETQ2A1JkFR0dFWQVHf4wDxUVDwHQHQRMHRX8GBUdHRUBtv47DggVA+gVCA7+OwG2FR0AAAIAZADIBLAESAALABsAAAkBFgYjISImNwE2MgEhMhYdARQGIyEiJj0BNDYCrgH1DwkW++4WCQ8B9Q8q/fcD6BUdHRX8GBUdHQQ5/eQPFhYPAhwP/UgdFWQVHR0VZBUdAAEAiP/8A3UESgAFAAAJAgcJAQN1/qABYMX92AIoA4T+n/6fxgIoAiYAAAAAAQE7//wEKARKAAUAAAkBJwkBNwQo/dnGAWH+n8YCI/3ZxgFhAWHGAAIAFwAXBJkEmQAPADMAAAAyHgIUDgIiLgI0PgEFIyIGHQEjIgYdARQWOwEVFBY7ATI2PQEzMjY9ATQmKwE1NCYB4+rWm1tbm9bq1ptbW5sBfWQVHZYVHR0Vlh0VZBUdlhUdHRWWHQSZW5vW6tabW1ub1urWm7odFZYdFWQVHZYVHR0Vlh0VZBUdlhUdAAAAAAIAFwAXBJkEmQAPAB8AAAAyHgIUDgIiLgI0PgEBISIGHQEUFjMhMjY9ATQmAePq1ptbW5vW6tabW1ubAkX+DBUdHRUB9BUdHQSZW5vW6tabW1ub1urWm/5+HRVkFR0dFWQVHQACABcAFwSZBJkADwAzAAAAMh4CFA4CIi4CND4BBCIPAScmIg8BBhQfAQcGFB8BFjI/ARcWMj8BNjQvATc2NC8BAePq1ptbW5vW6tabW1ubAeUZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0JCXh4CQmNBJlbm9bq1ptbW5vW6tabrQl4eAkJjQkZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0AAgAXABcEmQSZAA8AJAAAADIeAhQOAiIuAjQ+AQEnJiIPAQYUHwEWMjcBNjQvASYiBwHj6tabW1ub1urWm1tbmwEVVAcVCIsHB/IHFQcBdwcHiwcVBwSZW5vW6tabW1ub1urWm/4xVQcHiwgUCPEICAF3BxUIiwcHAAAAAAMAFwAXBJkEmQAPADsASwAAADIeAhQOAiIuAjQ+AQUiDgMVFDsBFjc+ATMyFhUUBgciDgUHBhY7ATI+AzU0LgMTIyIGHQEUFjsBMjY9ATQmAePq1ptbW5vW6tabW1ubAT8dPEIyIRSDHgUGHR8UFw4TARkOGhITDAIBDQ6tBx4oIxgiM0Q8OpYKDw8KlgoPDwSZW5vW6tabW1ub1urWm5ELHi9PMhkFEBQQFRIXFgcIBw4UHCoZCBEQKDhcNi9IKhsJ/eMPCpYKDw8KlgoPAAADABcAFwSZBJkADwAfAD4AAAAyHgIUDgIiLgI0PgEFIyIGHQEUFjsBMjY9ATQmAyMiBh0BFBY7ARUjIgYdARQWMyEyNj0BNCYrARE0JgHj6tabW1ub1urWm1tbmwGWlgoPDwqWCg8PCvoKDw8KS0sKDw8KAV4KDw8KSw8EmVub1urWm1tbm9bq1ptWDwqWCg8PCpYKD/7UDwoyCg/IDwoyCg8PCjIKDwETCg8AAgAAAAAEsASwAC8AXwAAATMyFh0BHgEXMzIWHQEUBisBDgEHFRQGKwEiJj0BLgEnIyImPQE0NjsBPgE3NTQ2ExUUBisBIiY9AQ4BBzMyFh0BFAYrAR4BFzU0NjsBMhYdAT4BNyMiJj0BNDY7AS4BAg2WCg9nlxvCCg8PCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw+5DwqWCg9EZheoCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmBLAPCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw8KlgoPZ5cbwgoP/s2oCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmRA8KlgoPRGYAAwAXABcEmQSZAA8AGwA/AAAAMh4CFA4CIi4CND4BBCIOARQeATI+ATQmBxcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyAePq1ptbW5vW6tabW1ubAb/oxXJyxejFcnKaQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwdABxUHfHwHFQSZW5vW6tabW1ub1urWmztyxejFcnLF6MVaQAcVB3x8BxUHQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwAAAAMAFwAXBJkEmQAPABsAMAAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgcXFhQHAQYiLwEmND8BNjIfATc2MgHj6tabW1ub1urWm1tbmwG/6MVycsXoxXJyg2oHB/7ACBQIyggIagcVB0/FBxUEmVub1urWm1tbm9bq1ps7csXoxXJyxejFfWoHFQf+vwcHywcVB2oICE/FBwAAAAMAFwAXBJkEmQAPABgAIQAAADIeAhQOAiIuAjQ+AQUiDgEVFBcBJhcBFjMyPgE1NAHj6tabW1ub1urWm1tbmwFLdMVyQQJLafX9uGhzdMVyBJlbm9bq1ptbW5vW6tabO3LFdHhpAktB0P24PnLFdHMAAAAAAQAXAFMEsAP5ABUAABMBNhYVESEyFh0BFAYjIREUBicBJjQnAgoQFwImFR0dFf3aFxD99hACRgGrDQoV/t0dFcgVHf7dFQoNAasNJgAAAAABAAAAUwSZA/kAFQAACQEWFAcBBiY1ESEiJj0BNDYzIRE0NgJ/AgoQEP32EBf92hUdHRUCJhcD8f5VDSYN/lUNChUBIx0VyBUdASMVCgAAAAEAtwAABF0EmQAVAAAJARYGIyERFAYrASImNREhIiY3ATYyAqoBqw0KFf7dHRXIFR3+3RUKDQGrDSYEif32EBf92hUdHRUCJhcQAgoQAAAAAQC3ABcEXQSwABUAAAEzMhYVESEyFgcBBiInASY2MyERNDYCJsgVHQEjFQoN/lUNJg3+VQ0KFQEjHQSwHRX92hcQ/fYQEAIKEBcCJhUdAAABAAAAtwSZBF0AFwAACQEWFAcBBiY1EQ4DBz4ENxE0NgJ/AgoQEP32EBdesKWBJAUsW4fHfhcEVf5VDSYN/lUNChUBIwIkRHVNabGdcUYHAQYVCgACAAAAAASwBLAAFQArAAABITIWFREUBi8BBwYiLwEmND8BJyY2ASEiJjURNDYfATc2Mh8BFhQPARcWBgNSASwVHRUOXvkIFAhqBwf5Xg4I/iH+1BUdFQ5e+QgUCGoHB/leDggEsB0V/tQVCA5e+QcHaggUCPleDhX7UB0VASwVCA5e+QcHaggUCPleDhUAAAACAEkASQRnBGcAFQArAAABFxYUDwEXFgYjISImNRE0Nh8BNzYyASEyFhURFAYvAQcGIi8BJjQ/AScmNgP2agcH+V4OCBX+1BUdFQ5e+QgU/QwBLBUdFQ5e+QgUCGoHB/leDggEYGoIFAj5Xg4VHRUBLBUIDl75B/3xHRX+1BUIDl75BwdqCBQI+V4OFQAAAAADABcAFwSZBJkADwAfAC8AAAAyHgIUDgIiLgI0PgEFIyIGFxMeATsBMjY3EzYmAyMiBh0BFBY7ATI2PQE0JgHj6tabW1ub1urWm1tbmwGz0BQYBDoEIxQ2FCMEOgQYMZYKDw8KlgoPDwSZW5vW6tabW1ub1urWm7odFP7SFB0dFAEuFB3+DA8KlgoPDwqWCg8AAAAABQAAAAAEsASwAEkAVQBhAGgAbwAAATIWHwEWHwEWFxY3Nj8BNjc2MzIWHwEWHwIeATsBMhYdARQGKwEiBh0BIREjESE1NCYrASImPQE0NjsBMjY1ND8BNjc+BAUHBhY7ATI2LwEuAQUnJgYPAQYWOwEyNhMhIiY1ESkBERQGIyERAQQJFAUFFhbEFQ8dCAsmxBYXERUXMA0NDgQZCAEPCj0KDw8KMgoP/nDI/nAPCjIKDw8KPQsOCRkFDgIGFRYfAp2mBwQK2woKAzMDEP41sQgQAzMDCgrnCwMe/okKDwGQAlgPCv6JBLAEAgIKDXYNCxUJDRZ2DQoHIREQFRh7LAkLDwoyCg8PCq8BLP7UrwoPDwoyCg8GBQQwgBkUAwgWEQ55ogcKDgqVCgSqnQcECo8KDgr8cg8KAXf+iQoPAZAAAAAAAgAAAAwErwSmACsASQAAATYWFQYCDgQuAScmByYOAQ8BBiY1NDc+ATc+AScuAT4BNz4GFyYGBw4BDwEOBAcOARY2Nz4CNz4DNz4BBI0IGgItQmxhi2KORDg9EQQRMxuZGhYqCFUYEyADCQIQOjEnUmFch3vAJQgdHyaiPT44XHRZUhcYDhItIRmKcVtGYWtbKRYEBKYDEwiy/t3IlVgxEQgLCwwBAQIbG5kYEyJAJghKFRE8Hzdff4U/M0o1JSMbL0QJGCYvcSEhHjZST2c1ODwEJygeW0AxJUBff1UyFAABAF0AHgRyBM8ATwAAAQ4BHgQXLgc+ATceAwYHDgQHBicmNzY3PgQuAScWDgMmJy4BJyY+BDcGHgM3PgEuAicmPgMCjScfCic4R0IgBBsKGAoQAwEJEg5gikggBhANPkpTPhZINx8SBgsNJysiCRZOQQoVNU1bYC9QZwICBAUWITsoCAYdJzIYHw8YIiYHDyJJYlkEz0OAZVxEOSQMBzgXOB42IzElKRIqg5Gnl0o3Z0c6IAYWCwYNAwQFIDhHXGF1OWiqb0sdBxUknF0XNTQ8PEUiNWNROBYJDS5AQVUhVZloUSkAAAAAA//cAGoE1ARGABsAPwBRAAAAMh4FFA4FIi4FND4EBSYGFxYVFAYiJjU0NzYmBwYHDgEXHgQyPgM3NiYnJgUHDgEXFhcWNj8BNiYnJicuAQIGpJ17bk85HBw6T257naKde25POhwcOU9uewIPDwYIGbD4sBcIBw5GWg0ECxYyWl+DiINfWjIWCwQMWv3/Iw8JCSU4EC0OIw4DDywtCyIERi1JXGJcSSpJXGJcSS0tSVxiXEkqSVxiXEncDwYTOT58sLB8OzcTBg9FcxAxEiRGXkQxMEVeRSQSMRF1HiQPLxJEMA0EDyIPJQ8sSRIEAAAABP/cAAAE1ASwABQAJwA7AEwAACEjNy4ENTQ+BTMyFzczEzceARUUDgMHNz4BNzYmJyYlBgcOARceBBc3LgE1NDc2JhcHDgEXFhcWNj8CJyYnLgECUJQfW6l2WSwcOU9ue51SPUEglCYvbIknUGqYUi5NdiYLBAw2/VFGWg0ECxIqSExoNSlrjxcIB3wjDwkJJTgQLQ4MFgMsLQsieBRhdHpiGxVJXGJcSS0Pef5StVXWNBpacm5jGq0xiD8SMRFGckVzEDESHjxRQTkNmhKnbjs3EwZwJA8vEkQwDQQPC1YELEkSBAAAAAP/ngAABRIEqwALABgAKAAAJwE2FhcBFgYjISImJSE1NDY7ATIWHQEhAQczMhYPAQ4BKwEiJi8BJjZaAoIUOBQCghUbJfryJRsBCgFZDwqWCg8BWf5DaNAUGAQ6BCMUNhQjBDoEGGQEKh8FIfvgIEdEhEsKDw8KSwLT3x0U/BQdHRT8FB0AAAABAGQAFQSwBLAAKAAAADIWFREBHgEdARQGJyURFh0BFAYvAQcGJj0BNDcRBQYmPQE0NjcBETQCTHxYAWsPFhgR/plkGhPNzRMaZP6ZERgWDwFrBLBYPv6t/rsOMRQpFA0M+f75XRRAFRAJgIAJEBVAFF0BB/kMDRQpFDEOAUUBUz4AAAARAAAAAARMBLAAHQAnACsALwAzADcAOwA/AEMARwBLAE8AUwBXAFsAXwBjAAABMzIWHQEzMhYdASE1NDY7ATU0NjsBMhYdASE1NDYBERQGIyEiJjURFxUzNTMVMzUzFTM1MxUzNTMVMzUFFTM1MxUzNTMVMzUzFTM1MxUzNQUVMzUzFTM1MxUzNTMVMzUzFTM1A1JkFR0yFR37tB0VMh0VZBUdAfQdAQ8dFfwYFR1kZGRkZGRkZGRk/HxkZGRkZGRkZGT8fGRkZGRkZGRkZASwHRUyHRWWlhUdMhUdHRUyMhUd/nD9EhUdHRUC7shkZGRkZGRkZGRkyGRkZGRkZGRkZGTIZGRkZGRkZGRkZAAAAAMAAAAZBXcElwAZACUANwAAARcWFA8BBiY9ASMBISImPQE0NjsBATM1NDYBBycjIiY9ATQ2MyEBFxYUDwEGJj0BIyc3FzM1NDYEb/kPD/kOFZ/9qP7dFR0dFdECWPEV/amNetEVHR0VASMDGvkPD/kOFfG1jXqfFQSN5g4qDuYOCBWW/agdFWQVHQJYlhUI/piNeh0VZBUd/k3mDioO5g4IFZa1jXqWFQgAAAABAAAAAASwBEwAEgAAEyEyFhURFAYjIQERIyImNRE0NmQD6Ck7Oyn9rP7QZCk7OwRMOyn9qCk7/tQBLDspAlgpOwAAAAMAZAAABEwEsAAJABMAPwAAEzMyFh0BITU0NiEzMhYdASE1NDYBERQOBSIuBTURIRUUFRwBHgYyPgYmNTQ9AZbIFR3+1B0C0cgVHf7UHQEPBhgoTGacwJxmTCgYBgEsAwcNFB8nNkI2Jx8TDwUFAQSwHRX6+hUdHRX6+hUd/nD+1ClJalZcPigoPlxWakkpASz6CRIVKyclIRsWEAgJEBccISUnKhURCPoAAAAB//8A1ARMA8IABQAAAQcJAScBBEzG/p/+n8UCJwGbxwFh/p/HAicAAQAAAO4ETQPcAAUAAAkCNwkBBE392v3ZxgFhAWEDFf3ZAifH/p8BYQAAAAAC/1EAZAVfA+gAFAApAAABITIWFREzMhYPAQYiLwEmNjsBESElFxYGKwERIRchIiY1ESMiJj8BNjIBlALqFR2WFQgO5g4qDuYOCBWW/oP+HOYOCBWWAYHX/RIVHZYVCA7mDioD6B0V/dkVDvkPD/kOFQGRuPkOFf5wyB0VAiYVDvkPAAABAAYAAASeBLAAMAAAEzMyFh8BITIWBwMOASMhFyEyFhQGKwEVFAYiJj0BIRUUBiImPQEjIiYvAQMjIiY0NjheERwEJgOAGB4FZAUsIf2HMAIXFR0dFTIdKh3+1B0qHR8SHQYFyTYUHh4EsBYQoiUY/iUVK8gdKh0yFR0dFTIyFR0dFTIUCQoDwR0qHQAAAAACAAAAAASwBEwACwAPAAABFSE1MzQ2MyEyFhUFIREhBLD7UMg7KQEsKTv9RASw+1AD6GRkKTs7Kcj84AACAAAAAAXcBEwADAAQAAATAxEzNDYzITIWFSEVBQEhAcjIyDspASwqOgH0ASz+1PtQASwDIP5wAlgpOzspyGT9RAK8AAEBRQAAA2sErwAbAAABFxYGKwERMzIWDwEGIi8BJjY7AREjIiY/ATYyAnvmDggVlpYVCA7mDioO5g4IFZaWFQgO5g4qBKD5DhX9pxUO+Q8P+Q4VAlkVDvkPAAAAAQABAUQErwNrABsAAAEXFhQPAQYmPQEhFRQGLwEmND8BNhYdASE1NDYDqPkODvkPFf2oFQ/5Dg75DxUCWBUDYOUPKQ/lDwkUl5cUCQ/lDykP5Q8JFZWVFQkAAAAEAAAAAASwBLAACQAZAB0AIQAAAQMuASMhIgYHAwUhIgYdARQWMyEyNj0BNCYFNTMVMzUzFQSRrAUkFP1gFCQFrAQt/BgpOzspA+gpOzv+q2RkZAGQAtwXLSgV/R1kOylkKTs7KWQpO8hkZGRkAAAAA/+cAGQEsARMAAsAIwAxAAAAMhYVERQGIiY1ETQDJSMTFgYjIisBIiYnAj0BNDU0PgE7ASUBFSIuAz0BND4CNwRpKh0dKh1k/V0mLwMRFQUCVBQdBDcCCwzIAqP8GAQOIhoWFR0dCwRMHRX8rhUdHRUDUhX8mcj+7BAIHBUBUQ76AgQQDw36/tT6AQsTKRwyGigUDAEAAAACAEoAAARmBLAALAA1AAABMzIWDwEeARcTFzMyFhQGBw4EIyIuBC8BLgE0NjsBNxM+ATcnJjYDFjMyNw4BIiYCKV4UEgYSU3oPP3YRExwaEggeZGqfTzl0XFU+LwwLEhocExF2Pw96UxIGEyQyNDUxDDdGOASwFRMlE39N/rmtHSkoBwQLHBYSCg4REg4FBAgoKR2tAUdNfhQgExr7vgYGMT09AAEAFAAUBJwEnAAXAAABNwcXBxcHFycHJwcnBzcnNyc3Jxc3FzcDIOBO6rS06k7gLZubLeBO6rS06k7gLZubA7JO4C2bmy3gTuq0tOpO4C2bmy3gTuq0tAADAAAAZASwBLAAIQAtAD0AAAEzMhYdAQchMhYdARQHAw4BKwEiJi8BIyImNRE0PwI+ARcPAREzFzMTNSE3NQEzMhYVERQGKwEiJjURNDYCijIoPBwBSCg8He4QLBf6B0YfHz0tNxSRYA0xG2SWZIjW+v4+Mv12ZBUdHRVkFR0dBLBRLJZ9USxkLR3+qBghMhkZJCcBkCQbxMYcKGTU1f6JZAF3feGv/tQdFf4MFR0dFQH0FR0AAAAAAwAAAAAEsARMACAAMAA8AAABMzIWFxMWHQEUBiMhFh0BFAYrASImLwImNRE0NjsBNgUzMhYVERQGKwEiJjURNDYhByMRHwEzNSchNQMCWPoXLBDuHTwo/rgcPCgyGzENYJEUNy09fP3pZBUdHRVkFR0dAl+IZJZkMjIBwvoETCEY/qgdLWQsUXYHlixRKBzGxBskAZAnJGRkHRX+DBUdHRUB9BUdZP6J1dSv4X0BdwADAAAAZAUOBE8AGwA3AEcAAAElNh8BHgEPASEyFhQGKwEDDgEjISImNRE0NjcXERchEz4BOwEyNiYjISoDLgQnJj8BJwUzMhYVERQGKwEiJjURNDYBZAFrHxZuDQEMVAEuVGxuVGqDBhsP/qoHphwOOmQBJYMGGw/LFRMSFv44AgoCCQMHAwUDAQwRklb9T2QVHR0VZBUdHQNp5hAWcA0mD3lMkE7+rRUoog0CDRElCkj+CVkBUxUoMjIBAgIDBQIZFrdT5B0V/gwVHR0VAfQVHQAAAAP/nABkBLAETwAdADYARgAAAQUeBBURFAYjISImJwMjIiY0NjMhJyY2PwE2BxcWBw4FKgIjIRUzMhYXEyE3ESUFMzIWFREUBisBIiY1ETQ2AdsBbgIIFBANrAf+qg8bBoNqVW1sVAEuVQsBDW4WSpIRDAIDBQMHAwkDCgH+Jd0PHAaCASZq/qoCUGQVHR0VZBUdHQRP5gEFEBEXC/3zDaIoFQFTTpBMeQ8mDXAWrrcWGQIFAwICAWQoFf6tWQH37OQdFf4MFR0dFQH0FR0AAAADAGEAAARMBQ4AGwA3AEcAAAAyFh0BBR4BFREUBiMhIiYvAQMmPwE+AR8BETQXNTQmBhURHAMOBAcGLwEHEyE3ESUuAQMhMhYdARQGIyEiJj0BNDYB3pBOAVMVKKIN/fMRJQoJ5hAWcA0mD3nGMjIBAgIDBQIZFrdT7AH3Wf6tFSiWAfQVHR0V/gwVHR0FDm5UaoMGGw/+qgemHA4OAWsfFm4NAQxUAS5U1ssVExIW/jgCCgIJAwcDBQMBDBGSVv6tZAElgwYb/QsdFWQVHR0VZBUdAAP//QAGA+gFFAAPAC0ASQAAASEyNj0BNCYjISIGHQEUFgEVFAYiJjURBwYmLwEmNxM+BDMhMhYVERQGBwEDFzc2Fx4FHAIVERQWNj0BNDY3JREnAV4B9BUdHRX+DBUdHQEPTpBMeQ8mDXAWEOYBBRARFwsCDQ2iKBX9iexTtxYZAgUDAgIBMjIoFQFTWQRMHRVkFR0dFWQVHfzmalRubFQBLlQMAQ1uFh8BawIIEw8Mpgf+qg8bBgHP/q1WkhEMAQMFAwcDCQIKAv44FhITFcsPGwaDASVkAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBJSYGHQEhIgYdARQWMyEVFBY3JTY0AeLs1ptbW5vW7NabW1ubAob+7RAX/u0KDw8KARMXEAETEASaW5vW7NabW1ub1uzWm/453w0KFYkPCpYKD4kVCg3fDSYAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgENAQYUFwUWNj0BITI2PQE0JiMhNTQmAeLs1ptbW5vW7NabW1ubASX+7RAQARMQFwETCg8PCv7tFwSaW5vW7NabW1ub1uzWm+jfDSYN3w0KFYkPCpYKD4kVCgAAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBAyYiBwMGFjsBERQWOwEyNjURMzI2AeLs1ptbW5vW7NabW1ubAkvfDSYN3w0KFYkPCpYKD4kVCgSaW5vW7NabW1ub1uzWm/5AARMQEP7tEBf+7QoPDwoBExcAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEFIyIGFREjIgYXExYyNxM2JisBETQmAeLs1ptbW5vW7NabW1ubAZeWCg+JFQoN3w0mDd8NChWJDwSaW5vW7NabW1ub1uzWm7sPCv7tFxD+7RAQARMQFwETCg8AAAMAGAAYBJgEmAAPAJYApgAAADIeAhQOAiIuAjQ+ASUOAwcGJgcOAQcGFgcOAQcGFgcUFgcyHgEXHgIXHgI3Fg4BFx4CFxQGFBcWNz4CNy4BJy4BJyIOAgcGJyY2NS4BJzYuAQYHBicmNzY3HgIXHgMfAT4CJyY+ATc+AzcmNzIWMjY3LgMnND4CJiceAT8BNi4CJwYHFB4BFS4CJz4BNxYyPgEB5OjVm1xcm9Xo1ZtcXJsBZA8rHDoKDz0PFD8DAxMBAzEFCRwGIgEMFhkHECIvCxU/OR0HFBkDDRQjEwcFaHUeISQDDTAMD0UREi4oLBAzDwQBBikEAQMLGhIXExMLBhAGKBsGBxYVEwYFAgsFAwMNFwQGCQcYFgYQCCARFwkKKiFBCwQCAQMDHzcLDAUdLDgNEiEQEgg/KhADGgMKEgoRBJhcm9Xo1ZtcXJvV6NWbEQwRBwkCAwYFBycPCxcHInIWInYcCUcYChQECA4QBAkuHgQPJioRFRscBAcSCgwCch0kPiAIAQcHEAsBAgsLIxcBMQENCQIPHxkCFBkdHB4QBgEBBwoMGBENBAMMJSAQEhYXDQ4qFBkKEhIDCQsXJxQiBgEOCQwHAQ0DBAUcJAwSCwRnETIoAwEJCwsLJQcKDBEAAAAAAQAAAAIErwSFABYAAAE2FwUXNxYGBw4BJwEGIi8BJjQ3ASY2AvSkjv79kfsGUE08hjv9rA8rD28PDwJYIk8EhVxliuh+WYcrIgsW/awQEG4PKxACV2XJAAYAAABgBLAErAAPABMAIwAnADcAOwAAEyEyFh0BFAYjISImPQE0NgUjFTMFITIWHQEUBiMhIiY9ATQ2BSEVIQUhMhYdARQGIyEiJj0BNDYFIRUhZAPoKTs7KfwYKTs7BBHIyPwYA+gpOzsp/BgpOzsEEf4MAfT8GAPoKTs7KfwYKTs7BBH+1AEsBKw7KWQpOzspZCk7ZGTIOylkKTs7KWQpO2RkyDspZCk7OylkKTtkZAAAAAIAZAAABEwEsAALABEAABMhMhYUBiMhIiY0NgERBxEBIZYDhBUdHRX8fBUdHQI7yP6iA4QEsB0qHR0qHf1E/tTIAfQB9AAAAAMAAABkBLAEsAAXABsAJQAAATMyFh0BITIWFREhNSMVIRE0NjMhNTQ2FxUzNQEVFAYjISImPQEB9MgpOwEsKTv+DMj+DDspASw7KcgB9Dsp/BgpOwSwOylkOyn+cGRkAZApO2QpO2RkZP1EyCk7OynIAAAABAAAAAAEsASwABUAKwBBAFcAABMhMhYPARcWFA8BBiIvAQcGJjURNDYpATIWFREUBi8BBwYiLwEmND8BJyY2ARcWFA8BFxYGIyEiJjURNDYfATc2MgU3NhYVERQGIyEiJj8BJyY0PwE2MhcyASwVCA5exwcHaggUCMdeDhUdAzUBLBUdFQ5exwgUCGoHB8deDgj+L2oHB8deDggV/tQVHRUOXscIFALLXg4VHRX+1BUIDl7HBwdqCBQIBLAVDl7HCBQIagcHx14OCBUBLBUdHRX+1BUIDl7HBwdqCBQIx14OFf0maggUCMdeDhUdFQEsFQgOXscHzl4OCBX+1BUdFQ5exwgUCGoHBwAAAAYAAAAABKgEqAAPABsAIwA7AEMASwAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JiQyFhQGIiY0JDIWFAYjIicHFhUUBiImNTQ2PwImNTQEMhYUBiImNCQyFhQGIiY0Advy3Z9fX5/d8t2gXl6gAcbgv29vv+C/b2/+LS0gIC0gAUwtICAWDg83ETNIMykfegEJ/octICAtIAIdLSAgLSAEqF+f3fLdoF5eoN3y3Z9Xb7/gv29vv+C/BiAtISEtICAtIQqRFxwkMzMkIDEFfgEODhekIC0gIC0gIC0gIC0AAf/YAFoEuQS8AFsAACUBNjc2JicmIyIOAwcABw4EFx4BMzI3ATYnLgEjIgcGBwEOASY0NwA3PgEzMhceARcWBgcOBgcGIyImJyY2NwE2NzYzMhceARcWBgcBDgEnLgECIgHVWwgHdl8WGSJBMD8hIP6IDx4eLRMNBQlZN0ozAiQkEAcdEhoYDRr+qw8pHA4BRyIjQS4ODyw9DQ4YIwwod26La1YOOEBGdiIwGkQB/0coW2tQSE5nDxE4Qv4eDyoQEAOtAdZbZWKbEQQUGjIhH/6JDxsdNSg3HT5CMwIkJCcQFBcMGv6uDwEcKQ4BTSIjIQEINykvYyMLKnhuiWZMBxtAOU6+RAH/SBg3ISSGV121Qv4kDwIPDyYAAAACAGQAWASvBEQAGQBEAAABPgIeAhUUDgMHLgQ1ND4CHgEFIg4DIi4DIyIGFRQeAhcWFx4EMj4DNzY3PgQ1NCYCiTB7eHVYNkN5hKg+PqeFeEM4WnZ4eQEjIT8yLSohJyktPyJDbxtBMjMPBw86KzEhDSIzKUAMBAgrKT8dF2oDtURIBS1TdkA5eYB/slVVsn+AeTlAdlMtBUgtJjY1JiY1NiZvTRc4SjQxDwcOPCouGBgwKEALBAkpKkQqMhNPbQACADn/8gR3BL4AFwAuAAAAMh8BFhUUBg8BJi8BNycBFwcvASY0NwEDNxYfARYUBwEGIi8BJjQ/ARYfAQcXAQKru0KNQjgiHR8uEl/3/nvUaRONQkIBGxJpCgmNQkL+5UK6Qo1CQjcdLhJf9wGFBL5CjUJeKmsiHTUuEl/4/nvUahKNQrpCARv+RmkICY1CukL+5UJCjUK7Qjc3LxFf+AGFAAAAAAMAyAAAA+gEsAARABUAHQAAADIeAhURFAYjISImNRE0PgEHESERACIGFBYyNjQCBqqaZDo7Kf2oKTs8Zj4CWP7/Vj09Vj0EsB4uMhX8Ryk7OykDuRUzLar9RAK8/RY9Vj09VgABAAAAAASwBLAAFgAACQEWFAYiLwEBEScBBRMBJyEBJyY0NjIDhgEbDx0qDiT+6dT+zP7oywEz0gEsAQsjDx0qBKH+5g8qHQ8j/vX+1NL+zcsBGAE01AEXJA4qHQAAAAADAScAEQQJBOAAMgBAAEsAAAEVHgQXIy4DJxEXHgQVFAYHFSM1JicuASczHgEXEScuBDU0PgI3NRkBDgMVFB4DFxYXET4ENC4CArwmRVI8LAKfBA0dMydAIjxQNyiym2SWVygZA4sFV0obLkJOMCAyVWg6HSoqFQ4TJhkZCWgWKTEiGBkzNwTgTgUTLD9pQiQuLBsH/s0NBxMtPGQ+i6oMTU8QVyhrVk1iEAFPCA4ZLzlYNkZwSCoGTf4SARIEDh02Jh0rGRQIBgPQ/soCCRYgNEM0JRkAAAABAGQAZgOUBK0ASgAAATIeARUjNC4CIyIGBwYVFB4BFxYXMxUjFgYHBgc+ATM2FjMyNxcOAyMiLgEHDgEPASc+BTc+AScjNTMmJy4CPgE3NgIxVJlemSc8OxolVBQpGxoYBgPxxQgVFS02ImIWIIwiUzUyHzY4HCAXanQmJ1YYFzcEGAcTDBEJMAwk3aYXFQcKAg4tJGEErVCLTig/IhIdFSw5GkowKgkFZDKCHj4yCg8BIh6TExcIASIfBAMaDAuRAxAFDQsRCjePR2QvORQrREFMIVgAAAACABn//wSXBLAADwAfAAABMzIWDwEGIi8BJjY7AREzBRcWBisBESMRIyImPwE2MgGQlhUIDuYOKg7mDggVlsgCF+YOCBWWyJYVCA7mDioBLBYO+g8P+g4WA4QQ+Q4V/HwDhBUO+Q8AAAQAGf//A+gEsAAHABcAGwAlAAABIzUjFSMRIQEzMhYPAQYiLwEmNjsBETMFFTM1EwczFSE1NyM1IQPoZGRkASz9qJYVCA7mDioO5g4IFZbIAZFkY8jI/tTIyAEsArxkZAH0/HwWDvoPD/oOFgOEZMjI/RL6ZJb6ZAAAAAAEABn//wPoBLAADwAZACEAJQAAATMyFg8BBiIvASY2OwERMwUHMxUhNTcjNSERIzUjFSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAljIyP7UyMgBLGRkZAEsx2QBLBYO+g8P+g4WA4SW+mSW+mT7UGRkAfRkyMgAAAAEABn//wRMBLAADwAVABsAHwAAATMyFg8BBiIvASY2OwERMwEjESM1MxMjNSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAlhkZMhkZMgBLMdkASwWDvoPD/oOFgOE/gwBkGT7UGQBkGTIyAAAAAAEABn//wRMBLAADwAVABkAHwAAATMyFg8BBiIvASY2OwERMwEjNSMRIQcVMzUDIxEjNTMBkJYVCA7mDioO5g4IFZbIArxkyAEsx2QBZGTIASwWDvoPD/oOFgOE/gxkAZBkyMj7tAGQZAAAAAAFABn//wSwBLAADwATABcAGwAfAAABMzIWDwEGIi8BJjY7AREzBSM1MxMhNSETITUhEyE1IQGQlhUIDuYOKg7mDggVlsgB9MjIZP7UASxk/nABkGT+DAH0ASwWDvoPD/oOFgOEyMj+DMj+DMj+DMgABQAZ//8EsASwAA8AEwAXABsAHwAAATMyFg8BBiIvASY2OwERMwUhNSEDITUhAyE1IQMjNTMBkJYVCA7mDioO5g4IFZbIAyD+DAH0ZP5wAZBk/tQBLGTIyAEsFg76Dw/6DhYDhMjI/gzI/gzI/gzIAAIAAAAABEwETAAPAB8AAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmAV4BkKK8u6P+cKW5uQJn/gwpOzspAfQpOzsETLuj/nClubmlAZClucg7Kf4MKTs7KQH0KTsAAAAAAwAAAAAETARMAA8AHwArAAABITIWFREUBiMhIiY1ETQ2BSEiBhURFBYzITI2NRE0JgUXFhQPAQYmNRE0NgFeAZClubml/nCju7wCZP4MKTs7KQH0KTs7/m/9ERH9EBgYBEy5pf5wpbm5pQGQo7vIOyn+DCk7OykB9Ck7gr4MJAy+DAsVAZAVCwAAAAADAAAAAARMBEwADwAfACsAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFg8BBiIvASY2AV4BkKO7uaX+cKW5uQJn/gwpOzspAfQpOzv+FQGQFQsMvgwkDL4MCwRMvKL+cKW5uaUBkKO7yDsp/gwpOzspAfQpO8gYEP0REf0QGAAAAAMAAAAABEwETAAPAB8AKwAAASEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFFxYGIyEiJj8BNjIBXgGQpbm5pf5wo7u5Amf+DCk7OykB9Ck7O/77vgwLFf5wFQsMvgwkBEy5pf5wo7u8ogGQpbnIOyn+DCk7OykB9Ck7z/0QGBgQ/REAAAAAAgAAAAAFFARMAB8ANQAAASEyFhURFAYjISImPQE0NjMhMjY1ETQmIyEiJj0BNDYHARYUBwEGJj0BIyImPQE0NjsBNTQ2AiYBkKW5uaX+cBUdHRUBwik7Oyn+PhUdHb8BRBAQ/rwQFvoVHR0V+hYETLml/nCluR0VZBUdOykB9Ck7HRVkFR3p/uQOJg7+5A4KFZYdFcgVHZYVCgAAAQDZAAID1wSeACMAAAEXFgcGAgclMhYHIggBBwYrAScmNz4BPwEhIicmNzYANjc2MwMZCQgDA5gCASwYEQ4B/vf+8wQMDgkJCQUCUCcn/tIXCAoQSwENuwUJEASeCQoRC/5TBwEjEv7K/sUFDwgLFQnlbm4TFRRWAS/TBhAAAAACAAAAAAT+BEwAHwA1AAABITIWHQEUBiMhIgYVERQWMyEyFh0BFAYjISImNRE0NgUBFhQHAQYmPQEjIiY9ATQ2OwE1NDYBXgGQFR0dFf4+KTs7KQHCFR0dFf5wpbm5AvEBRBAQ/rwQFvoVHR0V+hYETB0VZBUdOyn+DCk7HRVkFR25pQGQpbnp/uQOJg7+5A4KFZYdFcgVHZYVCgACAAAAAASwBLAAFQAxAAABITIWFREUBi8BAQYiLwEmNDcBJyY2ASMiBhURFBYzITI2PQE3ERQGIyEiJjURNDYzIQLuAZAVHRUObf7IDykPjQ8PAThtDgj+75wpOzspAfQpO8i7o/5wpbm5pQEsBLAdFf5wFQgObf7IDw+NDykPAThtDhX+1Dsp/gwpOzsplMj+1qW5uaUBkKW5AAADAA4ADgSiBKIADwAbACMAAAAyHgIUDgIiLgI0PgEEIg4BFB4BMj4BNCYEMhYUBiImNAHh7tmdXV2d2e7ZnV1dnQHD5sJxccLmwnFx/nugcnKgcgSiXZ3Z7tmdXV2d2e7ZnUdxwubCcXHC5sJzcqBycqAAAAMAAAAABEwEsAAVAB8AIwAAATMyFhURMzIWBwEGIicBJjY7ARE0NgEhMhYdASE1NDYFFTM1AcLIFR31FAoO/oEOJw3+hQ0JFfod/oUD6BUd+7QdA2dkBLAdFf6iFg/+Vg8PAaoPFgFeFR38fB0V+voVHWQyMgAAAAMAAAAABEwErAAVAB8AIwAACQEWBisBFRQGKwEiJj0BIyImNwE+AQEhMhYdASE1NDYFFTM1AkcBeg4KFfQiFsgUGPoUCw4Bfw4n/fkD6BUd+7QdA2dkBJ7+TQ8g+hQeHRX6IQ8BrxAC/H8dFfr6FR1kMjIAAwAAAAAETARLABQAHgAiAAAJATYyHwEWFAcBBiInASY0PwE2MhcDITIWHQEhNTQ2BRUzNQGMAXEHFQeLBwf98wcVB/7cBweLCBUH1APoFR37tB0DZ2QC0wFxBweLCBUH/fMICAEjCBQIiwcH/dIdFfr6FR1kMjIABAAAAAAETASbAAkAGQAjACcAABM3NjIfAQcnJjQFNzYWFQMOASMFIiY/ASc3ASEyFh0BITU0NgUVMzWHjg4qDk3UTQ4CFtIOFQIBHRX9qxUIDtCa1P49A+gVHfu0HQNnZAP/jg4OTdRMDyqa0g4IFf2pFB4BFQ7Qm9T9Oh0V+voVHWQyMgAAAAQAAAAABEwEsAAPABkAIwAnAAABBR4BFRMUBi8BByc3JyY2EwcGIi8BJjQ/AQEhMhYdASE1NDYFFTM1AV4CVxQeARUO0JvUm9IOCMNMDyoOjg4OTf76A+gVHfu0HQNnZASwAgEdFf2rFQgO0JrUmtIOFf1QTQ4Ojg4qDk3+WB0V+voVHWQyMgACAAT/7ASwBK8ABQAIAAAlCQERIQkBFQEEsP4d/sb+cQSs/TMCq2cBFP5xAacDHPz55gO5AAAAAAIAAABkBEwEsAAVABkAAAERFAYrAREhESMiJjURNDY7AREhETMHIzUzBEwdFZb9RJYVHR0V+gH0ZMhkZAPo/K4VHQGQ/nAdFQPoFB7+1AEsyMgAAAMAAABFBN0EsAAWABoALwAAAQcBJyYiDwEhESMiJjURNDY7AREhETMHIzUzARcWFAcBBiIvASY0PwE2Mh8BATYyBEwC/tVfCRkJlf7IlhUdHRX6AfRkyGRkAbBqBwf+XAgUCMoICGoHFQdPASkHFQPolf7VXwkJk/5wHRUD6BQe/tQBLMjI/c5qBxUH/lsHB8sHFQdqCAhPASkHAAMAAAANBQcEsAAWABoAPgAAAREHJy4BBwEhESMiJjURNDY7AREhETMHIzUzARcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyBExnhg8lEP72/reWFR0dFfoB9GTIZGQB9kYPD4ODDw9GDykPg4MPKQ9GDw+Dgw8PRg8pD4ODDykD6P7zZ4YPAw7+9v5wHRUD6BQe/tQBLMjI/YxGDykPg4MPKQ9GDw+Dgw8PRg8pD4ODDykPRg8Pg4MPAAADAAAAFQSXBLAAFQAZAC8AAAERISIGHQEhESMiJjURNDY7AREhETMHIzUzEzMyFh0BMzIWDwEGIi8BJjY7ATU0NgRM/qIVHf4MlhUdHRX6AfRkyGRklmQVHZYVCA7mDioO5g4IFZYdA+j+1B0Vlv5wHRUD6BQe/tQBLMjI/agdFfoVDuYODuYOFfoVHQAAAAADAAAAAASXBLAAFQAZAC8AAAERJyYiBwEhESMiJjURNDY7AREhETMHIzUzExcWBisBFRQGKwEiJj0BIyImPwE2MgRMpQ4qDv75/m6WFR0dFfoB9GTIZGTr5g4IFZYdFWQVHZYVCA7mDioD6P5wpQ8P/vf+cB0VA+gUHv7UASzIyP2F5Q8V+hQeHhT6FQ/lDwADAAAAyASwBEwACQATABcAABMhMhYdASE1NDYBERQGIyEiJjURExUhNTIETBUd+1AdBJMdFfu0FR1kAZAETB0VlpYVHf7U/doVHR0VAib+1MjIAAAGAAMAfQStBJcADwAZAB0ALQAxADsAAAEXFhQPAQYmPQEhNSE1NDYBIyImPQE0NjsBFyM1MwE3NhYdASEVIRUUBi8BJjQFIzU7AjIWHQEUBisBA6f4Dg74DhX+cAGQFf0vMhUdHRUyyGRk/oL3DhUBkP5wFQ73DwOBZGRkMxQdHRQzBI3mDioO5g4IFZbIlhUI/oUdFWQVHcjI/cvmDggVlsiWFQgO5g4qecgdFWQVHQAAAAACAGQAAASwBLAAFgBRAAABJTYWFREUBisBIiY1ES4ENRE0NiUyFh8BERQOAg8BERQGKwEiJjURLgQ1ETQ+AzMyFh8BETMRPAE+AjMyFh8BETMRND4DA14BFBklHRXIFR0EDiIaFiX+4RYZAgEVHR0LCh0VyBUdBA4iGhYBBwoTDRQZAgNkBQkVDxcZAQFkAQUJFQQxdBIUH/uuFR0dFQGNAQgbHzUeAWcfRJEZDA3+Phw/MSkLC/5BFR0dFQG/BA8uLkAcAcICBxENCxkMDf6iAV4CBxENCxkMDf6iAV4CBxENCwABAGQAAASwBEwAMwAAARUiDgMVERQWHwEVITUyNjURIREUFjMVITUyPgM1ETQmLwE1IRUiBhURIRE0JiM1BLAEDiIaFjIZGf5wSxn+DBlL/nAEDiIaFjIZGQGQSxkB9BlLBEw4AQUKFA78iBYZAQI4OA0lAYr+diUNODgBBQoUDgN4FhkBAjg4DSX+dgGKJQ04AAAABgAAAAAETARMAAwAHAAgACQAKAA0AAABITIWHQEjBTUnITchBSEyFhURFAYjISImNRE0NhcVITUBBTUlBRUhNQUVFAYjIQchJyE3MwKjAXcVHWn+2cj+cGQBd/4lASwpOzsp/tQpOzspASwCvP5wAZD8GAEsArwdFf6JZP6JZAGQyGkD6B0VlmJiyGTIOyn+DCk7OykB9Ck7ZMjI/veFo4XGyMhm+BUdZGTIAAEAEAAQBJ8EnwAmAAATNzYWHwEWBg8BHgEXNz4BHwEeAQ8BBiIuBicuBTcRohEuDosOBhF3ZvyNdxEzE8ATBxGjAw0uMUxPZWZ4O0p3RjITCwED76IRBhPCFDERdo78ZXYRBA6IDi8RogEECBUgNUNjO0qZfHNVQBAAAAACAAAAAASwBEwAIwBBAAAAMh4EHwEVFAYvAS4BPQEmIAcVFAYPAQYmPQE+BRIyHgIfARUBHgEdARQGIyEiJj0BNDY3ATU0PgIB/LimdWQ/LAkJHRTKFB2N/sKNHRTKFB0DDTE7ZnTKcFImFgEBAW0OFR0V+7QVHRUOAW0CFiYETBUhKCgiCgrIFRgDIgMiFZIYGJIVIgMiAxgVyAQNJyQrIP7kExwcCgoy/tEPMhTUFR0dFdQUMg8BLzIEDSEZAAADAAAAAASwBLAADQAdACcAAAEHIScRMxUzNTMVMzUzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYETMj9qMjIyMjIyPyuArwVHR0VDIn8SokMFR0dswRMFR37UB0CvMjIAfTIyMjI/OAdKh1kZB0qHf7UHRUyMhUdAAAAAwBkAAAEsARMAAkAEwAdAAABIyIGFREhETQmASMiBhURIRE0JgEhETQ2OwEyFhUCvGQpOwEsOwFnZCk7ASw7/Rv+1DspZCk7BEw7KfwYA+gpO/7UOyn9RAK8KTv84AGQKTs7KQAAAAAF/5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQURByMRMwcRMxHIArx8sLB8/UR8sLAYA4T+DMjI/tTIyAEsAZBkyMhkZARMsHz+DHywsHwB9HywyP1EArzIZP7UZGQBLGT+1GQB9GT+1AEsAAAABf+cAAAEsARMAA8AEwAfACUAKQAAEyEyFhURFAYjISImNRE0NhcRIREBIzUjFSMRMxUzNTMFEQcjETMHETMRyAK8fLCwfP1EfLCwGAOE/gxkZGRkZGQBkGTIyGRkBEywfP4MfLCwfAH0fLDI/UQCvP2oyMgB9MjIZP7UZAH0ZP7UASwABP+cAAAEsARMAA8AEwAbACMAABMhMhYVERQGIyEiJjURNDYXESERBSMRMxUhESEFIxEzFSERIcgCvHywsHz9RHywsBgDhP4MyMj+1AEsAZDIyP7UASwETLB8/gx8sLB8AfR8sMj9RAK8yP7UZAH0ZP7UZAH0AAAABP+cAAAEsARMAA8AEwAWABkAABMhMhYVERQGIyEiJjURNDYXESERAS0BDQERyAK8fLCwfP1EfLCwGAOE/gz+1AEsAZD+1ARMsHz+DHywsHwB9HywyP1EArz+DJaWlpYBLAAAAAX/nAAABLAETAAPABMAFwAgACkAABMhMhYVERQGIyEiJjURNDYXESERAyERIQcjIgYVFBY7AQERMzI2NTQmI8gCvHywsHz9RHywsBgDhGT9RAK8ZIImOTYpgv4Mgik2OSYETLB8/gx8sLB8AfR8sMj9RAK8/agB9GRWQUFUASz+1FRBQVYAAAAF/5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQEjESM1MwMjNTPIArx8sLB8/UR8sLAYA4T+DMjI/tTIyAEsAZBkZMjIZGQETLB8/gx8sLB8AfR8sMj9RAK8yGT+1GRkASz+DAGQZP4MZAAG/5wAAASwBEwADwATABkAHwAjACcAABMhMhYVERQGIyEiJjURNDYXESERBTMRIREzASMRIzUzBRUzNQEjNTPIArx8sLB8/UR8sLAYA4T9RMj+1GQCWGRkyP2oZAEsZGQETLB8/gx8sLB8AfR8sMj9RAK8yP5wAfT+DAGQZMjIyP7UZAAF/5wAAASwBEwADwATABwAIgAmAAATITIWFREUBiMhIiY1ETQ2FxEhEQEHIzU3NSM1IQEjESM1MwMjNTPIArx8sLB8/UR8sLAYA4T+DMdkx8gBLAGQZGTIx2RkBEywfP4MfLCwfAH0fLDI/UQCvP5wyDLIlmT+DAGQZP4MZAAAAAMACQAJBKcEpwAPABsAJQAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgchFSEVISc1NyEB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWz+1AEs/tRkZAEsBKdentvw255eXp7b8NueTHHC5MJxccLkwtDIZGTIZAAAAAAEAAkACQSnBKcADwAbACcAKwAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgcVBxcVIycjFSMRIQcVMzUB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWwyZGRklmQBLMjIBKdentvw255eXp7b8NueTHHC5MJxccLkwtBkMmQyZGQBkGRkZAAAAv/y/50EwgRBACAANgAAATIWFzYzMhYUBisBNTQmIyEiBh0BIyImNTQ2NyY1ND4BEzMyFhURMzIWDwEGIi8BJjY7ARE0NgH3brUsLC54qqp4gB0V/tQVHd5QcFZBAmKqepYKD4kVCg3fDSYN3w0KFYkPBEF3YQ6t8a36FR0dFfpzT0VrDhMSZKpi/bMPCv7tFxD0EBD0EBcBEwoPAAAAAAL/8v+cBMMEQQAcADMAAAEyFhc2MzIWFxQGBwEmIgcBIyImNTQ2NyY1ND4BExcWBisBERQGKwEiJjURIyImNzY3NjIB9m62LCsueaoBeFr+hg0lDf6DCU9xVkECYqnm3w0KFYkPCpYKD4kVCg3HGBMZBEF3YQ+teGOkHAFoEBD+k3NPRWsOExNkqWP9kuQQF/7tCg8PCgETFxDMGBMAAAABAGQAAARMBG0AGAAAJTUhATMBMwkBMwEzASEVIyIGHQEhNTQmIwK8AZD+8qr+8qr+1P7Uqv7yqv7yAZAyFR0BkB0VZGQBLAEsAU3+s/7U/tRkHRUyMhUdAAAAAAEAeQAABDcEmwAvAAABMhYXHgEVFAYHFhUUBiMiJxUyFh0BITU0NjM1BiMiJjU0Ny4BNTQ2MzIXNCY1NDYCWF6TGll7OzIJaUo3LRUd/tQdFS03SmkELzlpSgUSAqMEm3FZBoNaPWcfHRpKaR77HRUyMhUd+x5pShIUFVg1SmkCAhAFdKMAAAAGACcAFASJBJwAEQAqAEIASgBiAHsAAAEWEgIHDgEiJicmAhI3PgEyFgUiBw4BBwYWHwEWMzI3Njc2Nz4BLwEmJyYXIgcOAQcGFh8BFjMyNz4BNz4BLwEmJyYWJiIGFBYyNjciBw4BBw4BHwEWFxYzMjc+ATc2Ji8BJhciBwYHBgcOAR8BFhcWMzI3PgE3NiYvASYD8m9PT29T2dzZU29PT29T2dzZ/j0EBHmxIgQNDCQDBBcGG0dGYAsNAwkDCwccBAVQdRgEDA0iBAQWBhJROQwMAwkDCwf5Y4xjY4xjVhYGElE6CwwDCQMLBwgEBVB1GAQNDCIEjRcGG0dGYAsNAwkDCwcIBAR5sSIEDQwkAwPyb/7V/tVvU1dXU28BKwErb1NXVxwBIrF5DBYDCQEWYEZHGwMVDCMNBgSRAhh1UA0WAwkBFTpREgMVCyMMBwT6Y2OMY2MVFTpREQQVCyMMBwQCGHVQDRYDCQEkFmBGRxsDFQwjDQYEASKxeQwWAwkBAAAABQBkAAAD6ASwAAwADwAWABwAIgAAASERIzUhFSERNDYzIQEjNQMzByczNTMDISImNREFFRQGKwECvAEstP6s/oQPCgI/ASzIZKLU1KJktP51Cg8DhA8KwwMg/oTIyALzCg/+1Mj84NTUyP4MDwoBi8jDCg8AAAAABQBkAAAD6ASwAAkADAATABoAIQAAASERCQERNDYzIQEjNRMjFSM1IzcDISImPQEpARUUBisBNQK8ASz+ov3aDwoCPwEsyD6iZKLUqv6dCg8BfAIIDwqbAyD9+AFe/doERwoP/tTI/HzIyNT+ZA8KNzcKD1AAAAAAAwAAAAAEsAP0AAgAGQAfAAABIxUzFyERIzcFMzIeAhUhFSEDETM0PgIBMwMhASEEiqJkZP7UotT9EsgbGiEOASz9qMhkDiEaAnPw8PzgASwB9AMgyGQBLNTUBBErJGT+ogHCJCsRBP5w/nAB9AAAAAMAAAAABEwETAAZADIAOQAAATMyFh0BMzIWHQEUBiMhIiY9ATQ2OwE1NDYFNTIWFREUBiMhIic3ARE0NjMVFBYzITI2AQc1IzUzNQKKZBUdMhUdHRX+1BUdHRUyHQFzKTs7Kf2oARP2/ro7KVg+ASw+WP201MjIBEwdFTIdFWQVHR0VZBUdMhUd+pY7KfzgKTsE9gFGAUQpO5Y+WFj95tSiZKIAAwBkAAAEvARMABkANgA9AAABMzIWHQEzMhYdARQGIyEiJj0BNDY7ATU0NgU1MhYVESMRMxQOAiMhIiY1ETQ2MxUUFjMhMjYBBzUjNTM1AcJkFR0yFR0dFf7UFR0dFTIdAXMpO8jIDiEaG/2oKTs7KVg+ASw+WAGc1MjIBEwdFTIdFWQVHR0VZBUdMhUd+pY7Kf4M/tQkKxEEOykDICk7lj5YWP3m1KJkogAAAAP/ogAABRYE1AALABsAHwAACQEWBiMhIiY3ATYyEyMiBhcTHgE7ATI2NxM2JgMVMzUCkgJ9FyAs+wQsIBcCfRZARNAUGAQ6BCMUNhQjBDoEGODIBK37sCY3NyYEUCf+TB0U/tIUHR0UAS4UHf4MZGQAAAAACQAAAAAETARMAA8AHwAvAD8ATwBfAG8AfwCPAAABMzIWHQEUBisBIiY9ATQ2EzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBqfoKDw8K+goPDwr6Cg8PCvoKDw8BmvoKDw8K+goPD/zq+goPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K+goPD/zq+goPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K+goPDwRMDwqWCg8PCpYKD/7UDwqWCg8PCpYKDw8KlgoPDwqWCg/+1A8KlgoPDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKD/7UDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKDw8KlgoPAAAAAwAAAAAEsAUUABkAKQAzAAABMxUjFSEyFg8BBgchJi8BJjYzITUjNTM1MwEhMhYUBisBFyE3IyImNDYDITIWHQEhNTQ2ArxkZAFePjEcQiko/PwoKUIcMT4BXmRkyP4+ArwVHR0VDIn8SooNFR0dswRMFR37UB0EsMhkTzeEUzMzU4Q3T2TIZPx8HSodZGQdKh3+1B0VMjIVHQAABAAAAAAEsAUUAAUAGQArADUAAAAyFhUjNAchFhUUByEyFg8BIScmNjMhJjU0AyEyFhQGKwEVBSElNSMiJjQ2AyEyFh0BITU0NgIwUDnCPAE6EgMBSCkHIq/9WrIiCikBSAOvArwVHR0VlgET/EoBE5YVHR2zBEwVHftQHQUUOykpjSUmCBEhFpGRFiERCCb+lR0qHcjIyMgdKh39qB0VMjIVHQAEAAAAAASwBJ0ABwAUACQALgAAADIWFAYiJjQTMzIWFRQXITY1NDYzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYCDZZqapZqty4iKyf+vCcrI/7NArwVHR0VDYr8SokMFR0dswRMFR37UB0EnWqWamqW/us5Okxra0w6Of5yHSodZGQdKh3+1B0VMjIVHQAEAAAAAASwBRQADwAcACwANgAAATIeARUUBiImNTQ3FzcnNhMzMhYVFBchNjU0NjMBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYL1szb5xvIpBvoyIfLiIrJ/68Jysj/s0CvBUdHRUNivxKiQwVHR2zBEwVHftQHQUUa4s2Tm9vTj5Rj2+jGv4KOTpMa2tMOjn+ch0qHWRkHSod/tQdFTIyFR0AAAADAAAAAASwBRIAEgAiACwAAAEFFSEUHgMXIS4BNTQ+AjcBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYASz+1CU/P00T/e48PUJtj0r+ogK8FR0dFQ2K/EqJDBUdHbMETBUd+1AdBLChizlmUT9IGVO9VFShdksE/H4dKh1kZB0qHf7UHRUyMhUdAAIAyAAAA+gFFAAPACkAAAAyFh0BHgEdASE1NDY3NTQDITIWFyMVMxUjFTMVIxUzFAYjISImNRE0NgIvUjsuNv5wNi5kAZA2XBqsyMjIyMh1U/5wU3V1BRQ7KU4aXDYyMjZcGk4p/kc2LmRkZGRkU3V1UwGQU3UAAAMAZP//BEwETAAPAC8AMwAAEyEyFhURFAYjISImNRE0NgMhMhYdARQGIyEXFhQGIi8BIQcGIiY0PwEhIiY9ATQ2BQchJ5YDhBUdHRX8fBUdHQQDtgoPDwr+5eANGiUNWP30Vw0mGg3g/t8KDw8BqmQBRGQETB0V/gwVHR0VAfQVHf1EDwoyCg/gDSUbDVhYDRslDeAPCjIKD2RkZAAAAAAEAAAAAASwBEwAGQAjAC0ANwAAEyEyFh0BIzQmKwEiBhUjNCYrASIGFSM1NDYDITIWFREhETQ2ExUUBisBIiY9ASEVFAYrASImPQHIAyBTdWQ7KfopO2Q7KfopO2R1EQPoKTv7UDvxHRVkFR0D6B0VZBUdBEx1U8gpOzspKTs7KchTdf4MOyn+1AEsKTv+DDIVHR0VMjIVHR0VMgADAAEAAASpBKwADQARABsAAAkBFhQPASEBJjQ3ATYyCQMDITIWHQEhNTQ2AeACqh8fg/4f/fsgIAEnH1n+rAFWAS/+q6IDIBUd/HwdBI39VR9ZH4MCBh9ZHwEoH/5u/qoBMAFV/BsdFTIyFR0AAAAAAgCPAAAEIQSwABcALwAAAQMuASMhIgYHAwYWMyEVFBYyNj0BMzI2AyE1NDY7ATU0NjsBETMRMzIWHQEzMhYVBCG9CCcV/nAVJwi9CBMVAnEdKh19FROo/a0dFTIdFTDILxUdMhUdAocB+hMcHBP+BhMclhUdHRWWHP2MMhUdMhUdASz+1B0VMh0VAAAEAAAAAASwBLAADQAQAB8AIgAAASERFAYjIREBNTQ2MyEBIzUBIREUBiMhIiY1ETQ2MyEBIzUDhAEsDwr+if7UDwoBdwEsyP2oASwPCv12Cg8PCgF3ASzIAyD9wQoPAk8BLFQKD/7UyP4M/cEKDw8KA7YKD/7UyAAC/5wAZAUUBEcARgBWAAABMzIeAhcWFxY2NzYnJjc+ARYXFgcOASsBDgEPAQ4BKwEiJj8BBisBIicHDgErASImPwEmLwEuAT0BNDY7ATY3JyY2OwE2BSMiBh0BFBY7ATI2PQE0JgHkw0uOakkMEhEfQwoKGRMKBQ8XDCkCA1Y9Pgc4HCcDIhVkFRgDDDEqwxgpCwMiFWQVGAMaVCyfExwdFXwLLW8QBxXLdAFF+goPDwr6Cg8PBEdBa4pJDgYKISAiJRsQCAYIDCw9P1c3fCbqFB0dFEYOCEAUHR0UnUplNQcmFTIVHVdPXw4TZV8PCjIKDw8KMgoPAAb/nP/mBRQEfgAJACQANAA8AFIAYgAAASU2Fh8BFgYPASUzMhYfASEyFh0BFAYHBQYmJyYjISImPQE0NhcjIgYdARQ7ATI2NTQmJyYEIgYUFjI2NAE3PgEeARceAT8BFxYGDwEGJi8BJjYlBwYfAR4BPwE2Jy4BJy4BAoEBpxMuDiAOAxCL/CtqQ0geZgM3FR0cE/0fFyIJKjr+1D5YWLlQExIqhhALIAsSAYBALS1ALf4PmBIgHhMQHC0aPzANITNQL3wpgigJASlmHyElDR0RPRMFAhQHCxADhPcICxAmDyoNeMgiNtQdFTIVJgeEBBQPQ1g+yD5YrBwVODMQEAtEERzJLUAtLUD+24ITChESEyMgAwWzPUkrRSgJL5cvfRxYGyYrDwkLNRAhFEgJDAQAAAAAAwBkAAAEOQSwAFEAYABvAAABMzIWHQEeARcWDgIPATIeBRUUDgUjFRQGKwEiJj0BIxUUBisBIiY9ASMiJj0BNDY7AREjIiY9ATQ2OwE1NDY7ATIWHQEzNTQ2AxUhMj4CNTc0LgMjARUhMj4CNTc0LgMjAnGWCg9PaAEBIC4uEBEGEjQwOiodFyI2LUAjGg8KlgoPZA8KlgoPrwoPDwpLSwoPDwqvDwqWCg9kD9cBBxwpEwsBAQsTKRz++QFrHCkTCwEBCxMpHASwDwptIW1KLk0tHwYGAw8UKDJOLTtdPCoVCwJLCg8PCktLCg8PCksPCpYKDwJYDwqWCg9LCg8PCktLCg/+1MgVHR0LCgQOIhoW/nDIFR0dCwoEDiIaFgAAAwAEAAIEsASuABcAKQAsAAATITIWFREUBg8BDgEjISImJy4CNRE0NgQiDgQPARchNy4FAyMT1AMMVnokEhIdgVL9xFKCHAgYKHoCIIx9VkcrHQYGnAIwnAIIIClJVSGdwwSuelb+YDO3QkJXd3ZYHFrFMwGgVnqZFyYtLSUMDPPzBQ8sKDEj/sIBBQACAMgAAAOEBRQADwAZAAABMzIWFREUBiMhIiY1ETQ2ARUUBisBIiY9AQHblmesVCn+PilUrAFINhWWFTYFFKxn/gwpVFQpAfRnrPwY4RU2NhXhAAACAMgAAAOEBRQADwAZAAABMxQWMxEUBiMhIiY1ETQ2ARUUBisBIiY9AQHbYLOWVCn+PilUrAFINhWWFTYFFJaz/kIpVFQpAfRnrPwY4RU2NhXhAAACAAAAFAUOBBoAFAAaAAAJASUHFRcVJwc1NzU0Jj4CPwEnCQEFJTUFJQUO/YL+hk5klpZkAQEBBQQvkwKCAVz+ov6iAV4BXgL//uWqPOCWx5SVyJb6BA0GCgYDKEEBG/1ipqaTpaUAAAMAZAH0BLADIAAHAA8AFwAAEjIWFAYiJjQkMhYUBiImNCQyFhQGIiY0vHxYWHxYAeh8WFh8WAHofFhYfFgDIFh8WFh8WFh8WFh8WFh8WFh8AAAAAAMBkAAAArwETAAHAA8AFwAAADIWFAYiJjQSMhYUBiImNBIyFhQGIiY0Aeh8WFh8WFh8WFh8WFh8WFh8WARMWHxYWHz+yFh8WFh8/shYfFhYfAAAAAMAZABkBEwETAAPAB8ALwAAEyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2fQO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PBEwPCpYKDw8KlgoP/nAPCpYKDw8KlgoP/nAPCpYKDw8KlgoPAAAABAAAAAAEsASwAA8AHwAvADMAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFhURFAYjISImNRE0NhcVITUBXgH0ory7o/4Mpbm5Asv9qCk7OykCWCk7O/2xAfQVHR0V/gwVHR1HAZAEsLuj/gylubmlAfSlucg7Kf2oKTs7KQJYKTtkHRX+1BUdHRUBLBUdZMjIAAAAAAEAZABkBLAETAA7AAATITIWFAYrARUzMhYUBisBFTMyFhQGKwEVMzIWFAYjISImNDY7ATUjIiY0NjsBNSMiJjQ2OwE1IyImNDaWA+gVHR0VMjIVHR0VMjIVHR0VMjIVHR0V/BgVHR0VMjIVHR0VMjIVHR0VMjIVHR0ETB0qHcgdKh3IHSodyB0qHR0qHcgdKh3IHSodyB0qHQAAAAYBLAAFA+gEowAHAA0AEwAZAB8AKgAAAR4BBgcuATYBMhYVIiYlFAYjNDYBMhYVIiYlFAYjNDYDFRQGIiY9ARYzMgKKVz8/V1c/P/75fLB8sAK8sHyw/cB8sHywArywfLCwHSodKAMRBKNDsrJCQrKy/sCwfLB8fLB8sP7UsHywfHywfLD+05AVHR0VjgQAAAH/tQDIBJQDgQBCAAABNzYXAR4BBw4BKwEyFRQOBCsBIhE0NyYiBxYVECsBIi4DNTQzIyImJyY2NwE2HwEeAQ4BLwEHIScHBi4BNgLpRRk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div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} + ul.task-list{list-style: none;} + </style> + +<style type="text/css">code{white-space: pre;}</style> <script type="text/javascript"> if (window.hljs) { hljs.configure({languages: []}); @@ -1362,32 +1402,8 @@ if (window.hljs) { -<style type="text/css"> -h1 { - font-size: 34px; -} -h1.title { - font-size: 38px; -} -h2 { - font-size: 30px; -} -h3 { - font-size: 24px; -} -h4 { - font-size: 18px; -} -h5 { - font-size: 16px; -} -h6 { - font-size: 12px; -} -.table th:not([align]) { - text-align: left; -} -</style> + + @@ -1398,10 +1414,6 @@ h6 { margin-left: auto; margin-right: auto; } -code { - color: inherit; - background-color: rgba(0, 0, 0, 0.04); -} img { max-width:100%; } @@ -1417,6 +1429,12 @@ button.code-folding-btn:focus { summary { display: list-item; } +details > summary > p:only-child { + display: inline; +} +pre code { + padding: 0; +} </style> @@ -1429,13 +1447,12 @@ summary { max-height: 500px; min-height: 44px; overflow-y: auto; - background: white; border: 1px solid #ddd; border-radius: 4px; } -.tabset-dropdown > .nav-tabs > li.active:before { - content: ""; +.tabset-dropdown > .nav-tabs > li.active:before, .tabset-dropdown > .nav-tabs.nav-tabs-open:before { + content: "\e259"; font-family: 'Glyphicons Halflings'; display: inline-block; padding: 10px; @@ -1443,16 +1460,9 @@ summary { } .tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before { - content: ""; - border: none; -} - -.tabset-dropdown > .nav-tabs.nav-tabs-open:before { - content: ""; + content: "\e258"; font-family: 'Glyphicons Halflings'; - display: inline-block; - padding: 10px; - border-right: 1px solid #ddd; + border: none; } .tabset-dropdown > .nav-tabs > li.active { @@ -1558,7 +1568,7 @@ div.tocify { <!-- setup 3col/9col grid for toc_float and main content --> -<div class="row-fluid"> +<div class="row"> <div class="col-xs-12 col-sm-4 col-md-3"> <div id="TOC" class="tocify"> </div> @@ -1569,11 +1579,11 @@ div.tocify { -<div class="fluid-row" id="header"> +<div id="header"> -<div class="btn-group pull-right"> -<button type="button" class="btn btn-default btn-xs dropdown-toggle" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"><span>Code</span> <span class="caret"></span></button> -<ul class="dropdown-menu" style="min-width: 50px;"> +<div class="btn-group pull-right float-right"> +<button type="button" class="btn btn-default btn-xs btn-secondary btn-sm dropdown-toggle" data-toggle="dropdown" data-bs-toggle="dropdown" aria-haspopup="true" aria-expanded="false"><span>Code</span> <span class="caret"></span></button> +<ul class="dropdown-menu dropdown-menu-right" style="min-width: 50px;"> <li><a id="rmd-show-all-code" href="#">Show All Code</a></li> <li><a id="rmd-hide-all-code" href="#">Hide All Code</a></li> </ul> @@ -1581,35 +1591,57 @@ div.tocify { -<h1 class="title toc-ignore">Performance benefit by using compiled model definitions in mkin</h1> +<h1 class="title toc-ignore">Performance benefit by using compiled model +definitions in mkin</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">2020-05-12</h4> +<h4 class="date">2023-01-05</h4> </div> <div id="how-to-benefit-from-compiled-models" class="section level2"> <h2>How to benefit from compiled models</h2> -<p>When using an mkin version equal to or greater than 0.9-36 and a C compiler is available, you will see a message that the model is being compiled from autogenerated C code when defining a model using mkinmod. Starting from version 0.9.49.9, the <code>mkinmod()</code> function checks for presence of a compiler using</p> +<p>When using an mkin version equal to or greater than 0.9-36 and a C +compiler is available, you will see a message that the model is being +compiled from autogenerated C code when defining a model using mkinmod. +Starting from version 0.9.49.9, the <code>mkinmod()</code> function +checks for presence of a compiler using</p> <pre class="r"><code>pkgbuild::has_compiler()</code></pre> -<p>In previous versions, it used <code>Sys.which("gcc")</code> for this check.</p> -<p>On Linux, you need to have the essential build tools like make and gcc or clang installed. On Debian based linux distributions, these will be pulled in by installing the build-essential package.</p> -<p>On MacOS, which I do not use personally, I have had reports that a compiler is available by default.</p> -<p>On Windows, you need to install Rtools and have the path to its bin directory in your PATH variable. You do not need to modify the PATH variable when installing Rtools. Instead, I would recommend to put the line</p> +<p>In previous versions, it used <code>Sys.which("gcc")</code> for this +check.</p> +<p>On Linux, you need to have the essential build tools like make and +gcc or clang installed. On Debian based linux distributions, these will +be pulled in by installing the build-essential package.</p> +<p>On MacOS, which I do not use personally, I have had reports that a +compiler is available by default.</p> +<p>On Windows, you need to install Rtools and have the path to its bin +directory in your PATH variable. You do not need to modify the PATH +variable when installing Rtools. Instead, I would recommend to put the +line</p> <pre class="r"><code>Sys.setenv(PATH = paste("C:/Rtools/bin", Sys.getenv("PATH"), sep=";"))</code></pre> -<p>into your .Rprofile startup file. This is just a text file with some R code that is executed when your R session starts. It has to be named .Rprofile and has to be located in your home directory, which will generally be your Documents folder. You can check the location of the home directory used by R by issuing</p> +<p>into your .Rprofile startup file. This is just a text file with some +R code that is executed when your R session starts. It has to be named +.Rprofile and has to be located in your home directory, which will +generally be your Documents folder. You can check the location of the +home directory used by R by issuing</p> <pre class="r"><code>Sys.getenv("HOME")</code></pre> </div> <div id="comparison-with-other-solution-methods" class="section level2"> <h2>Comparison with other solution methods</h2> -<p>First, we build a simple degradation model for a parent compound with one metabolite, and we remove zero values from the dataset.</p> +<p>First, we build a simple degradation model for a parent compound with +one metabolite, and we remove zero values from the dataset.</p> <pre class="r"><code>library("mkin", quietly = TRUE) SFO_SFO <- mkinmod( parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))</code></pre> -<pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> +<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> <pre class="r"><code>FOCUS_D <- subset(FOCUS_2006_D, value != 0)</code></pre> -<p>We can compare the performance of the Eigenvalue based solution against the compiled version and the R implementation of the differential equations using the benchmark package. In the output of below code, the warnings about zero being removed from the FOCUS D dataset are suppressed. Since mkin version 0.9.49.11, an analytical solution is also implemented, which is included in the tests below.</p> +<p>We can compare the performance of the Eigenvalue based solution +against the compiled version and the R implementation of the +differential equations using the benchmark package. In the output of +below code, the warnings about zero being removed from the FOCUS D +dataset are suppressed. Since mkin version 0.9.49.11, an analytical +solution is also implemented, which is included in the tests below.</p> <pre class="r"><code>if (require(rbenchmark)) { b.1 <- benchmark( "deSolve, not compiled" = mkinfit(SFO_SFO, FOCUS_D, @@ -1629,15 +1661,19 @@ SFO_SFO <- mkinmod( print("R package rbenchmark is not available") }</code></pre> <pre><code>## test replications relative elapsed -## 4 analytical 1 1.000 0.186 -## 3 deSolve, compiled 1 1.769 0.329 -## 2 Eigenvalue based 1 2.371 0.441 -## 1 deSolve, not compiled 1 72.183 13.426</code></pre> -<p>We see that using the compiled model is by more than a factor of 10 faster than using deSolve without compiled code.</p> +## 4 analytical 1 1.000 0.098 +## 3 deSolve, compiled 1 1.490 0.146 +## 2 Eigenvalue based 1 1.714 0.168 +## 1 deSolve, not compiled 1 22.959 2.250</code></pre> +<p>We see that using the compiled model is by more than a factor of 10 +faster than using deSolve without compiled code.</p> </div> <div id="model-without-analytical-solution" class="section level2"> <h2>Model without analytical solution</h2> -<p>This evaluation is also taken from the example section of mkinfit. No analytical solution is available for this system, and now Eigenvalue based solution is possible, so only deSolve using with or without compiled code is available.</p> +<p>This evaluation is also taken from the example section of mkinfit. No +analytical solution is available for this system, and now Eigenvalue +based solution is possible, so only deSolve using with or without +compiled code is available.</p> <pre class="r"><code>if (require(rbenchmark)) { FOMC_SFO <- mkinmod( parent = mkinsub("FOMC", "m1"), @@ -1655,16 +1691,17 @@ SFO_SFO <- mkinmod( factor_FOMC_SFO <- NA print("R package benchmark is not available") }</code></pre> -<pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> +<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> <pre><code>## test replications relative elapsed -## 2 deSolve, compiled 1 1.00 0.459 -## 1 deSolve, not compiled 1 51.76 23.758</code></pre> -<p>Here we get a performance benefit of a factor of 52 using the version of the differential equation model compiled from C code!</p> -<p>This vignette was built with mkin 0.9.50.2 on</p> -<pre><code>## R version 4.0.0 (2020-04-24) +## 2 deSolve, compiled 1 1.000 0.194 +## 1 deSolve, not compiled 1 20.845 4.044</code></pre> +<p>Here we get a performance benefit of a factor of 21 using the version +of the differential equation model compiled from C code!</p> +<p>This vignette was built with mkin 1.2.2 on</p> +<pre><code>## R version 4.2.2 Patched (2022-11-10 r83330) ## Platform: x86_64-pc-linux-gnu (64-bit) -## Running under: Debian GNU/Linux 10 (buster)</code></pre> -<pre><code>## CPU model: AMD Ryzen 7 1700 Eight-Core Processor</code></pre> +## Running under: Debian GNU/Linux bookworm/sid</code></pre> +<pre><code>## CPU model: AMD Ryzen 9 7950X 16-Core Processor</code></pre> </div> @@ -1678,7 +1715,7 @@ SFO_SFO <- mkinmod( // add bootstrap table styles to pandoc tables function bootstrapStylePandocTables() { - $('tr.header').parent('thead').parent('table').addClass('table table-condensed'); + $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed'); } $(document).ready(function () { bootstrapStylePandocTables(); @@ -1696,7 +1733,7 @@ $(document).ready(function () { $(document).ready(function () { $('.tabset-dropdown > .nav-tabs > li').click(function () { - $(this).parent().toggleClass('nav-tabs-open') + $(this).parent().toggleClass('nav-tabs-open'); }); }); </script> @@ -1711,6 +1748,9 @@ $(document).ready(function () { <script> $(document).ready(function () { + // temporarily add toc-ignore selector to headers for the consistency with Pandoc + $('.unlisted.unnumbered').addClass('toc-ignore') + // move toc-ignore selectors from section div to header $('div.section.toc-ignore') .removeClass('toc-ignore') @@ -1722,7 +1762,7 @@ $(document).ready(function () { theme: "bootstrap3", context: '.toc-content', hashGenerator: function (text) { - return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase(); + return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_'); }, ignoreSelector: ".toc-ignore", scrollTo: 0 diff --git a/vignettes/web_only/dimethenamid_2018.R b/vignettes/web_only/dimethenamid_2018.R new file mode 100644 index 00000000..2554cd13 --- /dev/null +++ b/vignettes/web_only/dimethenamid_2018.R @@ -0,0 +1,152 @@ +## ---- include = FALSE--------------------------------------------------------- +require(knitr) +require(mkin) +require(nlme) +options(digits = 5) +opts_chunk$set( + comment = "", + tidy = FALSE, + cache = TRUE +) + +## ----dimethenamid_data-------------------------------------------------------- +library(mkin, quietly = TRUE) +dmta_ds <- lapply(1:7, function(i) { + ds_i <- dimethenamid_2018$ds[[i]]$data + ds_i[ds_i$name == "DMTAP", "name"] <- "DMTA" + ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i] + ds_i +}) +names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) +dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) +dmta_ds[["Elliot 1"]] <- NULL +dmta_ds[["Elliot 2"]] <- NULL + +## ----f_parent_mkin------------------------------------------------------------ +f_parent_mkin_const <- mmkin(c("SFO", "DFOP"), dmta_ds, + error_model = "const", quiet = TRUE) +f_parent_mkin_tc <- mmkin(c("SFO", "DFOP"), dmta_ds, + error_model = "tc", quiet = TRUE) + +## ----f_parent_mkin_sfo_const-------------------------------------------------- +plot(mixed(f_parent_mkin_const["SFO", ])) + +## ----f_parent_mkin_dfop_const------------------------------------------------- +plot(mixed(f_parent_mkin_const["DFOP", ])) + +## ----f_parent_mkin_dfop_const_test-------------------------------------------- +plot(mixed(f_parent_mkin_const["DFOP", ]), test_log_parms = TRUE) + +## ----f_parent_mkin_dfop_tc_test----------------------------------------------- +plot(mixed(f_parent_mkin_tc["DFOP", ]), test_log_parms = TRUE) + +## ----f_parent_mkin_dfop_tc_print---------------------------------------------- +print(f_parent_mkin_tc["DFOP", ]) + +## ----f_parent_nlme, warning = FALSE------------------------------------------- +library(nlme) +f_parent_nlme_sfo_const <- nlme(f_parent_mkin_const["SFO", ]) +# f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const["DFOP", ]) +f_parent_nlme_sfo_tc <- nlme(f_parent_mkin_tc["SFO", ]) +f_parent_nlme_dfop_tc <- nlme(f_parent_mkin_tc["DFOP", ]) + +## ----AIC_parent_nlme---------------------------------------------------------- +anova( + f_parent_nlme_sfo_const, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc +) + +## ----f_parent_nlme_logchol, warning = FALSE, eval = FALSE--------------------- +# f_parent_nlme_sfo_const_logchol <- nlme(f_parent_mkin_const["SFO", ], +# random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1))) +# anova(f_parent_nlme_sfo_const, f_parent_nlme_sfo_const_logchol) +# f_parent_nlme_sfo_tc_logchol <- nlme(f_parent_mkin_tc["SFO", ], +# random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1))) +# anova(f_parent_nlme_sfo_tc, f_parent_nlme_sfo_tc_logchol) +# f_parent_nlme_dfop_tc_logchol <- nlme(f_parent_mkin_const["DFOP", ], +# random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1))) +# anova(f_parent_nlme_dfop_tc, f_parent_nlme_dfop_tc_logchol) + +## ----plot_parent_nlme--------------------------------------------------------- +plot(f_parent_nlme_dfop_tc) + +## ----saemix_control, results='hide'------------------------------------------- +library(saemix) +saemix_control <- saemixControl(nbiter.saemix = c(800, 300), nb.chains = 15, + print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE) +saemix_control_moreiter <- saemixControl(nbiter.saemix = c(1600, 300), nb.chains = 15, + print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE) +saemix_control_10k <- saemixControl(nbiter.saemix = c(10000, 300), nb.chains = 15, + print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE) + +## ----f_parent_saemix_sfo_const, results = 'hide', dependson = "saemix_control"---- +f_parent_saemix_sfo_const <- mkin::saem(f_parent_mkin_const["SFO", ], quiet = TRUE, + control = saemix_control, transformations = "saemix") +plot(f_parent_saemix_sfo_const$so, plot.type = "convergence") + +## ----f_parent_saemix_sfo_tc, results = 'hide', dependson = "saemix_control"---- +f_parent_saemix_sfo_tc <- mkin::saem(f_parent_mkin_tc["SFO", ], quiet = TRUE, + control = saemix_control, transformations = "saemix") +plot(f_parent_saemix_sfo_tc$so, plot.type = "convergence") + +## ----f_parent_saemix_dfop_const, results = 'show', dependson = "saemix_control"---- +f_parent_saemix_dfop_const <- mkin::saem(f_parent_mkin_const["DFOP", ], quiet = TRUE, + control = saemix_control, transformations = "saemix") +plot(f_parent_saemix_dfop_const$so, plot.type = "convergence") +print(f_parent_saemix_dfop_const) + +## ----f_parent_saemix_dfop_tc, results = 'show', dependson = "saemix_control"---- +f_parent_saemix_dfop_tc <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, + control = saemix_control, transformations = "saemix") +f_parent_saemix_dfop_tc_moreiter <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, + control = saemix_control_moreiter, transformations = "saemix") +plot(f_parent_saemix_dfop_tc$so, plot.type = "convergence") +print(f_parent_saemix_dfop_tc) + +## ----AIC_parent_saemix, cache = FALSE----------------------------------------- +AIC_parent_saemix <- saemix::compare.saemix( + f_parent_saemix_sfo_const$so, + f_parent_saemix_sfo_tc$so, + f_parent_saemix_dfop_const$so, + f_parent_saemix_dfop_tc$so, + f_parent_saemix_dfop_tc_moreiter$so) +rownames(AIC_parent_saemix) <- c( + "SFO const", "SFO tc", "DFOP const", "DFOP tc", "DFOP tc more iterations") +print(AIC_parent_saemix) + +## ----AIC_parent_saemix_methods, cache = FALSE--------------------------------- +f_parent_saemix_dfop_tc$so <- + saemix::llgq.saemix(f_parent_saemix_dfop_tc$so) +AIC_parent_saemix_methods <- c( + is = AIC(f_parent_saemix_dfop_tc$so, method = "is"), + gq = AIC(f_parent_saemix_dfop_tc$so, method = "gq"), + lin = AIC(f_parent_saemix_dfop_tc$so, method = "lin") +) +print(AIC_parent_saemix_methods) + +## ----AIC_parent_saemix_methods_defaults, cache = FALSE------------------------ +f_parent_saemix_dfop_tc_defaults <- mkin::saem(f_parent_mkin_tc["DFOP", ]) +f_parent_saemix_dfop_tc_defaults$so <- + saemix::llgq.saemix(f_parent_saemix_dfop_tc_defaults$so) +AIC_parent_saemix_methods_defaults <- c( + is = AIC(f_parent_saemix_dfop_tc_defaults$so, method = "is"), + gq = AIC(f_parent_saemix_dfop_tc_defaults$so, method = "gq"), + lin = AIC(f_parent_saemix_dfop_tc_defaults$so, method = "lin") +) +print(AIC_parent_saemix_methods_defaults) + +## ----AIC_all, cache = FALSE--------------------------------------------------- +AIC_all <- data.frame( + check.names = FALSE, + "Degradation model" = c("SFO", "SFO", "DFOP", "DFOP"), + "Error model" = c("const", "tc", "const", "tc"), + nlme = c(AIC(f_parent_nlme_sfo_const), AIC(f_parent_nlme_sfo_tc), NA, AIC(f_parent_nlme_dfop_tc)), + saemix_lin = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so, + f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so), AIC, method = "lin"), + saemix_is = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so, + f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so), AIC, method = "is") +) +kable(AIC_all) + +## ----sessionInfo, cache = FALSE----------------------------------------------- +sessionInfo() + diff --git a/vignettes/web_only/dimethenamid_2018.html b/vignettes/web_only/dimethenamid_2018.html index 07299242..daa0bb8f 100644 --- a/vignettes/web_only/dimethenamid_2018.html +++ b/vignettes/web_only/dimethenamid_2018.html @@ -31,7 +31,7 @@ document.addEventListener('DOMContentLoaded', function(e) { !function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var 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) 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) 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g5NLLIEXi8otcDQcUts0D8eQ1iVDRAMBTsYiNdRIxE09EIBJO9A2xqgERTaW86BUFn0OD2xFO97FAgFhF6OoQ7prYt4XwSeUgQHiJyDbeke9IdQntciLQ1FlJMaYcUNvZBg+FB1ubjlnRNvl3o6IEU2w7fdNPhm/hh+FLysUu6++DLHkOkrSHYEjH0tEPe7WdD3uyDgvAgK/m4szFFR7ch0toUgBTdWHr7EpaWru6+6dmbbnqWEbV2EtxAsXiZAPTtGPSbHsotI2leoM8TePEqgSQprs7AGFf8kuOkPdZPXGb55POAW1d/jLST9v5YflasP6v/CO7+GNAPC2BMZWmsOjp2NNbfHwMCJD+LPVL+D/OYlWEEI/9jpPddOFkB5d1GSuKZYggmCCd7JUxD7EXAzxyirYnNDLdDZoFdx14kivkvGc3579Jm36reTTvDgBnaO6vzyQ6chQmlsMoIkIQ2+bBDWBud1Va4pcCn8CPqxlh/fgtG8IPaPH8C5wk6/nZDv69jurV5QhtwE0x2iqOsj9Mx8B9/0EaUdiPfOYYDCi/q9jhWRuupMDEU0+CtX0sDFxv07T/K5niBPqN9+tQjgEc31NGCXFeMcCEuQBIc/BK4CO78u7EPYvl3yaEfK3vcb6qP1R2tI7vUjVDDUdKubsSrNjYKY1qBEa2P50SJoaXiksIoLiCwnxS6EBuBde87botNfdEWwYvF/R0/u5yCqhGeEOR2ynSeyXjt6ka7neyye8kryBSWE52y+RBgogrXPZ8E1yIHoHIFUM+AbJhE7lbMtt8ApL+xmZW7PwbjAO0fAVoXQOuiSP/ksIVdFZ0aulsamKUzwPZ/NYDMJRBPCxsBqLzqHyneXF6Ej9HlIFo7+pg+jUb3unRmGpstGkm6etOuDBGA5wCMefp1gTHcdZlvPBXlOslvYTp1cd8UjYLVd/J5awNrIOKLnIt9MD9qdrKrWCvA6ALm3QV9VrsPm60Q7+RHJHP+2hqfugo/MvI2H/mqr4b9tFnKSRY1Y5Ek80Nm/WIhr1ikKnxGz9TWXrokf9xwujfvcOTtNTWnxd0F37Y2W79tteBqZ4G5qLCuomw+nSr28QESCRVLTyYKILGJOPfcnaIFOsewhRdvv+rWa/Wih0vlbX6Zb75T5C0qNKVFvH1QL/vazSWgC2s6oWXXIuUxQelKiJbowuJDQViatLmLijg9CQBMg8WiPgiw3LEeYRmm5f+XdnvkDnxLLjMLxtvX74C3OlwPQqx4xwIdpPx38LrlDphiyWUWHWKAzzxurS/xTo+P5wGFak62ap1PVFFN4v/y+xuR39WnIO7lsWfwgVsK17wxrs9K8ltIKuhkw7f/6dhK6gQokFKhWX3urrjk/rnI0pgfpGMeuQIUaEM7+GF5q2iMkCaMQwxxOzcvU0eXbsnS9XknXvP7Gtw5dwPXlFu2ecvSHEZgNDsU6x/GdXBYXyOQjzZReSedeEPY6nEv9gJR4oBQJtFO6Kd0fwC6BO4LNHDeBujB6dSNcUQC9zIv2LnAzGk99bUDrdFY+9yGFQtEo0GQPNv6vS2drj4+1jHbv3aJSMUWP+QTZrmbNTjU8wyG/iXNNpskybLcJ3CiTF5Ir+JYzmJwE0mSVhlxbtbmvweB3ulB6Til5UuUZydpgiFVeobhU0WaBqpJ198d+/XeNRTZ9/1OPfG7+2hwzd5W3D+hmyjsRcUg/+Cavb++Vh2ls3L7zT/etOnHNxeerv313vzLVqPai4nJv+K1FC6040/4udw7sAb3laSg0XCkAAs0npBO6VJabS4Elk/U+D4gTXW+j0wnrMlqNamq4tMIYB87tE10i0FR3LZNhJsb7/R561btmes8YBCRkhYNByRtKd55mqTas9FYhJnbRGHuOh3M4QTdgQSqmgRxuzGdSvZGcbMxNQGk5C3ebLjoXIOFM4l+WKHmLTJwRv9E8GWJ6dYvf/FmEyEGr+gyrr1p5zrgkz0Cw2j94Hv8Jdx7dIVegBSNtgsqGsRQEYiIBoXwD0LNvQ5d7s5Z00QzwNhqZA0b+tMG1tQq5nd84uq8R0zPvX35G8uRaze4jcOHzz0w1+Q2BIRvf6J6Kgatnrbiem+CFvAxfkrndzD9MFPP1GWTUHclpASUkCNAQkpCCcCgDSUDAhDZ+CuEkgn8J7i9nMA7pA4lISappxILKfAeSAbIcSDuN2bJcfZILqeO5rLs0MnngSHYRdrHjmaz7JEsEPw51ZqDJDmUIOZIe34WaQeegNsJn1qz8AIpT3yCjyEih/xELkuJ0lEMYTLVCiWpo5oYMleMH6USyYJcD+uOe+kWKpn1Qns34iyYDjkSLvgnZXcgVQNeqINXr48m3iS7cjm8tedyY0f1QvTnHHdsrKby/+SSbPY8/NH6vpl/Esq3Ae4ZU1HC44KFiI9o7CEgab/RqHbj7s5KAg06s39ZP/zxI/mVuF/TbTSy+3Fb8If9/cv7+wt91yy8RfP1QXtW5RzQn7qIiZyuFM5QfJ5E9uVnqT85TanFx0lkP3ukBAMprvsRyi/C8NAJL1xbIIirSvnSj4O5netb4JxmNANHPssHAcHMHsFRgEug816gDBeMbdfiuRcghqYcm0+Xxx/5IAEtN3fqFF3LzAXqwoT0PN0OVTNqxo8sxMkd5Ig6k79Zk7VxxX6gMLOZFQgvpW2RrMW1D0BDihaXQ9wVRoBxPLfpknmkeMtoB/qM9cRc9IqmMD2XUmdZ7GSRKPUZvChf8BoykriM2MnKYbOHX8R7cLdNCxSFFVQqoYswnlWtlFS2mNkhswVpZiQW1J/UKFfipHGlUkM6UKBhMz1istELIHJLMSctu3ugzfaVSOjKvUgc/THK4Sdg2Wscz69leKIkkrwuuWiOe9yGYKQXRumkC3qbRcMwrvhjNXgdZk3RxAUEhuSPvn3nnd++U/3vlVOmrJzCD8JLxV1OHRjrZifbcFDOuRNTGqdgQm1tSNJ2OcQ04YiEXuxtII1ECSQRoQGYioEsgCfchB4ghAtw7FfJre4WZ9hkVi9MtjuWqtdNDlpMrfEG9fOT6q21okg+e4As38MfGquNt7oUws6Ysarj1/efE+yst86YUVNvDdts3Pv5c8m/aP0C+f8/Qb+IMnGq09BgwN01oIOAnAdagI8mBSrqk1gxTDUBOtk2ousEtBH2z4Ir2d3f6k8PXXVlt2qN9RODxRuoJT/v27wm09jRYVc/e++iyx2tyzJb/n3J0htXP87eSsQaf2Ly0s6Zmxela88REy1cf4273mI3iXNJ7KxrZibOm9xm6rl4fqy/t27smU8tOfdW2ucBzg2UfmOIVyLIl3kpYlwphDISTXJXsctmiDtN7fNV6zelgxwnWxsVr83Aj/S5ki1jL/a0GC6+2L6Um+aoddlNFuj+bJ8mH/iaLh8I0/U51NspIEfq0dohwyFXKgm4NggwQ4rRhCOUFtxxo8XnitT4cnGfT93IS8FaT85XE3H5LMY4zIEPL1hw443wz+1UmhTJyJGxZzw+wsKkKZgUiVtKOKMEb2AKHTv61FNc01PQFwKnvsZ/9pPA4RKTASWahmh+8MxwzHxKy74IRn5LGRjsPUUwTu64UYNY38caqd7HKucZ/tHnODtENw/2UfHRMaq1UUPDJQ0OKkWCeet5fYOhII1VRz8+/Elg5j4Gxur3J8o2PJ4rg+2d08T/fwEzSVbyZ9XPro95T477lRKqUSRXQnauHNsISAl27oWi6Fv9z48JMv8r/aMMj8onCP/DuDZOuN+GPPr/+p7bx+7JlbYdppcNhzKU/1Px5aiaGDn/s1iGMaBcleKUo/v9rcxkZj7DBEKOfrayytXNLYiUdBY+pleQXdnscKlQcpzuWluxsieeyuXIK6SdxozitWyGOV3vOHHjguyCQ6fpIYy2JwvrQEF/Qa9Pdf/QqOSqCiE/EE1/XIVKTc2tzWbHnimrEd+Vyz311Ml3P0GVTj7PD5aDnsvCvH36alEaPMePcMegXs7x8igTu4B9v7G9vTHvhCu/kzIdx+BxC0ay9zRSvoS0F2lIxI+X7klU63I40gLQ3w5ep5na+SFnba3z5D64zv+QtM4n4ffG3tq4aNHGRfxgrXPMim+5487abL7xhdseIRn1KDl+7aINixdv0OD+JSPwKf5+xoP6aiTeQIDVlIhMcL1H5R9PYXvprs3fv2bO7MOplCmweuiq2JRZ1zz+9a/v2PH1Hfz9236w+ZrPXvWfAxlj4NLLHpq3c/PQ3uvmvbrjG7fe+o2y/cLdtE6VUlXi0ASb1VLUBVSUWSU4HdvAraTyS8xzM8NxvxFkXV6pUVRiJwcgC5zEeht4rwcp7ki0k41G0qlQhG1Vzlq8alEmnFi58caB5Q9vn988MLhqyVlHvLEWjtQFeupdiocF/tkkOGPW2ibWaBTkeZ/dvPWazXfOnnvL6jkRXpi85sFzZt+55ZptW3bl1cCCHZPD06MhySha7UFzjcjbp8fOecFCirzAG/yVjBX6OFIaadSjQq1nNhyIe8tVbaaSdHlXIWKacMeuZA1uxS95zILhyrxAdsXTL6m7kNQlx2P9uZf2qhufePFFbpI6/OU0WcP99RrCsrwseVot5mtytpf6Y0gm9sdeyKnPQ7onyK4nXlR/rg7H95M1upzu89DH6pgUcikoiihJ6NJKmRxV1x+MJiOA3YwhDRQrWU0u/0rvq0VYXnyCwsLeTJYBq3dAtJDavuzyoVpzZ99Z0+a0uoiFH/xcqgDR7rUFeOrUn6Cywb8ZeNMbhLV5ugP9l0zv9UN5b5mFkjzxUcpPJCn3V402pRxtJd2GrnLdhtVk9ZSZh9W91fCSH5B7ofxPiWL+j3D/uwhBRdyAyozeZwvQzs79soi+BKSnafLviZCcfrpBpLyimfLfTyJtbyruIQKD01tUwJyKEo/ybaxkSNFUMdMkhQoJyRBQFhnUkDQSXhTM+3NmY0EDM7ffLIjqWEGt8lCO6mLia3PukFnghosJD5p5SIho/VDkzQfLE+IrYoJXkD19pdP7OwG/voIUtagiWiZ4PAFTHHlTVhRZ7dYmPar+NJ+8JhmR6DFK5DV1foHoLNO/pHrvZfmWZ15RQlwvoVDKhCWNK3CCch9lfFBuAqUgpFSShmNaPj+i5++WZfKeViJfW5HnUakVL4UCNVkA4+ETfIqx4B5xSaP2L1yn0zn2ltPn4+OqZGmwwEVCaCSqG53ldtL1oLGAhdMLd09MpCCF6tD6ZnAZBY9hDaYsP0jzZ0j5ZjKsF4i1UmLuhbJMCnYJPt5VwFNvmZawXjEvLJqIH8STonZjq7BZ8gKgR20C9MDFqJAX1H64QW2NEup6qgzLP8cvppL/NNTOBTCJABOHeWoXzLhw4Wuy7gaBtjKr9kgKq8ZlRYBS32Lpxc8vIhpNDTfyNXWybMJbn2RyQ5EmWc2QF9wmSZ0KYCE+cPuYO6b15Uotj2Kd4MItLS7gtFbkTdrFND6pvEZqv5Yv7jXAus7Pg7avo7KDot50NX3CPkP+Kps8J9/3mGQIteY/LGPC+L7872SPR2br5fy8MtKBMHedGuM28/MZmPJMrGgi3Gb1S+Si1/L/zrZwO9XH1ce/z7ZQ1WSoY/+pMb5FT4ua0Wm+Jf/298nFmChEQ+Ti71est4mq9VYI6RsymoRJKYidElT2FGnDTZvqtfhGAFTbeqEw68GqtfmbVa/1IFO1/jdWr/8BDRRtQh9XNjubEm4aWVpVonpTGR7PVGc+KJNoBIWF7kYi4gUV3r1U6723i6TxUl3n3/tM27aZfKb7THiHW9VzFSwHJ05VfK6Ar7kaB0XgPPE0BSkSFKsBUpaLihEWoA9wBt8qirh2VSOkZwXEwyrxZ5jyt2rJmSo9gX7cg6jsEUGJU9z9xJPOEM3uQQxKgkh35DNATnVyrmJ3mbCNyIB/yox4wH1bg2DwN7q9kov4pFqny8oSm3RQbGgJ1QQTs6ZMLilOVYJ9v6Wha3HcJ9jddsXp9YhGUXLXt/qMDnvLpPNTXfNa60z5/yjXQOMq+lNmwh5egpYrdfZQZV9rI47xlRkuyTjpzsmCBSWNkAXVoK8sgYWqQJWbo1RLo6QH0YW6pxqfCnRgkd+RiFjUQUQ7poIaYoakgXxwFd9BuuI38H1xBxXSFb/pBDIKQFn7YB3dB36l7sG1FLaKiBdp1KxLvfswap/30lnVESgNnvjbUoT6w9N+Xoio0qcYOIM+heg940YimsucQVvli9NEcft2UZwGQwLuilj1fFr1i3NP94X+PE7Hpvtj6lBJfJ4R6NvWiaL6MgzWHxiN66DExa+dAdAbMYX6HVF8A+7rjEZIXAVbDe7PVI9rmN69JOLV1DOSvRPxWNPZBZf/Nf+Ny65BhYxxxV+77XJ2wfQ389/IQPgajXbwMsuAz/0IaQcXJavKbRqR2IqyZruXjVC2+hdee/5vdnYOedpmVtR3NGXldxSzDSIiBVpkGb9by89UpEPKrSLZmyFDzMab/wXl2CNe7s/qCtTvWgG5kpBmCBlSzDS/r8N4uwBwohRW63JTS1y32f0TQsPfXVGEHQrV8/NCfiOUVirYcBbIeA2+iF68rQIo3B/S628vYESr79ehzS7Q9LEL9UXmik9XVHb1yBO3Ngvt5935+k1efkV51mzzrM0LL3/20avnwMeKuWyOUZg2TasSqZ+KcZQiOn1Iu2Vh497ALUVZiCKt/gh6IvTIj1ZLRjWAkpHKOKovNwp00eqPROiAbiNEKieXwMLcXhVJ1/uzmLP4tfxaHR59cBdJVG1kTAgl9ze9QKUEQ946Hkb+okJ5JRDyf54Axur1D+WS49cLr0tTPEu7UmXrxcSr3XNvumv4yXzInXKH4F7Tc7p17Zt+t/qW2+93k063X7VW6lALxTY7i1nBXMxcxmzQbabxz+tJo+wijYaIGMNS8AoSMgAPt84DdHOoMPfjXhF+kuH1tZvuFQrRCN07xGcXRX9MYxYchDe5BcHj+Z4i+42WyPc8Xofi7bbZJN5nJLJ5qr6IqRtzqNlM17SpFsnkEyTWoABEjz4JXOQvzWYuwdnV5LNGOwTM5v9r4RpQ8ZXsYodks3o31JBlzbYtNotisnm22MxiwGFXam5oN1n0TA/hRvshvTSDwHff4nNzRo9Dum6PaJbMXzDz+x+Fkj4L4bFNBb1asqsgH7Dyh4DvbkPtf5yMDKzEwyoaESMSNS9P9gJVA3/RTlwoMwZvxECFWxIPNw9gi01nOHjP32esZTtmXHnxvZd8ZtakqQ7ekajbXetpNa6ocTVxJtY+uSe69OLz77zh5bDR3xjZMzUz6fxrz1nqrZGcHQHfPVefN+fiK86LeXj+Sc5lPKy+k/vCUI/DaLFYCWHr6nbXuILTIsb5imNKY/rCm28fSMxPhkN1XbNMNZGuqwOBhtTSxWuTk6bw0ZaG86b1hKddePOKuBvmiguYBn4T/yOqOyGRBt7bKUI1GjioBC8aUKwF7Q319UgcmtFGIzCJGBqwQij0ynDsfdFGc3TS3BlNfJ25xmzniMkpXXTPvCaD3ZaZvyzjmZdudBostmhb0ORZNN2sJBeed1HXkrUsywueQH+L0eCPxmsa5ZpgRJSDZ11yDv+jmbd86vxZfc1WcZJ3UkMq1BOOOVtvu/+pB+en186d3GTwWAw2jheaJs09/+LNfZft37DALyrNj1wABMuUKbODyTVnT/KYbJ3Tpq8IrNh92dkxOj5P/YpZx4/ycyiVcDYdn4JbEoKdQi9054iBKsygLW46FRGxAb0NPNCm8BSNCPjoKcj6EAus4SuP3rB+cV99/eTF6294dA8+TK6v74MHVpYNRt/I30e8QGTOOdfGWzzxcy+87a7bLjw37rHw1nPzp0KyyRSeZO+QQhInt3dYgvycjrPOv+T8s1rptaP84VeywdWX2T4ysr0/7TLIs6+x9zib56ye1dM9e/XsZmePY3NDs9zlnNVt4+WgHJbbz3Livg4P9WWgviOMm4kCRT6I8vw0NbUUEnFvOuFKoxQW1gTsvFirsF5pb7qTUCx4i7VmtToveaDxvK9uOaedVvPRpVOnNz0Q6bry7uiSdQ8t7Vy4JQKVS+XPplV2ts4bvCwZu+KzgITtxepaPRzWdpv74muvv6RO0SorX6cu/dqKn/XWnrtp/Zragz13DUCl5myiFW2Ycvb0PtsXnU+tx8pvLFbUspLX68mdegwmOif/NPDONajTGoUh6tU56HBJCTBASVvNUB5VIiKpc9kd7kludodSFz7xQbiOmMk5dOYk56gzL6uaf7N8a6MQOHm0ae6snZpFDfuT3/jdYzjzwkXXIVHoXNuCfQslQZqBZjTsoHMqrkE4jaYdgkGz2ATOgB3cPkSukD01DnV3ttb1wx+6arPqbkcNAHoFPzKUUQ+qL0k97pjbZv1I/egC9zTFbrrlFpNdmea+gIgfWW3wqkcis8ky5FAcRd1If5nNZrl2FFpungc8wpoCl1BpQV/ScS+zjlASyUTVv/AJ46gkJI4bHX4lTnloctxPZE1ckS3+jG2fKIjkQFyzuo8jvYQG1OrGvJPSTu/nSp9PHNTl4z5hK/8gtXVKF6gEKiglgcKiRlCESsQCV5QIlKWKpr34lt/wkSx/JCmP5/cBKQfl/5gd+rOS/+p91/+YCg5CXK2W4M9fu+/6xxX+vnelVuldIDCG0VQTpU9Dw4pRfei+6zWx0MLie0gPbyrkmRU7OwT16JGeyXLHqOLqAfVN1GPlBzWtFNzj0TRTCjogtP1NjIvu5habN5Aoa1k66wGpqriVetJgiGdwDZtKhnN0y4n9sXYnsqGmZfDSR15+5NLBlhoDaedEm7sxmpqRija6ZEEg2EAnTiAC8IrmFbGz1q08P9PSkjl/5bqzYqT9hMmptEXDgTqP3Wiye+sD4Wir4jCeoHbbp5hRfpB7BakUIppIlPCD30dR1GtslDz8OsqbXmejFC/v8wu5X2myq7SJ8Avzv9DFUJySf5uNvq4+Ti7W9D/OZrLChdwxmPNiBRqVjnpK/aGxRCDspVYKAW9AN1JANoo8wP4BJUlGqdgw6m1qPQ2QW3+OfU5/ieLS/NuKpDU3uf8bcAXyBal5jMR2NEAbPAZt0K3hvxHBEDlUxfIGcD+N2gNSNx36nfqlAYow0puatNpRz0e4W2oahKzQHsjf2c16ad/3t2KTtPobnX6D8C8pd0MDP+Kx7wnXqGGlLQcvikMErm6TmfsuxJXbSAxqNjOogJLQBLiKEHAE+JGTS3JoEhTrz8/CB+5YlupJ58aOat8Kv4JvregxwcU5Cp8GFAFm1FyOfto6GS2m1NGTS6CPNKkbsTdCBlnN9onMho55BX8IJZtEQ35lk+htwN5A0V3RCPoD/yXAcv6pAtbZczRUA64JmcUf4q7Q89ZHLeJVZ5D1Ps/t+0iCT3AHVtZC7JDCXfR7OSb/Xja5H3zQbZL1B+ULX1BMTEk3AseSpmnKEK4T9ekMIidUCRQFfcbj7z8gNLvzF7mbhQN8h6ZbRset+nQWdS/ZX3k7WpS8P9sfo0iGS64wV516pOhjI6TZ2dApgI5+LhxywYoWxKUrykKJsIoDsR4mSrCTg0egMPnLW/3Q5Nn8BZEuzqEI7HK3n0+zFmuO3TtWQ5WJoG9YqCD6Gc32SxnbnVPfsxvrFXK2dILl7bLthDp6glhcsfp4bYvbSmj/mQ94uBTw0E73x2jbNRCvC6VL6GCFDwU7eWQDcC5FY5s0slieRDwtAbRsbLXbaXAuu14e2OJw1dc6jQ3ZdY8v7rv2/BWZLqvFWVvvcmwZkK9f5jS4muO9yR5res4kfkRxhV03L1RfPOiPtYi8pd7jNEsOpyTwxpaY/yCZu/Amd5Or9uS3DYaeqVOhH7gZN/8I/wi1fEuLXvyNivibjuKvN+1Nc01HF/3h+ef/sOhox8MPd5SFucPjorQwXT+ytA8EmA5mamHNFDVhBI5pjZbQpugBNkO8MvRub8KVDKST1Wag7D3xlin1ZF7LFP/79nbvCXFOY+PUjrT7/otsPXXZ4exdPzuhZuL5LUXVAn7k7PbhG89uz3b41X01gbjP1xwlu5rrvvf9+pbs6E/Vu7Nk642/PYRaAiUBdrmO6CDTBLPQFA1ur0uXoBR1INDMkypKpoTqnSMx5GiEdTEaSHLs0Alvu/19/5QW9Rv1U1ridT22i+53pzumbs+XFFXYC++CGsTj5JUT/GCgRt3n78i2n71FHG4/u6X++9+raya7os3ZbDmgWfXun44e+u2NZKuGZ0HiF8M4TlMPR+EU6rPKRJ8wOU2RFUFLex3egEsz3YqEAq0cqhAAW19dBZIlVzR61tuIdTnpXH7l+uXrbjPUyep+8cl6aXKWhPHpDcXl9KiTWDNr4mBQc8Tq+NzK/OKSbsfl79o9G20R+brBXYvUg0rLHhtrc4TN81TTOWSZ0gL1ZVlOYH2ery/7XVUjFMbzYpg7UswcqJPQwBd0LKLabJ8IaCr2otcjSkIrGwootKECaUd4XH1+SdazRrfddkBU98t1htvWrbjqSqjaCguxrffM/5zDCpBALUycmajhd+R6ww4SWafuZ5eU+tPid4lgd3gt+b/Y9rQoZNmiXYPXyRHbRs8zX/f4WIFjWZJtUdSD55AP3xtXH+ZipC0EqdBGDA4CoYEU6gRLGPU11QhkLTBiEYPiqOeQgwTCl9aok1Qr5pFf71qEeNxjy/8F0GoqYPv75Yh9j3x4DuJ+uEzHRpAq2lMqb+qfTdiq6kGtzfOWsv0c7lSeMXDHBDe1MT+LUgx0Pg/p87u2UicdIvqQi8DkxhcUwUXCedMpb4NQjwY3npTmgsURJavLwCRyEcN2HfWsDVGfv/u9ZUWUx+PYFueUKwaNvbtu+Xps3eVWbN1GcgVrdMnWJ7WmJz9SD66EBidag0NF1Ukep0t5A7sFCWdhzvYwHv6L/BehXuHqfaBwBEU7hfVLcXvS4VQv+T/vaSIl7cbeMc7ekv9i8S3e1L5xxpvMGcu1EYPbKyCiijjGXcDKckm43PqU2qNWlXusZMiqF82cuVzolUHN9NNR0HZPxFPV9V0wLtvq+k4DqOwVWDlzuQLVdqFiP08cRX7aRlBVfR8cb55bWe5LExnlcsDp1vAP8Q9BucPMk1Ulh4GnN0SAdxcNHv3q9ohx1Ati4S/tkWjIDe3hQdkUGrGRaFBiUdiTSkI41UkMuuQHP+EaSQYlPQTFWJF03BNPpTu5KFAdkWgDukzsZKMG0Q1TAQQglScOaP/dsZ8+fP75D/9Uu5Gs3FY/2SxPld0DHOciXI9gqjcEidXjE+3BLosy0OcX3T7O5g65ROGyzQ2BZs7WbZVnO5ydLe32hMwTQ4wnnKXW6XW5LAa7oaXOIHoUl0FgLQLH2by8wSTWeAx2Y5PDazK3BqZbeJZwXGPaYhX87ZNszoDdaRxotXO1nNlpdvAPFWHDm8PqEE0sZxDEqGzxisFNnuCWetPcGrObN0p23tTZwMuRVodSV8+LTrOV3eRvzjQZiSjaLYS1WEJe0kNsJlZu9LFun7++wW4gRDRbaxw2nrOGm+xOj9cmtbp9ZqeTM1m8UXfQQCSTVSQox6pvtjot/FpHvIUjJovFEoYvHYV9C5Y/xN9OfcalvII37UEhTbTg/AQIaPb4Vz6j5u8/aViycMod/fkDcpu8QZbZoeBi/vbzP3XPsZvOubMtaPHkD9jt6+U2O7vqU/9C9SMvgrXpQNG/E0oJxun+CiElUa0IKQSUwERxOntKSV7ekcuh9VBZBBo3VUcB58ofKBHCwLyf9qFosz9Ibf8dGqwaBMjRig4SGOZ2UkWI7UiO9OfUPdxOYFApUZyfpY7mgEc5rtNGGk2H1lPhAk1Hp/VAMqQEHEUfEYkkUQq1JMdzsX7kklRrTrUi1wMcDjmu1YYfATj7Y+pGpPEBXuoQIj8rR9mgCl4C9yqmF7xnVWxGVniNqtpVmXBvQ6iwni5YQ8a1jYrXtc2J13HvgkvqWxuva1sbr+P2S5ceKGyBwDv2DbrToe1u6BkAJV7xnVLUaq0sJB8pFqcUIPi3yuwxi4JuLr+P30f3OkPQ72aO0xYo3/EsmO3QO5qEF8S0qQH0UsKXv0brnl9+8M7jF174+DsfvPOl1au/RL5/9DsbNnwHL2pHR1NTRxMZhJtHktOOxLxErPF6YlLvpC9YP73x+4ofw+3xVdrHcDE0dQQCmCRgvt9b35xINDf1CDcRSfJ+pYl+Sf8YcurfmXP5F/kj6J82jNsrkWiEuhVlgFfyNkB3S5MUzLhoNiwSCYcxQ7Ui4J0Xh7fmqRbaPa1tzujxkBRlsEHy0/OM4pYLPb7g9O6BQJN6l9zQ0OGyCaZz0vMTbHOzXfQ7a2tsterTcqxeInODoemdktw+1SbVhKwtW9ffe8VKadK0OVuC3bWzyKm5LeddsWTeorWyY9IMtUFutdu5g+Rn533qkocdvLs2HmhU75br/MmWtD8zA3OP2t1ea636jEzqYxJZGAwFiDEd61oTsrRuW3/3pYNi3bS+Rd+GjOfVpAPNd6y64Gsz1GaZleWIPoYL/v9mTeQBENVEguiF1aC4YeXxFETw6QyPfn0m9g8IrMFAvKM1EI11DARnbqibHk/Iojy5rSdgCyZi06y8sS024PeuO4MfwQ5Y9yKRZCqyYaF30vzeHlmUprR21tR0t0yz8KZY66zWuGvxVQB/36kP+K38t2Hu6NQ9SFJfw0AdpqPEK2qTMpf2VCqJwqPoJezTL824b8akoL+x03nhh+oNo5e77psxg9Q5LzebIKD+fsY34f2MtB9fk9v5b8PT6tYrgv4kRPwd0q9z3gdJSJ0653KjCYPwCaR5aUY63eW48O/kdo33yxX9wCiMv2QTrk8eGSI6Ag6moG9t2P/F7GRNlDjl0gw7pJ5aOXXqyqn8SENnXBmbSwUYLyqJjv3UmY1nKr4t80no0faXsaIEiF/BRaIBnItSce4OUif7W6Vm9T9H1X9Vj71BEm+RdmIJQST/ZfVdudUvh9S/qqNvqT98g9SQ3lHibZY0mRVHooyDN/FHmTgzjdozKw28NwQ0hwN6BCoPKaEk3YtKwNhwRLXuk076CGoZNXDQcRwZvreTZY9EZi+d0s4+ztv8iei04JQl6ZbDD2eHV7X4uHuFVfPrOmcs6m6Kr7hssr+1VZFcEZ/PdJkn1hOs8SXS/NFFgqt94PIZzZ3tdaL6Q5vo6piSzdy737pwsX1VyxUrF15iJ4uNkq+rbyg1Z+O8VsNC1UmcvORPRfxtPrfRwL2p/oA1eZp6Z/aGffoewaXcA/xBlKlQLfhQL/oPgBGP3qsA7IQS8qDVNswHKRSheDUvA3Q7MZoRcJMxlEygujn1QdyzfPfq3dEp/bXh5e5YXW2Ngfvza0ZF6UgFL/E0fTq4LBlvTE2qb/KuuzYSXVnjTfM1osvqMHVbm9950quIZlbqaL6YP7jk3kUtA0GnX2nvq53f3WoSsvEdDRnULgo2fN7lNZJgI8/VWi33c3bBZnGY05+dm+3qc7fNmj4YGKLj2nfqFP+g7jdDlxEV5XsJQZP6hYrS1l0VQr4c69Xueixp90gnZPmE5OF22j+SYEWHlZ0K/Hgsh/Ztsbh6h2DNRlvv6jJh9XaJaHCZDiUDKNTMkvb8vsqCyf3ZNdSmO0fa0Y4baJTtpbKzuVzeeSI7fCKr2Z0WypapnXJ4gnoWy3PoUIlIQ1TXdqhQJIXp9Wx5fYdpeWh2TY5D+YVyKd0jw3iumwi/BC3cEy4o83QlZnW79MrCgCjbhWXBlRZVVZZv4rIKpXC01HFlHdHLoeWVl6UVc/J5uGm6CViW5mulYMk+HqNYr0AyUPivLg2oMs2MPqtuhHyRyiwvNJej1Br+fcLyoAyu8D9B7bgmzUqfFobF5nKnK4+t8MPJkI/xHUNWk117jugWF+xazTAALQn6+UE9lhoI5ApGA/iuJOsrlNP28SVVuBVajXmircLel46w2bJS1Q0Ft0KDuikDFL/3pYrid1Q4FvofwRIo4R9h2ftSwc6jHAMqLcCql8YPHtlzGoByNXYN6v8hXnRaOhUvx0sVLCexwupGDR4NOYC7PePa5keIPACnuAdD7dEadRuTIiS6Lb7uskb381My5yjzF8lGCjBRqdwrWJCagfB3yCy7XT1i92hbcZ5Ci1FJkgYMDf6n+jspIsHFjJrTOdzSMuOa9DbDcj/nH9N9bIoGVgzHPWIQuFuYtaMRaq8eCKI0gEF6lPOZjBz3EEvaaxwSUT9U/8JbJZPJJLBLolH1La/RbF9AbC8JJjv/mMnssKjLRBJyqj9QXxNko0Ux/X79epfiXkm6fmKwF/en1HLc6LxloXWKvGa5rVCVL83VuiPcDEX/K5pTXOxHfx6HHB0t2FI0qI2rCZFTrvPWU67zVuS/kTsLnc7IKhFg30e4FOkqNSfH5PtkmUy6Cpiv/36k2sbqCeCFNa+URpoY0sZoYmCgCr3qgZz6s8I0gP1bYiR+D79H56NOz0EVWCTy2/fffvSCCx59W7uRV9995eqrX8GLesOXNm360iZ+T/El3uZqL+FyzSZ8XxpTiI/G0nkT4zznFZ0t4ipMz5v4q9ssqbdKUZt6u82knPCrt6PZwsnn0XySVnyPR1ZXAn72yx48bWJsu7apnI3Hy8bygUK5Js32qcytapqgmn95uexccj205vGgJ+euOeG2SORmKZr/qKzcx9SFctMJdwMUFZDJITs7dnOp1EKZCxg304Cevyfya+vlKqv6aXK1qIj3imL+L6hL+yvUlFfE0VKZ7E8gBY3M/8VoJCFgizH1W6VyC76nH6b7jiibYVxUmVIEspry/LgZIlCeP11Z4zs/AwvVwtGFEut5S1JY4lfyT0N/evOLo+rUEgjcqc9IkGpQbv3iW7Co5b+KgjvpzYdH85PLcc4X21ouwEGl/S4qnUAvoSlXUUhR1eKr2VWFTB+GMl6FsiQsVD1R3urlAAIoSn7JQkmiVVCHSpCwDH/qPepXQ0Db77CJOAImohB+RPWr31ev5g/kE+zTa4lbvZo8xdWPffQu9yJTPCNB66s+zXoJt/0L6hSoCuBIoK8fnBGG87OoRckJpLqyWe4YbpGi50g0+3I3UD85Oa0fzubfoXxPLbW3FDWzigmyJeM0tQkax7PqTy80+UxfUHPlBZIRVNQ+v0xRm8REKPoLmNr0+Uo48v9GFbXPKylqQ2IKm00QddgyWGMROCTxdLB9nCY8P7j2DjlsV/+mfr0C0r/NkeXbbpPlOTBBwT0mVz1zx9S/wJecBF9Wgv3p032iP2v4VSgfgW2G+HUEdEXU6iq4CtpLJfIN9XQG8dwa1VoO8XC2SrPDDyCOQptXgbcPvlAgBfxBoGwftQKeKFrNTASPt3pGGqDt/QRasn2kri+H6L80MJRsmVYJrAKyDItpJUy3/15WYIJqcJ9Q5N/LFJ4c3dc1URpWl9hW6mu50MUIelg4ucTPf15zs5DFo1c0VSp1tKB9jkwIyuM45kb+IP8gHed+6jO3v0KbIknzLy636E8KPTdCuUpB0wLo9JKnAO6pv0vS31EtBha/fJemkgLVVnd8KCk4qBTpQ5m7FbifBKrPJcq0pZAFVG/XbOFz+Tcq2MLrcmV28Nmi/OHskh82bau0k8eWCaPijQPWQ5lUvslwVCfHkXBMIehqUgtDNLeauH1huvZTbYmw+luPjyWoNGEuxRLR7LK5fSyXFUyK7PURQv2v8D3XOt2NJ6liBbmPGOsakw1kbeOs+31Wm5qpH+iJWSzqdPr2O7zc2TmtnrzCig6bBd/vgQmzOlz0STWIlmZEQfupogOZFHUZ7EkUnMn0RrpIMqAgHRJAOjIJ3yGw1I/MAp9q9S3Q/clADNm1wEeO+xbwg5OIYHZLY3ehG5lJk2xhco+6JWybpEVz2wrR6hZyD0QXZbeDVB+onmlimpkWprdAs4WEZDSQppsDlcdCBJJESIYFuAtUnC4GIF2C3Uu2Kv7L1bdz6FxtqxpG4TqQOqOUNAJ2HLvPWA2GgDy4O4vaDrtyl6P+1fAll+SyFcQ28GHqh7fvvf37udylf0fNwhzgz87Y+cf5x9GnF6ygHu18sAbipWeF0YPBgp2GaKeQduxxdEr3SgbH1kvH7tvqSLhedomOvZyts2dw8acu3dY/f+ucuMtCuP/e4zC4XnH3OLZ8ZuxTWxy8dJfU5dhDeKPSlJy5pn/+7u3XrJhmr9C5CuleGflGQocKnlAUaRKp0BAHV0ZwUt9VCqk6zYOgRIuMfePJzdmBdpPJ7/6B23+f+sp9NMDZevovvfYHG5dGPISQq1DojqNckchVrCcCYz/Q0hI0m3NKDRfkgsrnamo+p0CAq1FyvC3a3Nak/s5VX282x9Ufy3E39VAx6o7LpCvO2wK+ch9jNqpJCutcIOooKnYWtDK8gTRVYygRQfwgzKM5+jP2jOZdx3r32Py7rQUPOzAnoRs95NvRAR0qLGU11Taqu1bUYSzMcWjMEir067JQQHfIrLBHsrgv00/Wavd8HRLMEEYFSW3HCSNQehnrHztKqHcDyo4VfZ6gPKCR+gufwA8GegxUEo4A+gd0BASHiH6jYMLIsUdQJTs/C641KN4oCHWolCMLlMfIdtWKScjx7SM5LD9HnfmhrGI0S139UWfUnxgOXdJFW+AMcGjKr6eHAttHF5sUoeArYKDcxMSYcKA/xUDhPiEOEAPafSIUFArN0r24ynI91EPARDXvIDYyvqZaWeroBOUABQA/E+DXC7PWafDLQY2oiwpUEyj4RQtVlUp1GrM7In2p2A7VuiOW6otMiGOo5Mrp05ejVuTy6dNX/k/7mybZQ0nUmfrbx3U4KueDnlHm5wdh8FFeKnoaKKh/TK18StOPhwG9Xo5mqXAxvw/79YQwwDR+nAKQQ4izVXioB84qcppWB7IqjU45z4CE17OvF1Dw+oTFqxtz8dxwtogBnF9MjIl/in+K8s3hM9laIn0TiCbTAXL0T798bPXqx36p3chrv0O+GC9Xaj48Ecv8U8UEeBvUEsDlTepiU5OvlpeNGvpnKF0RvUooWhIjnx6GeBapXCQYTw9DNg6/OC3gZjp76oNTj9Kz6Jqobxb9NDqc08vcKReOpcsQV2K8InXFaXW3aI6Ofr1k48rp7CX7rx+v1UKPsfvzQU0Kc83i2VdILmd2/yX55zT9luN2+Cu4nKfwPcK/CvDVU+pHh8+LaldIf1fA5h3ndT6Fln9/W/9Ce1vndfvJtnPVO2xhm3qbafHVCN1X363UXHq9xuVD8OSD29Z8pZ5cZrern9cAdGW/uib/ud+VK0L9a42r6C90kL8KzxwLQw9NkIQJL0ASU8M+VG0KsUdgdvpgP/6NqqP0/gHZFUfGEijZLHpiIgvV5/Bltrj8Qd7XQd5p4P+7tJo30NMO6VGBwahSPMYiaaBYoLY6uEnciyhhh1Z/vvacG/rjpsvnpzs0B1Id6fmX8119l88XnOxe/uGrzzHcdu7UtY3+2vmXN5zUyj3ZcPl8p1sZSs6/nGXtwrV7Ka0XZdz83fwjjINpZWYw85lL8BRK4nGyIir2RiOsEyipuEcIakpGjWgBjLiHWOgj0Yi34gW1kKPxHt2Na5q+lwg1RdRSpFDNzosb44YJXnAfoEOpZW//6u1lhYA6leevezbI26zNHO811M2dc5HFxpk4i1jPC0s21/BWW5DnPQbn2X1WK43/aM2n18DfSoybbNHijFpamzXI31eRibGUOxSu/lT96YZlq1Yt20DaSBuG6knw2eusHs5EPBfNmVvHKdaQzcDfz9ZsXmLDWGXy2U5OsYSsIn8CS12jQIyD12KKqZrLPy7mSPdICmd6WGHG8NDZkkHuE4h9TU8FpmUO/VjC/EinToFyoNDz2p9XD6g78WgQdPG7Z3R0T/Z5dTM9lsL8Ktek7szl2L+gQwGgwkZHc2g5Su7NvVqwGy2Ua4KSXUwt1X4PaM5paaEu6jQ5zVFyNabxvUksVt2T/4VeamYPlLtffdQsk+2sUTY/zDXl/05W53/Bz9UK3p7LjapZ2ZxOm+UlZXrL3HHGqO8+wVroDaCTTnTxitMxmiAAYQzVJQH+nj3oIHnPaN6Zq6sNSLjBl8tKgVr2mj/9CWi9dnKca8rBQBsd5R1tzVlgrl5pbnPw6kZclCr2CHxMnHohLz+3KRQokzALyeIKFU1TNCiayJdoHvDYe7K6mZLm8S3uJ9dojuaJ62/qN/tjQxnSnhnKPw+LNrLi8ZKyJ3x1YhiI1aNAtP6NzCGzYv3DmaGh/LvQZnt0evgIhTFV0kE/PYxAnOHhCQUZdCWY5JWJwMzlAGl1mpNbDU7yyGnhRMILsYhH3VRAijrPcBU8/Cj1Y9NY6cnGVW0CjTLaz7E3epvaT/LtTV72Rs+0WVVmd0dz/MGTI5F0OsIviaqDlbbO5X6xT3PeXbXHRtf/z+fdka+eKPr8KF7IF4vBsT9MFPuPJMBTBMq9hQxXelQ+bewnf18ap4Ib+mSMrtDU5zqlD8QANa5MBGh/OwOvSDfcV2d66mfEWsbGWmIz6nsyZDWQSmqmxDneYyvjHPmRXHZxeueyRGLZzvRioKnGto9nIPkibAJA16adcOZRQr1iAP3bUyBR7T4RgAWTKxhkCYFwshq+7iV9r0whk50cmRcTg4fy5x4OmmNkHndIA2+YuMbmE9dwGYB4KFTsvnDE6Ah47r/fE3AYI+oXADpkdlENcZ8OZEEf8FFGZNxMs6ZLpG3SUFLL7Q2kcFU/A/Jsw+vWDa/7emewLaoeibaF1B9qUNnuqWK3+UfXYVL1v/omD15xxeDkPnXTOKSVcCbDGtOu0YQNpGAP7U1HU58UrqGu8xIbHtkQ3LVhb7Dx46ET3Ffcm1q0YcOizNmf3bC3VjWfAcpSv3MyTlgJ23FHQgmgvk+gk8pL0mcCDOn08MDAQlf+/SlTZ1z12fnqntOhbOTL9/ZdevbAPN+yby1f/uUtC/ixm8ZBo59LTXEW060hGrTDplNprWd58fwB/b/E27BdS/s7U+rGVCeQ46nzaw9QccnmZerGZZs3Yw9aVHt+Kh6HN4ti6lxIhT/wahnZtWwzlY9QHQ2c79C+dxzvVDKy8GqKWQERO9YAKbpsDUTLdWV5dE8PVPjvj9pqw7ah/PFVtkit7aj6G5xY9mfJrCz1j1e0BcnPol4UjtrCdbahIVtd2HaURujnFJR8CuOuUUfhrGhgKKgjCYNSvCc1WKlEp8wHUaAYynFNyzZn+2MnYv36dbMDBTonl/T/ma5IKAyEGz+4eRnVtaX6tss2o34u8mWorFtuFgm4A6qK/yp/gLEBVat5WnPDdKA574ubuFJ/IUfZ/Y2Nt6mN+ZNNTSTaeI56gKwkXerTe9DDHUw8/H35FY3nNN7GGuBKWhrV9ep+0k1WjNWVaHkW1yA+QHWNu8rtBw2a5YXuE40rs7/GA+j09V3hA98yRnFPOGr8ltGlsFdD/7tRce3LH6Trcneuiy7K7J3khKu+3qUaXPWaX7T6/Kfj9BX2eZq2XAcZT79u1ClJzUtHUqfqSMWBcZS43Ena0cUGLgpkKxB1QM+0Fxz10wgg6r5rltnFpH05pepUq3Y2HfYqeKRntmUFNz+XmcOs1H31U6cC6RTVLfCg7RNBF1UF2/wBgu0fFQtPEU1sSg3VcNsR7dWq3af87tUFn1l3ltXpaJxpNvtcZkH2WmMst3JqRpxUH+WC0E1qOGtP66s1MYv+VLu8/XFXvV/ZbunYYBeVN64ls0ur6NzpV9xzlmQwB5qC4Tq70WC0tk8dWJXeHvkD0h9zJOM0vD86/1NJMaIAolctvlByferCsqOKDKceOfUu1PsmoFCamV5mCrMUOCi6V6FJosMF22AcrKJgQDVhfYh6tepp/lYgvnCEAbJQ1L0rOpajEmRcasMiPfxhgGoVo4rwreQpV6fUJHH2e8fa1s2c13Apl1b89a58ozdoap2sjgLN9uISl7P1DrulyeIkt0zr6JjWocoPOZsaXPb6jtqBblsgsaRre2xHi4nELm0MhG1+x1SXwLpFi53b+aHRYo/IrbZtuWAKu5cSEXfybnnmUCaXGTpQr0xK2O2WWY76f+nAjNVf7nCZHU5XqIkTnpt6VtvsFlPXg1031g/VRdpkkyVpD7jnmax88QwDvg/66NnMRdRXTcGTmQc3cuINwN5IQqi0yzb+YFVHuVqI5s4ADfg5oE4ybDLd28mFSFmYvRoomsWXEdLU2Wl3GJy93ZNb/d5gqmNaqJZSO1l6PVRy0nZIj/45EetjLguh1rLqR+SK0hO6NrsqcNX8zoUdjQYDJ7tb4os6+i+Y0qpY2AWlnLRDWdGFTfGY1gV0zNAtJ7pdo24se0D88AwLY/gZmE9iuP4V5v7CSR/RThaHLh+UeBkXwU6BC7lGOevK65udTv+tS/PfW7qj3ljTcj3b9OkbV85t8xsMj7Ddj7DGpthZKwKPvso/c/1K9aLE12fMWLV1y1D9ua8lyJdWXr/bG+noCFutf/mLILe39ITUV4igr3876fpX5g2zeB52sWnIL4fXHlgeUzOx5QfIvJQyrKQE9wHUqVq+PEaOrz0wVvNbJZVSfsuMzxN4l9PkedFzw9V5Dj+nzpgoT4ZxCxJfC5RWLc74YVHxKlExCYt0JAOMatREhHBSCAtSfod6x6Ls8HCWECLwXZ9nd5Dz1T24JUdWs6fU3++fcnT49Qe+kBs+wdsMZgPXMp3U5S958snPP/EE7bvkOPCuTUDTUQ/UzirLhML9yPahoe1D5Fj5jWsaoveyP00PehdUAHk/seDVWsvDWXXXsyn/4wfpXc2V3/Qxli3jl/5hj/83avSCfpTNxOEKLmTjxOEKuxgNlsQn0xgct724mhynupNW1Ph6o3RYS3/+2TJrzLlkFz+ip3qCHKf6eqW02QJLjBYuuj4sobhCWqa/YHGEHpcnumuWSOhxeaL7sOakNR6vvmo+YcfFA8UFXEPZf9UjyudIOyNwx/i90DdsujS/FX2UAwvWSVK4NxaMhAGw3oowp/uc8CTi7D2rBgZWwb/60faR7SPsEbjkXy4G0XaqhXPwe2cePjxjxuHD6ssQuR1fq6PF0E+o2t1nePTn8TUmxz/A3crMoCc7egESuoTHYc7mYdg6etORoOhR7BBGD+qJopELrl4S6cJNRtEAsLP/OdvnJq0Wo0GolY2Et9VFB2Kf+4bZvVyxfOMz3WdFfSIryj6DwWghre7aQbdiDrkTL3A3vNDuDpk93HqXwam+bWmUJZfNn5ozKV5Pmmq8PF/jVY+2Tlk2M2RzSXKjmbQ4RZcQavEYrN/9rlXwtIQqzxQNMzPPfHYLvuPoO9TbT8bpGw5CQPGd+SyX/Cyf0Vxjd2R9NmsunnXYa8xGHzn+sSfM5J0y0DZEXWWxkXjcR75KBLNLHi7XvX2G8VOrf4Ykg0AMdBESIpo7MgAfyakA6rkqpI6UjNs0px7cMV+D5BF49Tez1VGnYmq0WIijp985m4Sn2gJR9b07riPPFo97OYbUZbxJCpot7H/lpZBicglCPN7WOfJkcHqc3ElWqvvz/1E6bIQrG+tz6WkM1SM9FBTR7FSs8KyBBytSmNEoquJNFN5EQyTiCrnKDx1h58yxCepPHU5nxGoxEQeeOZi2m80DxNxncVhr6BmEfUarxejw+WSiHhWk19bSY7aKR5MsteblJpfTLtjimBouXsm3d3djjYM+wEW0El9dM/ueVRWIsXwe43R7SgbVZqrnqoJ1X/kuF7pcgf8duv4q6vayV5U9zMV91GxO59UUjW8rHV6u799WzKMT7umRCXbYUKM+foaCcwgaoqZUtmodV3p+X7akb4dnU9B9La38RPFUG2SCC90tVA4XwEFhyOpZZrUCsgWYHsczLFBBVGNtstoN1bw0Z+O4fYIbvZVt4EUcJEKOhHeincWqONw+q6w5Go+WGOSR7LhKV+KBqbBPpfUvOf9QqkpDyVhBeyyZQGMsdA5FBUqvFMtUyGq9vjnsAJU4UcrxldP1CCaofyDkSAifoP5QwWx+SyUGxp75BzGAvtG7uQ38LehlyEQMeh0TeE6Bm7tYdXqdkt0uOb3kfYlNwmOdDyacOq/qlFo1v+PTmTi3E/glC9W11b34A22zmLzvb231Q0L2Bgg60OTW4YdstO+YOJnO38TtpH7zy9ymokWyA79qlVSn38HtpFlImFnhu3b4boNWXklOXV0Iwo7lQ1hrZyPFcwtjwFP7iEKSHSSJw509kh8kj6pr+H1jR7km9vcvqN9657vffefkv+fKxge1X+7RdjYUPIESN7gTvRkB/RMYtEkaVkdHApmdBPpnKmz0n1xSWFOyVIuLrinZwpoCRe6kyiVZoHX088F+UX4+WKS4iBTP0IWxGtZgOdMaV4KTayqHQF/VihBwTbgDXTCmKoOBJeNhwJMzEVjtjIFLuU38fPR7hqNG1JS7g/qRCuy3vmQ3W9Vu8qbVbP+SzazGRJH83MzP90Ck2m31mMjP8TiLn5uwD2Ugr2PFvPQjB5BnSJvQxGQZZEB+LopqzGzDbMmbkAPkZVJjeO5FzOSBKCgJze2ZS4Gemc9twrwY6u9H61iUQTcRvtdT9RW3tRxAWwFs2tcuJRnI6xjmBdWjbgFNRHMHiF1uHYBfUR/ut5Ug2jXAaT96+9RH/FToRwIzGbKmVJ1AZQnoabSB1yyIg7ByAridHApPMjyw0OiV6RjSbCuzwLAvFizBliWJua1tsuAgvNPbmljYbpt8lkWam7b3XZiOiKJskMOtmfScnsbPW208knwjuXrXK4Q1iKIgNyYXXDVT9C2Ye/78GQ5BEEXfFdde2RwauOysdJNL5AzCy84ard/nGAVN8alecnFdgu5Gbd5DJTL+hHZK0vApVy3OfU8XTSJg1TlssivsPYUlIqvn66PzrVTymCc4wgF6SDNR0pDf+9Gp+VnsUH5WtpHYsuhOaey8zdwLN47V8MTbm78g687+P3cx6tcAeNpjYGRgYGBk8s0/zBIfz2/zlUGeZQNQhOFCWfF0GP0/8P8c1jusIkAuBwMTSBQAYwQM6HjaY2BkYGAV+d8KJgP/XWG9wwAUQQGLAYqPBl942n1TvUoDQRCe1VM8kWARjNrZGIurBAsRBIuA2vkAFsJiKTYW4guIjT5ARMgTxCLoA1hcb5OgDyGHrY7f7M65e8fpLF++2W/nZ2eTmGfaIJi5I0qGDlZZcD51QzTTJirZPAI9JIwVA+wT8L5nOdMaV0AuMJ+icRHq8of6LSD18fzq8ds7xjpwBnQiSI9V5QVl6NwPvgM15NXn/AtWZyj3W0HjEXitOc/dIdbetPdFTZ+P6t+X7xU0/k6GJtOe1/B3arN0/pmz1J4UZc+D6ExwjD7vioeGd5HvhvU+R+DZcGZ6YBPNfAi0G97iBPwFXqph2cW8+D7kjMfwtinHb6kLb6Wygk3cZytSEoptGrlScdHtLPeri1JKueACMZfU1ViJG1Sq5E43dIt7SZZFl1zuRhb/GOs44xFVDbrJzB5tYs35OmaXTrEmkv0DajnMWQB42mNgYNCCwk0MLxheMPrhgUuY2JiUmOqY2pjWMD1hdmPOY+5hPsLCwWLEksSyiOUOawzrLrYiti/sCuxJ7Kc45DiSOPZxmnG2cG7jvMelweXDNYXrEbcBdxf3KR4OngheLd443g18fHwZfFv4NfiX8T8TEBIIEZggsEpQS7BMcJsQl5CFUI3QAWEp4RLhCyJaIldEbURXiJ4RYxEzE0sQ2yD2TzxIfJkEk4SeRJbENIkNEg8k/klqSGZITpE8InlL8p2UmVSG1A6pb9Jx0ltkjGSmyDySlZF1kc2RnSK7R/aZnJ5cmdwB+ST5SwpuCvsUjRTLFHcoOShNU9qhzKespGyhXKV8SPmBCpOKgUqcyjSVR6omqgmqe9RE1OrUnqkHqO9R/6FholGgsUZzgeYZLTUtL60WbS7tKh0OnQydXTpvdGV0O3S/6Gnopekt0ruhz6fvpl+nv0n/h4GdQYvBJUMhwwTDdYYvjFSM4oxmGd0zVjK2M84w3mYiYZJgssLkkqmO6TzTF2Z2ZjVmd8ylzP3MJ5lfsRCwcLJoszhhyWXpZdlhecZKxirHapbVPesF1ndsJGwCbBbZ/LA1sn1jZ2XXY3fFXsM+z36V/S8HD4cGh2OOTI51ThJOK5zeOUs4OzmXOS9wPuUi4JLgss7lm2uU6zY3NrcSty1u39zN3Mvct7l/8xDzMPLw88jyaPM44ynkaeEZ59niucqLyUvPKwgAn3OqOQAAAQAAARcApwARAAAAAAACAAAAAQABAAAAQAAuAAAAAHjarZK9TgJBEMf/d6CRaAyRhMLqCgsbL4ciglTGRPEjSiSKlnLycXJ86CEniU/hM9jYWPgIFkYfwd6nsDD+d1mBIIUx3mZnfzs3MzszuwDCeIYG8UUwQxmAFgxxPeeuyxrmcaNYxzTuFAewi0fFQSTxqXgM11pC8TgS2oPiCUS1d8Uh8ofiSczpYcVT5LjiCPlY8Qui+ncOr7D02y6/BTCrP/m+b5bdTrPi2I26Z9qNGtbRQBMdXMJBGRW0YOCecxEWYoiTCvxrYBunqHPdoX2bLOyrMKlZg8thDETw5K7Itci1TXlGy0124QRZZLDFU/exhxztMozlosTpMH6ZPge0L+OKGnFKjJ4WRwppHPL0PP3SI2P9jLQwFOu3GRhDfkeyDo//G7IHgzllZQxLdquvrdCyBVvat3seJlYo06gxapUxhU2JWnFygR03sSxnEkvcpf5Y5eibGq315TDp7fKWm8zbUVl71Aqq/ZtNnlkWmLnQtno9ycvXYbA6W2pF3aKfCayyC0Ja7Fr/PW70/HO4YM0OKxFvzf0C1MyPjwAAeNpt1VWUU2cYRuHsgxenQt1d8/3JOUnqAyR1d/cCLQVKO22pu7tQd3d3d3d3d3cXmGzumrWy3pWLs/NdPDMpZaWu1783l1Lpf14MnfzO6FbqVupfGkD30iR60JNe9KYP09CXfvRnAAMZxG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li.active { @@ -1597,24 +1612,54 @@ div.tocify { -<h1 class="title toc-ignore">Example evaluations of the dimethenamid data from 2018</h1> +<h1 class="title toc-ignore">Example evaluations of the dimethenamid +data from 2018</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 1 July 2022, built on 01 Jul 2022</h4> +<h4 class="date">Last change 1 July 2022, built on 05 Jan 2023</h4> </div> -<p><a href="http://www.jrwb.de">Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany</a></p> +<p><a href="http://www.jrwb.de">Wissenschaftlicher Berater, Kronacher +Str. 12, 79639 Grenzach-Wyhlen, Germany</a></p> <div id="introduction" class="section level1"> <h1>Introduction</h1> -<p>A first analysis of the data analysed here was presented in a recent journal article on nonlinear mixed-effects models in degradation kinetics <span class="citation">(Ranke et al. 2021)</span>. That analysis was based on the <code>nlme</code> package and a development version of the <code>saemix</code> package that was unpublished at the time. Meanwhile, version 3.0 of the <code>saemix</code> package is available from the CRAN repository. Also, it turned out that there was an error in the handling of the Borstel data in the mkin package at the time, leading to the duplication of a few data points from that soil. The dataset in the mkin package has been corrected, and the interface to <code>saemix</code> in the mkin package has been updated to use the released version.</p> -<p>This vignette is intended to present an up to date analysis of the data, using the corrected dataset and released versions of <code>mkin</code> and <code>saemix</code>.</p> +<p>A first analysis of the data analysed here was presented in a recent +journal article on nonlinear mixed-effects models in degradation +kinetics <span class="citation">(Ranke et al. 2021)</span>. That +analysis was based on the <code>nlme</code> package and a development +version of the <code>saemix</code> package that was unpublished at the +time. Meanwhile, version 3.0 of the <code>saemix</code> package is +available from the CRAN repository. Also, it turned out that there was +an error in the handling of the Borstel data in the mkin package at the +time, leading to the duplication of a few data points from that soil. +The dataset in the mkin package has been corrected, and the interface to +<code>saemix</code> in the mkin package has been updated to use the +released version.</p> +<p>This vignette is intended to present an up to date analysis of the +data, using the corrected dataset and released versions of +<code>mkin</code> and <code>saemix</code>.</p> </div> <div id="data" class="section level1"> <h1>Data</h1> -<p>Residue data forming the basis for the endpoints derived in the conclusion on the peer review of the pesticide risk assessment of dimethenamid-P published by the European Food Safety Authority (EFSA) in 2018 <span class="citation">(EFSA 2018)</span> were transcribed from the risk assessment report <span class="citation">(Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria 2018)</span> which can be downloaded from the Open EFSA repository <a href="https://open.efsa.europa.eu">https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716</a>.</p> -<p>The data are <a href="https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html">available in the mkin package</a>. The following code (hidden by default, please use the button to the right to show it) treats the data available for the racemic mixture dimethenamid (DMTA) and its enantiomer dimethenamid-P (DMTAP) in the same way, as no difference between their degradation behaviour was identified in the EU risk assessment. The observation times of each dataset are multiplied with the corresponding normalisation factor also available in the dataset, in order to make it possible to describe all datasets with a single set of parameters.</p> -<p>Also, datasets observed in the same soil are merged, resulting in dimethenamid (DMTA) data from six soils.</p> +<p>Residue data forming the basis for the endpoints derived in the +conclusion on the peer review of the pesticide risk assessment of +dimethenamid-P published by the European Food Safety Authority (EFSA) in +2018 <span class="citation">(EFSA 2018)</span> were transcribed from the +risk assessment report <span class="citation">(Rapporteur Member State +Germany, Co-Rapporteur Member State Bulgaria 2018)</span> which can be +downloaded from the Open EFSA repository <a href="https://open.efsa.europa.eu">https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716</a>.</p> +<p>The data are <a href="https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html">available +in the mkin package</a>. The following code (hidden by default, please +use the button to the right to show it) treats the data available for +the racemic mixture dimethenamid (DMTA) and its enantiomer +dimethenamid-P (DMTAP) in the same way, as no difference between their +degradation behaviour was identified in the EU risk assessment. The +observation times of each dataset are multiplied with the corresponding +normalisation factor also available in the dataset, in order to make it +possible to describe all datasets with a single set of parameters.</p> +<p>Also, datasets observed in the same soil are merged, resulting in +dimethenamid (DMTA) data from six soils.</p> <pre class="r"><code>library(mkin, quietly = TRUE) dmta_ds <- lapply(1:7, function(i) { ds_i <- dimethenamid_2018$ds[[i]]$data @@ -1629,28 +1674,54 @@ dmta_ds[["Elliot 2"]] <- NULL</code></pre> </div> <div id="parent-degradation" class="section level1"> <h1>Parent degradation</h1> -<p>We evaluate the observed degradation of the parent compound using simple exponential decline (SFO) and biexponential decline (DFOP), using constant variance (const) and a two-component variance (tc) as error models.</p> +<p>We evaluate the observed degradation of the parent compound using +simple exponential decline (SFO) and biexponential decline (DFOP), using +constant variance (const) and a two-component variance (tc) as error +models.</p> <div id="separate-evaluations" class="section level2"> <h2>Separate evaluations</h2> -<p>As a first step, to get a visual impression of the fit of the different models, we do separate evaluations for each soil using the mmkin function from the mkin package:</p> +<p>As a first step, to get a visual impression of the fit of the +different models, we do separate evaluations for each soil using the +mmkin function from the mkin package:</p> <pre class="r"><code>f_parent_mkin_const <- mmkin(c("SFO", "DFOP"), dmta_ds, error_model = "const", quiet = TRUE) f_parent_mkin_tc <- mmkin(c("SFO", "DFOP"), dmta_ds, error_model = "tc", quiet = TRUE)</code></pre> -<p>The plot of the individual SFO fits shown below suggests that at least in some datasets the degradation slows down towards later time points, and that the scatter of the residuals error is smaller for smaller values (panel to the right):</p> +<p>The plot of the individual SFO fits shown below suggests that at +least in some datasets the degradation slows down towards later time +points, and that the scatter of the residuals error is smaller for +smaller values (panel to the right):</p> <pre class="r"><code>plot(mixed(f_parent_mkin_const["SFO", ]))</code></pre> -<p><img 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" /><!-- --></p> -<p>Using biexponential decline (DFOP) results in a slightly more random scatter of the residuals:</p> +<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAIAAAD17khjAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdd1wT9/8H8M+t7AAhbJAlqIAiWPeuuOtA/Vlrrau1dmi139JWrbbaWldtnW21Vm3FgXtT9564EMWB7L0SAiRk393vj1iKgAgSCMb388Gjj8vnLp97Xxp8cfODsSyLAAAAAGBdcEsXAAAAAADzg4AHAAAArBAEPAAAAGCFIOABAAAAKwQBDwAAAFghCHgAAADACkHAAwAAAFYIAh4AAACwQhDwAAAAgBWCgAcAAACsEAQ8AAAAYIUg4AEAAAArBAEPAAAAWCEIeAAAAMAKQcADAAAAVggCHgAAALBCEPAAAACAFYKABwAAAKwQBDwAAABghSDgAQAAACsEAQ8AAABYIQh4AAAAwApBwAMAAABWCAIeAAAAsEIQ8AAAAIAVgoAHAAAArBAEPAAAAGCFIOABAAAAKwQBDwAAAFghCHgAAADACkHAAwAAAFYIAh5YoQcPHgwdOtTNzU0ikYSFhV27du15S2ZnZ2MYRtP0lStX/P39G7PIqgrVzLcXS8zVW2hoKPas0aNHl29vPTuXy+Vm6adajJZJe/9B8ZFCc3UoFovLPwQHB4cJEyaUlpa+cJbJggULMAxbs2bN8zpPSUkZOHCgRCLx9PT88ccfqy5w9uzZdu3aCYXCVq1a7dixw1wbBcALQcADC9PEPS7eebRk/0l9Ro5ZOrxz507Hjh1DQkKOHz9+4cKFdu3a9e/fPyEhwSydNyiFlrmVpzdjh4sXL86r4M8//zRj5w2E0TIpY+JopTFz5uPiw2bL+B07duTl5eXk5Pzzzz9xcXFff/11bWYhhHbu3Onj47Nr165qu9Xr9b169fLy8rp///6WLVtWrVq1efPmigsUFRWNGjVqwoQJiYmJn3322YQJEx4+fGiujQKgZhDwwJJkv29XbNmPURSj0eYv/E15+kr9+5w+ffqXX365cOHC4ODg4ODg5cuX9+7d+/fff69/z68cGxsb5wrs7OwsXdELsDom9b37hC3lGxXsdzAk839my3h7e3tnZ2dXV9eOHTtOmjTp3r17tZkVGxubnJy8YcOGa9euZWZmVu02JiampKTk119/9fDwePPNNz/55JODBw9WXOD69etOTk6ff/65m5vbtGnTvLy8YmJizLJFALwQBDywGE3sQ31KputPs2xHDZCMG+a6OKI46ihdoqxPnxkZGdeuXfvss88qNm7ZssW0W3bixIn27dsLhUJ3d/cFCxZU24PRaBw9evSQIUN0Ol1ycvLAgQPt7Oy6deu2bdu2+hTWBFX7aVTbeP369c6dO9vY2HTp0uXmzZvlPRw9ejQgIEAsFr/99ts6na6e9bA6JmXcfVxAeG8OwkiMHyz2O2DOjDfJzs7ev3//yJEjazNr586d/fr169u3r6+v7+7du6u+xcXFZdWqVRRFmV4WFRXh+DP/qPbs2fP8+fOm6dzcXLlcHhQUZK5tAaBmpKULAFZOE/dYfT22/KW4fw+Oj4epXbHjMEYSRZt2l7dz/LxUp68aZUVVl6+lBw8eSKVSBweHio329vYIIbVaPWLEiKVLl44ZM+bevXtDhgwJDw93dHSsuKTRaBw3bpxGo9m3bx9CqF+/fuPGjdu6deuDBw/effddqVQ6aNCgl/kUENIa2WXXlW4i4sMQoWlaz7AIoRIdm6uiW0lJmkE3cvX5ZfTciyU0g+4W6AkMa+tEYRjytiHL32XqoZYrnT59+vTp08tfqlSq8ulqP40WLVpUbXRzcxswYMCKFSsGDx68adOmt956Kyfn6cmUyMjIS5cu5ebmdu3ade/evePGjatNVYp9+coLCoSQS4R32a0S0zRCqPS0HCMwUW+JIVtX3u75a2Da+/HyrXaUK7diJy4R3hwvXi0/B4TQwIEDy6eDgoImTJjwwlksy+7atWvhwoUIoVGjRu3evTsiIqJSt/7+/uWXbpw7d27btm1RUVEVFxCJRCKRqKSkpG/fvo8fP549e3bHjh1rXzYA9QEBDxoWaW/L8fUsf4mLheXthIM9RjOmuaZ2Vq8nJDa4jajq8rVE0zSGYdXOoigqNja2ZcuWDMO4uLgIBAK5XF4x4E3pHh0dnZeXx+VyDx06pNPpvvvuO4qievfu/cknn/zxxx8vHfAUgQU5kFI+UT5tZBBCqFTHOPLwllKSYVGxhinRMm0cKIZFDMNiGBbsSCGEnIVEpR5qafHixe+//375S4FAUFxcXMOnUW3jhQsXOnTo8MEHHyCEvvnmGwcHh5KSp1cCLlu2zMHBwcHBoX///jKZrJZVcTz5ghAjQgi3IcqnEUK6VI0+Vc0PEFVsNyr0GIkJQm0oF07FTnCbOnwOCKEdO3b06dMHIaRUKhcsWNCvX7+4uLiaZ127di0vL2/AgAE6ne6tt9766aefUlNTfXx8qnau0Wjmz5//+++/b9myZfDgwVUXEAqFS5cuPXPmzNq1a8PCwjp37lyn4gF4ORDwoGFRzVypZq7VttuNGlD480bpJ2MJiS1CSPso2ZBdIOgSivPrsGdWSWBgoEwmKywsrJjc+/fvj4yMPHDgwNGjR0eOHEkQhL+/f6VDqQihtLS0rl27urq6rlmz5ptvvklJSZHL5Z6e//110r59+5cujMDQ/7USVJ2uqL0rZ+ZpxTuBAoTQu0GVF3jeu2pgOgdf7SySJKt+GtU2pqenl++k4jj+8ccfI4TkcjlCyMvLy9TO4XCqXUu1hB1shB1snpbRgSqfdpjsnvX1k6IdufZjXUzLFB8uyJiR4HcoRNDOpk4bXpXpRDtCyNnZ+fvvv/fz80tJSfH19a1h1s6dO3U6XcUPcPfu3bNmzarUc3Jy8rBhw1xdXW/fvt2yZctKc01/UdnZ2YWFhYWFhSUkJGzfvh0CHjQOCHhgMVw/L5shfXK+WsoL8GO0On1atuOMCfVJd4SQj49PcHDwypUrFy9eXN64ceNGW1vbY8eOLVu27ObNm6ZYqnpTnIeHx9atW8+cOTN8+PB33nnH1dW1ffv2ly9fNs3NyMgwGo31qa1JqfbTqLbR3d39zJkzpnexLLtgwYIJEyaYrter+kdSvWDI46cWWV8/SRoa63ckVHVJkTEjwW9/2/qneyWmsiuesKg6i6bp3bt3//333xMnTjTNioiI2LVrV6WANxgMgwYNGjp06PLly6v9NNasWXP79u1Dhw6ZXrq7u2s0GvNuDgDPAxfZAUuyGfKm67JZgq7tbAb2dF89j9em8g5QXWEYtn79+p9//nnWrFn37t2LjY2dPn362bNn582bp1AoCIJACKnV6jVr1iQnJ5eVlVV8L4/Hw3G8X79+4eHhn3766cCBAxMTE1esWCGTyS5fvtyxY8cbN27Us7ymo9pPo9rG0aNHX7x4cfv27TKZ7Jdfflm9erVUKm2osjDk8VMLYSfbJ31vZ36e4H/YDPvuJiUlJXK5XC6Xp6enf//9956enq1ataph1vnz5xUKxfDhw8t7GD16dGxsbGJiYsVujx49KpfLp06dmpqampycnJycnJubixDav3//nTt3EELDhg07ffr0/v37FQrFqVOnIiMjw8PDzbJFALwYC4DVuXHjRlhYmIODg1QqDQsLu379Osuyer1+3LhxYrHY19d3+fLlc+bMkUgkd+/eRQgZjcbLly/7+fmZ3p6Xl2dnZ7dz587Y2NiePXuKRCIvL68VK1Y0dNklWnpznMpcvYWEhPz666+VGrOyskzbW+2nkZOTU7WxsLDQ9KgWgUAQGhp6/vx5lmVNZ9yNRqOp23feeWfVqlXmqpxl2LyVaeq4UnP1JxL9d1UHl8vt2LHjzZs3a541ZcqUoUOHPlMUw3h6ei5cuLBiY9V7MYYMGcKyrJ+f3+zZs03LHD58uGPHjqYH3WzatMlcGwXAC2EsyzbCnxEAAAAAaExwiB4AAACwQhDwAAAAgBWCgAcAAACsEAQ8AAAAYIUg4IHlyfWFGlpt6SoAAMCqQMADC6NZekXK99uy/zBXhy8c4du8zDXCutlZdhD0Fy7QmEJDQ7FnNWvWDFX4f1c+kZ6eXj5yDACvOgh4YGFnZNESyv6xKj5ZbbYh22se4buuOnbsaHpoSQNRMeyvJUpjhRtWM4zGrcqyGt5SS5YaBP2FCzS+xYsX51XwvP+hUqn0t99+q6Gfhv4yAGBG8KhaYElKY2l0wd45fktS1InbszZ82+JnDFU/VEydlD9d3NXVddKkSXv27HnhW1iWNRgM1T5TvbCw0GAw1L+q5+HjWLzBECFX/CKVkBiWYTROLJBPtxXXv+caPocaZpkGQT927Fj//v0zMzNNO7sVlQ+CTlGUh4eHaRD0iqPavHCBaumTPjLKqhmSFWEUr/UZXNimzttfQQ2P5a9IJBJNnTq1hgUa+ssAgBnBHjywpP1527pK3nThuneR9KJwztWic+btv9II37du3erevbtYLA4KCjKN756fny+RSE6ePOnj43Pt2rVly5Z5enoKhcJevXqZHkrau3fvzMzMwYMHmwaQbYgR4gmE1kolRoS+kCtSDIaJBfKpNqJRwrqNK1OzRh4E/YULVItwGodxvQUd8wSdFeU/HL8/cUEALmykMdQrHqKv+m2p9GUAoImDJ9mBhnW39MY1xYUBjsN9BS0qTZ+R/ZOuTl4asP5J2YNrigvBNu335kaOdXv/dsn1qsvXfo1isbjiOCJBQUFnz551cnIqKiry8/ObO3fu+PHjY2Ji3n333ejo6JYtW3p6eo4bN27JkiUPHz4cPnz48ePHPT09Z86cSdP0wYMHEUI+Pj47d+7s1KmTTqcLCAgYN27cjBkzTCPEb9q0KTg42MPDw2g0mp7i/jxalp1fVKyv0u5DkjNsxVqW/VauuKbXK2imJUV6/Zsx5XMrvrcth5okFlXpqbafQ82zWJb18fFZuHDh+PHjZ82adf78+ZiYmBrWcu7cufDw8KioqGqHSa12AWPhdrr4dLULM8oYREpw/r//u1mGLj5NiN9AZDVPv8cwDuX1I0Y5Vp1VSWhoqOmZxOXCw8MPHDiQnZ1t+n+Xl5dnmsjKyvLz8zMYDNV+W3r27Fn+ZXjhSgGwODhEDxqWG8+zvW1XB45T1WmFQfaW8ygBITS1txQFPVLeS1A9rHb5Oql2hO+DBw82b948IiICITR06NDJkydHRkYuWrRIr9fPmTPH2dk5NjaWYRiZTPbGG2/s2rWr6pHY48ePVx0hvuZTtuU4GNZfwDdU+XvakSBMc9/gcS/r9C4EQWFYXx6XwLCKcyu+152s7a+tZQdBf94CuLAdhgurLZi16WrIWEi4TMUIW4QQXXSAEAaTTs85to/hGFHboWgWL15c8RwBl8uteflqvy09e/as5eoAaAog4EHDcuK4OHFcqk5naFKKDQo7Snqz+IqpJUH1wEvguzdn6yCncBvSrtLydVLtCN+mnbPyZfz9/Y8cOWKaNo2OOnDgwOXLly9atGjs2LHt27f/7rvvwsLCKnZbnxHicYTCnj8SbqbR+Eepaoat+P+Egs/lihMa7QqphMSw2ry3BpYaBL3mBXBBABIEPK9mVpfDKG9ymv/K6rP1qV/y2pzDuJUvAngJtTwHX66GbwsArwo4Bw8sw8Dog8Qhd0qu3yq5Wv6TVPY4xLaD0mjOu9rKR/h2d3dPSUkpb09JSfHw8DBNm46up6SkvPnmmzExMQUFBcOGDZswYUKlm99MI8Tn/ismJmb16tX1r1DJMJMK5NNsxWNFQgrDVkoleoS+V5TUv+eK6jQIevloVF988UXVa+lNg6APHDjw5MmT1ab7CxeoAekeQZecZVS39enfki5TzZLuL6GGbwsArwwLjWIHQEMRiUS7du2SyWQymSwtLW3ixImenp46na6wsNDW1nb16tVFRUXHjh0Ti8Xnz5/Py8tD/w57unHjRh8fn3v37slksiVLljg5OTEMw7Kst7f3iRMnWJZVKBROTk6//PJLYWHhpUuXnJ2do6KiygdgfemCjSwbp9NVbNEzzH2dvn4fw3M/hxpmnT59msPhKBSK8k6uXbuGEHry5EnFnvfv329vb//48eOkf+Xk5LAsu2/fvtu3b9ewQC0ZCrapY0PVd9qwtLqeH4JJSEjI0qVLZc+iabr8/135RFpaGkmSLMtW+21hK3wZAGj6IOCBtalh8O/r16936dJFJBIFBARs27aNZdmKAW8wGD799FNHR0eBQNClS5erV6+a3jVr1iyRSLR7926WZauOEF//gG8gFhwE/XkL1Bqtie9vlB+s1/ZXEBISUnXfJi0trYaAZ6v7trDPfhkAaOLgKnoAAADACsE5eAAAAMAKQcADAAAAVggCHgAAALBCEPAAAACAFYKABwAAAKwQBDwAAABghSDgAQAAACsEAQ8AAABYIQh4AAAAwApBwAMAAABWCAIeAAAAsEIQ8AAAAIAVgoAHAAAArBAEPAAAAGCFIOABAAAAKwQBDwAAAFghCHgAAADACkHAAwAAAFYIAh4AAACwQhDwAAAAgBWCgAcAAACsEAQ8AAAAYIUg4AEAAAArBAEPAAAAWCEIeAAAAMAKQcADAAAAVggCHgAAALBCEPAAAACAFYKABwAAAKwQBDwAAABghSDgAQAAACsEAQ8AAABYIQh4AAAAwApBwAMAAABWCAIeAAAAsEIQ8AAAAIAVgoAHAAAArBAEPAAAAGCFIOABAAAAKwQBDwAAAFghCHgAAADACkHAAwAAAFYIAh4AAACwQhDwAAAAgBWCgAcAAACsEAQ8AAAAYIUg4AEAAAArRFq6gPpiWTYuLo6maUsXAkC98Pn8wMBAS1fR1MHvO7AOjfP7jrEs29DraFAJCQkhISFBQUGWLgSAerl7925ZWRmXy7V0IU0a/L4D69A4v++v/B48TdO+vr63bt2ydCEA1ItAIGAYxtJVNHXw+w6sQ+P8vsM5eAAAAMAKQcADAAAAVggCHgAAALBCEPAAAACAFYKABwAAAKwQBDwAAABghV752+QqYZQ3DFlLqp1F2A8jnSc3cj0AAACARTR2wCuVyqioqPj4+Pz8fKPR6Orq2rZt2zFjxtjY2Jilf4znxahuUV4LMa73f62sUZ/0IenysVlWAQAAADR9jXqI/sqVK+7u7uvXr2cYJiAgoE2bNjiOb9y40cvL68aNG2ZZBUY5k25f0MVnCNve5T+s5gkuDCEkA8yyCgAAAKDpa9Q9+BkzZvz4448zZsyo1L5169bp06ebK+Mpt2mauK50yVnCtg9CiDUqDDmruIGHzdI5AOClqdXqli1bZmZmWroQAF4LjRrwycnJw4cPr9oeHh4+c+ZMs60G43A85xvSviGCLyOMNGQuJBxG4/xWZusfAFALqampkZGRFVv0en1WVtb333+PEJo/f76F6gLgddGoAd+7d+/Zs2evWrXK2dm5vFEul8+ZM6dnz55mXBFhP9SY//f9lIg4lhxVcpTXNsaMnQMAaoMgiG3btmVmZg4aNIgkSYSQ0WhECMXHx1u6NABeC40a8Bs2bJg8ebK7u7uPj49UKsUwrKioKCUlJSwsbMeOHeZZB6NlNI8RQjppuGvql0KcX2T/lqsundWlYxwPjHIwz1oAAC/i6el59+7dmTNnPnr0KDIysnnz5mVlZSKRaM+ePS98r1Kp7Nevn+kPgkrt6enpDVMvANamUQPeyckpOjo6PT39wYMHubm5LMs6Ozu3bdvW09PTXKugSy/rHo/GhcGlepkAIx0ZdUHRcZ36AatJpFymUp5wVBCAxiMUCjdu3Lhv374+ffrMmzdv7NixtXyjWCzevHmzRqOp1H7x4sXZs2ebu0wArJMFbpM7ceJExdvk8vLyzHibHGEXhos7l9gN+qU4tlmaC5ml1vYVd+V7v5GzlHT9xCyrAADUyahRozp37jxx4sTdu3fX/l2BgYFVG3NycjAMM19pAFgza7tNDiGM8l6MZy3rlN2267SeXZcMaXbUQ5iznHH9BKOczLQKAEDduLu7nzp1aujQoe+9956lawHgdfEq3SanVCqrnpMrLS1lWbZiy029kqv16nT2Dr24r3OIBzX7tltb5iDDHVf/DQAAvCwMw6r+7gMAGs4rc5ucSqVq2bKlVqut1G40GtVqdflLPaM7cPlG+NwIyW8z8873SNjl5vP1lqxVHyR2scucltaM513vjQAAAABeAY16iN50m1x+fn7FRrlcHhER8cLb5EQiUU5OTlEV27dvN92BY/LoZtyw6a22fmazw3aIpkOkocPJZK5g9vSBfX61e7TlToNsFQAAAND0NGrAb9iwobS01N3d3d/fv3Pnzl26dGnZsqWLi0tGRsbmzZvNsgr3eHeaT2YGSO45fOPCVwR13/eb869OlDorCPO91twsqwAAAACaPmu7Ta5wjPzhvTszp7bfOxvP2/MB1zfDz4VxuUuL1fIHC5R+qK25VgQAAAA0ZRYYLtbLy8vLy6uBOvc05o0ZtwUN+Ku9jlD9j+CpmenGY1gQy0qNLA5jxQIAAHhdWNt48AJRB4wxpK+fet8lwDHRYCszFLXg2chKgxYu4Nv1sXR1AAAAQCNp1IDfsWPHpUuXnjd33bp19V+F+i5RsPltw//OnKZ7eO/m97tbvPYP6WR6u0dsG80TL4cp9V8DAAAA8Apo1IvsQkNDFQrF+vXrVSoVrwqzrKIoXlV8ebBYUOrtlX69hyODoa6uZBfO3oKYjwuvF5tlFQAAAEDT16h78AEBAdu2bTt//vzs2bODgoIaYhXrfITGHtLwZRPGzFgcVXYYZ9jAhwvT4t6KK3A89qHTgYZYJQAAAND0NOoePEKIJMnPPvvM1ta2gfqX8vFjHRz2cXtQ8c2m2EaSbZK64HcenBj9+/95CPmNvbEAAACApVjgIru5c+c2XOcz24snSErkj0/g2DeTHd7TT7dXX/n4zTGF/T0whwHdG269AAAAQJNibTu1JI6ERXJ7ZzuPaT3upQ5TUQL1N9OknvaCwkIeCYNQAQAAeF1YW8AjhCh3Z31qFmukH9qHcu67lYTf0SWmUs1cLF0XAAAA0His7T54hBDl4UL4e+cs2NozthAfUExeVxdmiZrfbsciBLvwAAAAXhNWtwfP0qwu7WJYm8JDXMxOrfMuLvoim2HpEx+duK1RWro4AAAAoJFY2x48XXxKlzB2YJ4zvQTTOSMxLW879BddP6YPXaDZOAJ9tt7SBQIAAACNwdr24Am7fvp0b8XhKeJBD1Z7HNBqxEvPbjngtxHniPJX986el2TpAgEAAIDGYG0BjzBCeetzybCNZbcL+9AuXK6KV2zslPVhyc1h+kInuyEOlq4PAAAAaAxWF/AINVs+UalqUbx54a67CpZlWwuu+xZpcn/sj+1rI+xsZ+nqAAAAgMZghQGPEBKMXG379tHPZtzRacT9/Dcqfp2wco3ErQdr6boAAACARmKdAX+Ydkz2eo8/ewdXyxIGLOrd1rkdS9fln7B0XQAAAEAjsc6AnyQWubuMF7eNwSWlT+4OM4bGdNEfVShPJ5Y9snRpAAAAQGOwzoBHCK3L/2eF7ZxSzLUU2YuOjB/p/iFC7PbsDSyCA/UAAACsn3UG/PHi2ENE/0wCk5f6uRfIOmwqOLlUwyUl2dqM64oLlq4OAAAAaHDWGfBprF1z7fG3b9qLztmxUuWj1efaHVF1jhxIYJQtKbF0dQAAAECDs86AD9Vf63k93mtl2eNQtVCan+5449KaNU5XUzts88vUplm6OgAAAKDBWdujahFCrCG/TcpnAY4Scu0djCgzuGrnykhKaoctWxaY4GWImopmWLpEAAAAoIFZ4R48RkoZQyd9uv8ZzhcK8ah0qnn+mX4c11k0zi943N2Qo7N0gQAAAECDs8Y9eKMC590WhLIdbhtyfVk/Q3qZg7Dg4UIxWWIbesd15CJLFwgAAAA0OGvcg6ccKdfPcGEA301MfT1Wr5CWSOeKjWWMges8YAnCYVB4AAAA1s8KAx4hRLlHMBoZQg/SestJUSlevFqrcVXHdD173MnSpQEAAACNwToDXpvA5P/+jlrM9Ru9C7ctdWh7g3XNX93lK9HXycVHCi1dHQAAANDgrDDgi9S6wphdLM7hCTg8XENwDAzFvcrpSbtedw3PUuzLt3SBAAAAQIOzwovsvj8Tu6jVl8bmPFyFOfC0CLFcUtZFez6s7JhhoJ1dd3gcPQAAAOtnhXvw73cKpQlnRuDx8NKv8rh2iEUnS8fHRn6FI1btFY4LCUsXCAAAADQ4Kwz4zfc0vxat4BhSznXCD92frSl2IqPb+L8RrSZ46cJ5lq4OAAAAaAxWeIh+dV87hAZpH7SfRH7Vf8y58Md+LfDHroE31tl+G4c96oY6WbpAAAAAoMFZ4R68Cen/l5hWTCz6S2Xn6DR8P8bxHuwx2IVJt3RdAAAAQGOwwj14E5LjynEcP4X5Va1pz+MVc1sdCBC2+EncwtJ1AQAAAI3BagMeIcTxXqzK32UnuIJRPrgwGCFEszSBwUV2AFgjuow1VvuUCxLjuiMEj7AErx1rDniE8/aVzXlH+IPqri83SKeIPHDsrVJ/YUAnux6WrgwAYGb6tC+NRUcw0vHZZprVZfJan8JF7S1TFgCWY9UBj9BfOW+7CjlBaD9GEKpz171GvqWh1ZYuCgBgfqTbTLr4DK/NGYy0L2805v9llO3CRW9YsDAALMVqL7IzwXFUhDx4vBI9SRAS287GkISy+Hulty1dFwDAzHB+K8J+iCH75/IWllYasn/ieC+F4/Pg9WTlAS+iMLG9M0egLGUYytXxTt7Fm4qrmzJWG1mDpUsDAJgZ1exbWraXUT99WqUhaykhGYQLQyxbFQCWYoWH6C9l6pR61jTNsEjOteeISvama3mugef1u3EepmHLjubvCXd517J1AgDMCyMllNv/DOnfcAMOsNoUunAXr+1VSxcFgMVYYcCfTNUW6xjTtEzLnCwUjnXQXE5XFjtJSb3dINc2Srr0WOHBN6WDbCmJZf2j52UAACAASURBVEsFAJgX6fKhsXArXXzSmPcn5f4FRsEI0eD11dgBr1Qqo6Ki4uPj8/PzjUajq6tr27Ztx4wZY2NjY65VLOxpWz7dZ0eBHZcy6kXvBJbsLVjU3rbrpGbT1HRZxMMPIrPXf+Y9x1wrBQA0CRhJeS7UJ03FKCnpMsXS1QBgSY16Dv7KlSvu7u7r169nGCYgIKBNmzY4jm/cuNHLy+vGjRsNsUYhhQc6kKVihxuqkxiLjXX7ACGkodUjXcfFl95JVSc2xEoBABZE2IURkoGU9zKEcSxdCwCW1Kh78DNmzPjxxx9nzJhRqX3r1q3Tp09viIwXUFh3Dy7vHM/W5tYbHd4p+2kbFTHxu8SZa4K2nig49GfmykUtf8PgClsArAun+W+WLgEAy2vUgE9OTh4+fHjV9vDw8JkzZzbEGn1syTI9q1VzpWTxkYI9h/vpsISLBmSY+eA9I8voDbonqgctRa0bYtUAAACABTXqIfrevXvPnj07Pz+/YqNcLo+IiOjZs2dDrHGoP++v+2U6oS3HVigghDya4jNcW0rCJ0Ri0kbKceQR/IZYLwAAAGBZjboHv2HDhsmTJ7u7u/v4+EilUgzDioqKUlJSwsLCduzY0RBrpAhMZ2S1Ia0FGLW85beKrQdxoYAf3lthkDlz3RpijQAAAEBT0KgB7+TkFB0dnZ6e/uDBg9zcXJZlnZ2d27Zt6+np+cL3qlSq0aNHG43GSu0ymaxqYzkugWlplncFd7TLRi0R5emmT0pP1yTvzY2c47ekvtsDAADgtcewzNbs9aNcxotIsaVreUZj3yZ3586dmJiYbt26DR48ODo6+u+//zYYDGPGjBk7dmzNbxQKhXPnztVqtZXar1279vDhw+e9i0diOiPLslI++xAhJOrZge32hhA3tBK1qf+2AAAAABeKTl4pOkdgxHvuH1m6lmc0asBv27Zt0qRJgYGBERER8+bNW7du3UcffcSy7Oeff15cXPzJJ5/U8F4Mw7p37161XaPRYFj1l8HTJWc9VHfm+BhsCx6xnEfGnF9M7TjGHeH8fv03BwAAwGuujFYdytsZ4btgXfryXtIBzXjelq7oP40a8AsXLty8efOECROio6OHDBly5cqVrl27IoS6dev26aef1hzwL0Gf8jmuy3oDFzH2Rgzp9FmrEUIIMyJao+J40uLODhx4yhUAAICXdyhvZ3u7Li1FQcOc396Zvfmr5j9YuqL/NOpV9NnZ2d26dUMImf7btm1bU3twcHB2drbZV8fx/olFWIbbMY38H41SKuiYIeiYQZc4YUSzWJb6p2C/2dcIAACgyVKz7Am1pmJLEcNc0FQ+81szVpfGlN01/RQoTmYVHQ4Xt2bK7vbkuamMJXdKYsxacr006h58u3btVqxYMXv27N9++w3DsJ07d37wwQcIoZ07dwYHB5t/fSyNKEdx2jCB/UQWFWmzV+K6NEKUweqGtBX4CQih+dcIAACgqaJZdnWJMs1If2QjQgjJaWZyoWyoQNCrLrdL65NnMpqHGMcDIaTR5U4iBGT6PB2tYnXp41ru3pSzuY1NKNU0nqLYqHvwa9asOXjwoKen5969e2NiYr777rsePXp069Ztzpw5y5YtM/vqaOUVDMMlpIIQPMQQIoxyWr4PMbYscdKesu0saZA77wEAJizLbt68ediwYeHh4ceOHStvz83NHTp0qAULA68tMY5vdXI4olavK1UWMcz7hfJefN6HNqI6dUI1m4cwLi8o+rHXTxvEfaUhN3ltzhE23UmXqS3sejfje58qPNJA9ddVowZ8SEhIenp6SkrKkydPOnTocPv27YkTJ44bNy4+Pr7aC+jqiXL/GrE0YfcmY7yOc10QRrGMDhGB+tTeGOWUoHpQYlCYfaUAAJNFixbNmjWrbdu27u7u77zzzr59+0ztarX66NGjlq0NvLakBL7F0eGwWjM8r6AnnxthW+dxznBxB9ymsyFn9YG87UbGsDL1h78TPi2V7V5vMP6cMr9Al380f6+e0TVE8XXV2LfJkSTp4+NjmnZxcZkypQGHe8JIO8r9q9K8gzxaZyxBDP0bZT/UWHJLnzAeIXS75KoDx7m/47CGKwCA19mGDRv27NnTu3dvhNCoUaOGDRvWunXrli1b1vLtGRkZVR9xkZ+fz7Jstcsz6oes5kk1MzCKsHsT4YLaVw7KDOyCyyXL37SzdCENAsMQwWJGhBzjNcZWfFJK1bUHjucP2ns9p/pvKcFFCCGXtJllLp/0lTw9LkXhHA7ONXPRL8UKx4OviHSenJOw0dNpGMnuZpCQ0Tzh+i4SfD8UIdRLOsDAGixdIABWS6lU+vv7m6b79OnzySefTJs27eTJk7V87+DBgzUaTaX2srIymqarfYsxfzNddAS36fpMK11GF5/mBV/GBYF13oDXmErPXMnSW7qKBlHEMJML5G8KuGOu6jMnx8d4cDsde4N0rNspc4zjRrp86FCw2d1/Ey0/aGBp+2ZzEEY0UM0vrVEP0VsARvxZ9A2mvmHQiIv5bTHSnrB/+keWO8/Tm9/cstUBYMU6dOiwcOFCg+Hpn9E//PBDTk7OV199VcOjJ8uJxeL4+PjkKv7880+SrH63BBO0QsZSDOEYYVf+wxrycX5zjGhazxcDlqJi2EkFsr583oc3DIppjzwPtr001OZmv1uGvDofUafcZjKqW3TxGX3GAsp7SRNMd2T9AY/QY30XAy9QYeNro4mjvBdXnHWl6KyGVluqMACs27p166Kjo6VS6ebNmxFCfD7/8OHDR44cMT39wuzo4pMsoo3FJxnl1ac/pRcYdTyjTWH05r8LF7yKMAx9ZCOeeEOf/tFD353B7p0lU+YFsGOdEwfH1jnjcR7Hc77uyThcGELYmP8aMrOw8kP0CCEeiRVKFnikdy7BW9oIWiOESqPPszq97cj+N4ovC0lxiE0HS9cIgBVq3rx5cnJyTEyMi4uLqcXPzy8+Pj46OvrevXtmXx1pN0BffA5hXMJxLC4MQQgZsn7CaDViVDg/wOyrsz6ZSnpDrIpBCCGkMbKlOmbuxRLTLAGJfdlJzCWqf2boK0SIYT2uqE3pLuxoixByIHCHWc3zKTJxcKz/P6GUSx3OnRPSEWTpVcqtQcY6NwvrD/ihfnyJnX3hzmGqDgbT+HGErUh94x5CaJTre7akxLLlAWApzzuZbUIQZjjkyOFwevToUallxIgRI0aMqH/nlZDOkw05v2F0MV0YRblOp5XXWG0Sokspzx8w0tbsq7M+IgoLcnh6uVmpjjlHYG3+fUniiMRf+XQ3yfs5zW6Ykyndyzl+5FH4Z1bxkULHDz3q0hnG8fnZvOWZl/UH/LggAYGhgrwOlOHpnbiUh4vhwCmEkCff16KlAWBJzzuZbfK8i9WbMJzrt077cDhOssb8zcaCSIxlWNKBhIEnakfCw98JfHqvQX4Z/Xe8uvylNfHZ2iZx8J28Zakus57ez8WU0cn/FyfuY+/4gbtlazM76w/4xVdLPcRkV9yBi+SmFsrN2ZBXiBgG4fih/J1DnUbjTfL6CAAaVFZW1vNmyWSyxqzEXHBxJ9y2O1NyRZ/+Lc71YJgybou/EWb9/8qB2qOcOP7/tEscfAdhmMvX3qZ05/jyvda2QtZylKKc9X/1Y3INKgM7kOtkSz59rA3GoXiBfoxOj/N5t4uvtbPp3IzvbdEaAbAAd/f/9lfu3r0rlz/9C7isrGzSpElFRUUWqqteDHnf43gfY6EH1SwPF3dpslc/AQuinDj+R0ITB99haVZ5tojXSui5phWytnBH6HUIeA6BygwMV+BEkf/9g+U891PTxCdeXzlwYUw58FqbP3/+0qVL+Xw+SZISiSQ5OXnOnDmWLupllJ4tSpss990+nnLbwupw0nO5pSt6Vdly8feCrPD4fDnKlWvajxd1l9Q/3VVG5fwnn8/0mefJ9zFTgeZh/bfJcXCMZhDlbMfq8exv7lSa68rzaCKjAgBgKX/++Wd0dPTFixd79+6dmJi4atUqieTVu/i09GxR2uT4ZqtbZX7xFqMT67PfThwhM8rhYVYvg0din7ar2xPaXzmUKzcgppPn2pdP9/0JmqiHaoTQwfwdPIK/I3ujOeszB+sM+DnnS9r9lW/6uV9oSLinevR1gqHYVnbg0eHx8aZlmDINQsjIGrdk/W7RYgGwsOLi4uDg4NatWycmJiKEpkyZsn79eksXVTfl6Z4zP1kyyrvgj7Wqq1NsBzkmDrkDGd/E3dPrr2qfuQf9slZ3X98Y/9cwTr0SMLeMzlXROdrMW8VXZzdfrGd1N4uvmKs2s7DOgF/Qw+bcu46mH38NvWxViojCVUaJbYSoWXRB+ozHNzJz839YixAiMOJ2yXU1XWbpkgGwmICAgJ07d+I4bjAYMjIyCgoKCgsLLV1U3eQtTrEd5JDzXZL0PVeXWT7emwZqk2h9lpYuNir25Vu6OlATLoZ9XaQ48++g7Kc02jlFCj72ypwSj8rZNNxlrJi0Gev2we7cv5vIMDMm1nkO/kiiJrbAgBASFOi/WZyo52FHZ7l/WCI5FaRtNbM1vSo9VaN1VuchhsFwfL7/LzA2PHidLV68eMSIEQMGDHj//ffbt29PEMTw4cMtXVTdeCxtkdD3lqiXxOUrb4QQLiB8Ils/CLlGOlCO77tZujpQk5YUtdFR+mGhHCE7BqHvi4rXO0r9qFcjm3J12VyDvJd9P4SQvzDAV9DiROGhoc5vW7qup16ND7GuPMSkkUEIoebj4tQ4mj+nuW0HtuiBYzNhUeQUuzcIfeflOboOrQz5csrVUcpxtHS9AFjSgAEDZDIZRVFffPFFYGCgQqF4++2m8i/UC+kej2Y0ScYCnd9ujNExqvMkYUsiFulzabuhm5TndYV/5dTx6SWgsbWiqD8dpRMLZCyLbXaStubUeXi3RrP9gfp6ztN99ESFMV9rCFZ9MTOjFCH0hgtnTKvJC5580VXyZhOJFesM+M7unM7uHITQQ3vKmKkVlTJ/h3ocYt0YKpfgoEE5qBjDuO5CQ0YO5eqoMMgP5kVNbjbd0lUDYBmbNm2q1BIZGfnBBx9YpJi6wsVdWEbD8n9M+CpBNNeb+CHbbpQTMv7FuOadsOV3F9B2w/67TSbvlzRRJ1tR9xovIWT17HOGqMBIW2SVd1M1AZlGGkcYhmH5NN0aNd2A7+TGkfCentrOjE+2wzThfs1ML5tLaC7O62LXa1/etqme/7Ncjf+xzoA3oUsv+R1JTFiYvOr+Sd5974HnHqT007TfxmGLy9wXTRS04mMcCiFEM8YrinNvu00SElZ+1SgA1dq7d69pgmXZvLy8uLi4YcOGvSoBT7lOMxZuF7VT6Bf2lk19JF7RiVkZ57747I5LazueVPofe4NyfnqbTPY3iaVnigp+zfTZ0lrc87kZr0+fb8zfhFU5bccai7mt9hJ2YQ24Ma+rUxrt90XFGx2lBIamFhYhhML4PEsXVT0/CekneZqbi2+rdEbmrO7pzuHZPITyEEKIxCg9o2sKQ8JbdcDL9tElZ7JGd2t5p0RxMIXrXBooK9CjS8LP7i70HveTaxfTYocKdolw8aG8qHfdP7RswQBYxLFjxyq+/PPPPzds2GCpYuoM53I85xvSvuk28NKVDQHKqY8E2/fdTR/c8YA06FBwebrnLEhWXlD4H2+nTShLGXvf568gcW/7avujXKbSsj28tlcwyrm8kS46asj8kbDt1Rhb9Jq5rtX9oCje6CRtRVEIofUO9lNlcjEu6ci1fEDWzFfgrzOya1tvs3Qhz2WdAR9faMgopTlMRAfD0SPaD5eEuc57L8nT/r7DlG0qX6f9thNFvKcHVdI0yfHK2Hktfvox8eue0v4ePC/LVg6Axb333nuffPKJpauoA8J+mDH/b2PB1m6DJkf/cS+47Kxi9VqJ35Hc2XtIqUQyfnjxUWHpabnf0VBSQok62/lGtUl997735uozHuP5kI7vGjIXc3xXP21i9fqMBRyfn+Cptw3BjSQinRx8/h0ZIZBDbXF04DbYVfQGlqWe7bxqS82M+X+xugyE0AihlmZYQwb/6QwMI90+xwgb8xVbX9Z5m9zNXP2xFM2hNNFpzQcjhUuLyrCtv7fUquwYbwXXNSbE6+tmJIEQ0mfnRWVv7O8wdGHiV7aU3c7symciAXjdsCwbFRUllUotXUjdUN6LDVlLrpblSd1+ilONm/dHq+INs7y2r5B++m7m9Fslx3L9o9uRkqdndkWd7Xy2tkmd/EB1vbja3kiPr+nik4zq6XOxDDm/4YIAwrZPI23Ma8aTJH2eHfeoOUV6kA0yPkg+TffLLUg2GMtb1pYoZxdV/zWolkrPKhV3tfJjJbQYI20QaVtCi0tosaosw1CwE8Oa1tUDtfqDNDExcffu3XPnzm3oasxlcrBwMhIihBAbkXW98/vqG1+EDT/XzM1PWJjhvqKr0KErQgihU4eW63saEolHAlwo0xcyFHO39CYMDw9eN2KxuHyapmmtVrtixQoL1vMSWH6rf3hhfgmjPAlCeUQz7zfXqaWlnjr93lbNSfcsXaoS4z6zM0PYkBiOmNLqB8zFCBHlMUefPocXdJw1FGpyVhGBR8x+vFhtZH+6XrqgOwxl23icCWKWnc37hfKNjlJ/ilxbojyu0WxxdKh9D+tiVVfSPl7X7OhXSd0eqrxZxP6ajWOIXeH+dgpn1iic/+IuGlFNe/CpqanLli1r165dixYtXqVzchVhnOvYnLGiRRfVKrbddcSy37LdEwwGhJCe0Z1tn/tGWascbea8Fj8JCEFPaf+d2ZuMLDz3Crxe7lZw//59mUz2+eefW7qoOvvLdqaTMbfA7htcJMJIgmUYg1prlCncFrhgHHVSeCxT9jTONfdVScNj3Zf42/R/7oEK0uk9xBho+QFFyhfnMLt9CvM/oUypY06mNqGHorwmBgn4s+1sphTKv1eUmNLdgajDkeyvOokPjglw8J65KXjVB22F44OE5991OtXvTICUM6rLhIYr++VUswefkZGxZ8+e3bt337hxAyH0xRdf/P777506dWr02swjiR3YHNt6J3PNxP5blazjHKHuw8KiPx3tH8j3+undr1I33nGfJiREI13GnSg84sZrdrrw6ECnEZauGoAG97//1XQnz8qVKxutkvojEDrq0eqy/d3ZxerRwUW7MrKXHT7bmmZyk9JIF0enD5spb/CTRt712x+iS9EkDY91X+pv/7ZLjV3iHO9FuicTabpM7L3mgiy6p6a/N795I20PaEiDBPzjas3eMvUmR2md0r0c5fqxpmCrO3M+BfVCdJkh80dui61N8BbKZ7ZtzZo13bp18/b2XrduXe/evS9cuIAQ+uWXXzp37oy9Og8OrETMRwtsIz4sXkk4dM3meHQnS+fY2SxSlJyRRceKUwo5JZsyVn8W/97WrD8yNSnxyrsnZUcsXTIAjYH3L41Gs3r16ri4OIZhUlNT169f7+BQh4OWTUd3gc27ItEfg3u9f+Zql/7dJePDbYaE6RPTSWep17oATjNeUvjdpPBYj19avijdEUIIF3eRCdud43d802nkMOcxUdkbWcQ2wlaAhra2RJlkNH5rZ/uVXJFY4Xx8HWAcjtfCTvT3ODIYsn8mbN/ERW+Yu0wzeGYPfubMmT4+PqdOnQoLC0MI6XTWcPhoXCvh5p2tuG+tle32I5wXMT55g6SdBwn4RZK13yf8b3J2X/8BIxFCJQZFZNa6PH3Od/4/W7pkABrDkiVLTBMjR45cu3bttGnTTC///vvvw4cPW66ul3dRq9uhVE49duGvwb2bHz3fIjGN4+spmThC9ygZG9rH64/A7G8SnaY3sxteqxGi9YxuDSue6jsfQ1gvaf+L8pO3S661t+1anwpzVfTWB0+folNmYFR69qcYpeklB0fT3hBR+Ku6K/WqKD/v7kDgYhyb8u/5+Lr2Q0gG5hnW+xl/NBYc5gVfbohS6++ZPfj58+dzOJyBAwcOHjx469atJSUllirLjPgUpjGwpOM7JM/VWCzR6J+OPBGdvyfEtqMp3RFCtpTkM59vWggCT8uiLVcsABZw9uzZkSNHlr8cPHjw2bNnLVjPy7mo1X1TpPiVyx11++ESF6dvR/aTL5/lOHMi5e7MaLQIIYzAPJa1qGW6I4SiC/a1EAb6CwMQQhjCxrpP2ZXzVz2HEiFxzJbz9MeGwjAM/feSi2NN7xivlcmn6Zs63Vanp+fdBwn4X9rZRKlecrCxeP783rxtpOs0jPPiA0IW8cyfLQsWLFiwYEFsbOzOnTu//fbbDz/8ECH0999/h4eH29nZWajC+uISmJFhaRYJQsRFT5x0+jwxQgzLxJXe0jHaOyXXy5dkEcIQytZl/J/reAsWDEAja9as2dGjR02/7wiho0ePenp6WrakuqIRWqQoWecgbcOhsnCsY1rWD17uO/Ov+mWPQogR90Xq6/MqLk86jef4rqmhwyKD7Jz8+Hz/X8pb/IUBPnz/eg4l4ijAPwp9+sTM/DJ63xNt+UvQCJwJItLpmdNPQwX8oYKXvPS9FPOPxraMcXlTez/BkFtIOkv5bVoivAndfF7NcYnQ0NDQ0NClS5deu3Zt586ds2fP/uijjwYMGPCKHrVDCH3aToQhJAgVZ7MewbqHCCEcw38O3IgQMsoUrFZHebgghGKKL90vvT3F89W7fhiA+liyZMmoUaP++eefli1bJiQk/PPPP/v27bN0UXVDIHTC9emuufTT9wqXbwx9s1N7B1uGcNM+bCf9+E+M+/SRdojRauI6Ew4vCOm9uZF8nH+0YA9CyMgaSYxECOlZ/T8F+3tJB9iQcG8bQAihLLpz7re/IZrmNPdUnb+u2HbI+bvphLip/NH2TMD7+/tfu3bNdH0NhmFdu3bt2rXrypUrz58/HxUVZaEKzeDzDmKEkOZRWbd1mM2XBRVnae8naOMTHT4bjxBqIQxMKXtimRIBsJwhQ4bExcVt3749Ozs7ODh42bJlLVq0sHRRtaW+6YpobaVG6YcIofXapIU87y6Crqfo4i3o3yeQ0MUnMUqCveh+5W6SPi2FrRFCBtZwIG97e9tuvgJ/bz5qZ9uJhzfRx6SDutKlaihnDi6ow0N11sWqjqc8/b4VqBmmTHPWaxjpIEEIhbXjjX9yVvHXPocZExuk3Lp7JuCTkpJouvKTHwiCCAsLM11294qKKzC0dqAIMYllignjMwFPebqVRp83TUso6Vj3V2OADQDMq1WrVgsXLrR0FS+DtBtiLD4u6JBe8YoiXXw/li62GzVAe28BRroYC6NwQVuEEGLUjOIcIm2Msj2cGi97DhKHmCb25ka68zwfKGPfdf+Aa9Zot+HiI1o0reeiNBGMhsH5DX6gu+xmaVJ4LD9I5HcgBBfWNuNHtuD38Hj60KPdj9XFlx++Hx5IOtgihBwFuI3/mzkRixuq4rprQmcLGs7cCyVJxUbSntKmifW6vIqzOM1cDTn5rPHpnzUqo7LYUGSJGgFobBRFmR5gRVXH0tXVFsd/HWIZQ9q35S1MySVadYfj/xdd7Ek6TkwV9KC1GZR7BMd3JUIId3pXy9KMW0RtOi/Q510sOj3Na1YLUdA/BfvNWzmfxL7qJH7xcq+Z/JXp97wvqq7U4fGxL0Edq0wZE+f9RyA/SJQ0PJZWVf9Mw6qchURrR8r04ywk7A1lraVE+UuMIFiaadDK66TyOfhJkybxeNX/lXrgwIGGr6dBjG7Fd+TjOB83lthgxhLEGssHjcA4FOlob8jJ53i6IYTuKW89VMbBaXjwOkhKSjI9cz4pKcnStdQHyfH6UZ/2FeURgUh7hJAuaQpu10cT3yxp+HXb4aNcpkw6xevXJ+N7ketHdOnlRNI33vbjMRzH2nS9M3vzIMcRtpRktOuE+U/+18O+rwOnthfhg5eQvzJdtiXHc22rlPfu+25rI+rWIBd3q2OVyaPueq4NsH3LwfYtx8yIhKThsX6HQglRnR+ATzrYK09flbw71PRSefoKL8jf3PW+vMoB7+PjIxRWHgj5VTexzdMtou24WsxGZJRXHAjS7p0hhN3T8X+CRCFKY6kFSgSg0Xl5eVWaQAjRNI3j+Cv0YCtD+reMPgvhHPW9zoS4G6NJZI0ytoRQXZ3g/cdq2dYSw/mxoQNuFRen4Kpb10WD/VRnRzWfUZt/yB+q7uVoMz71/gohJKGkfR3e2pO75ROvrxp6i15b+WsyZFty/PaHaB+X+W5vkzLuvs+W1uKeEvOupWK6I4QQhpr90vLlMh5DiB/cQr33jD4lg+vnpU/NMmTnu/zQhPYPKwf8/PnznZ2dq1301ZVYZHQREWIOhkkoJeMo1edXDHhhl9DyaVtKMsBxuCVqBMBinjx58vXXX0dFRV25cmXixIk0TR88eLBz586WrqtWWKaM1edQ7l8aMn7U3HXheEYjrL/yvF7UVS7s7mkzAKVOoB0CjyT5hWeoY5urzrr4LOXinBd2y7DMzuxN77i/T/57dd5AxxHzEqYnqB60FAU18Da9jvLXZMg2Z/sdCMmY8bjsRonzTM8GyvjMLxMk/+f8NN1NMOSxxP9B6DX5lhynac1q39UgXx7N8ty6zFHHxBlyCoTd2ws6t8Wa0umt1+Ic/G93VBcytAghzhCpHHdgDfk1LCzT5+dqsxqrNAAsb8qUKSRJUhT1ww8/REREfP75519++aWli6olhnSeyGqTMKqNIa8F5bGB1XCyIoaKejzgtf4eIRzj4j6RISVnp3umHk7gdMkiPEtsajXq6zn5MRIj3Xlehfp800+JUdFHOigqZxPDNqGTrNZBda04Z0Gyb1RwxozHpD0VFNel+FBhyXGZ+0K/lLfjWIM5nxDsvSGw+HChfHtueQtrYFMnxfMDhI5T3OvUlY8d6SchMYIQdm1n938DhT3aN6l0R5X24Pv27cvhvPhv21cOj8TURhYhJJ3kdqvQOVSfW2kB5anLROPHewAAIABJREFU4n7dTdNPyh7dK731sder8g8cAPV169atxMREmqZv37597NgxjUbz448/WrqoWqGLT+sej0G4WPdkHC4SYYiljZjbwi8xTEfL9hB2YQihbQbN3elDv4qOesfmt+P+pxYVyPvzeZ/aink1noZI16SU0aqfk7+r1E5gpMIgl9buFD6oJWFHW7vhTk8G3Bb3lHhvCsJIzD869En/O7KCbLcf/DDKnCeMuM0F/tGhiW/FIoSk41xZA5s64T6rZ3x3BFcaU9gKPBPw27Zt0+v1+fnV7+C+uofu+SSmMbAIIeOaLOING8axcsAX7/qHHxpkupexrU17AqvzpRYAvLqkUml6evrt27fbtGkjFAqTk5NflQtxMEKMEFLJ7IQiGS2X4NxAQ3Yxr/ljhFijiskMj3b86LCjBzbxipIQ2BDizkNK/+ZqtCqGoQ1hyLH6Z92wugxGmzLRpg2yaVNpFi5qjxFN5Rkm1oTVs8ZCPWFD0KVG1sBiJMaoGVZLYzzcKNObfXXc5gK/I6GJQ+6wBrb0hIxlWN+oYIxjbemOKgW8i0tND9Rl2Vd1JCUBhWmMLEKIY0cZi+yN+txKhyk4Ph761CxTwAsJUSe7HpYoEwDLmDZt2sCBAxmG+fnnnx8/fhweHj569GhLF1UrGL+zITeIJ0lJWv2/5l/vEHbYZxR/pEkq5TkWpI4fIPk/e4J/pPXhoaWGFtvHSN6TiGJjisqcyYGczaQh4Hl9GvP/MuZvxkXtKjayrIEpvcINPELYdG/4zXrtaOJVZdeLW17skL8iPeWdOI+fWyaPvOv0uRdGYDkLU1y+9DZ7+vL8Bf5H2yUOuiN4Q+y7tY1VpjuqFPAdO3aMi4sLCwsbNWpU//79+XzzP4RBqVRGRUXFx8fn5+cbjUZXV9e2bduOGTPGxsbG7Osq18ODazoaJ+pq61/sS+jjKi3A8WmmT80UdHj6B3u2NkNlVMKlNOA1MXv27E6dOul0uoEDB6akpMydO3fSpEmWLqpWGA2dvWya1/KZWTPT5VyvkKRPygxXbN1LZJGjDLl2/NbeyphxHOF9R2qejYdk6fqM8N8L27V5wJmHcV0+fl6fpNtMY+EOymsRLggsbzTmrqMxCtK9gQg72DRb1Spp2F2/QyF5P6U96nDd4+cWlAMn438JfgdCGih9ef6CwNudcSGBka/MPSN19cwHFxMTk5iYOGDAgC1btrRu3XratGlnz57l8XhSqdR0v2w9Xblyxd3dff369QzDBAQEtGnTBsfxjRs3enl53bhxo/79P09nd04nNw5CSNTFrnmgP1vlHDy3eTN9amb5y3xd7j8Fr9izuAGoD41Gs379+qFDhyKEhEIhQbwaZ6lYI6t77Ky80udNas8/NhOIoqOIITGVoPjACBbDMj5PUN97R9StsPTSIfuPEwdvlK3b6e381Za8leM0j587CjhG2lHuEYa0ORXWojDkrKS8mtATyqyP9D1XtwXNk4bfdfnau8XJN56m+/4QQWgDPguIsCWtON0RQoh9jvz8/D/++KN///42Njbh4eFbt2593pK1165du9WrV1dtj4yM7NChw8v1efjwYS6XW/MyCg2dpDCwLFt6oejmqNOqm83LZzF6fcmh0/mL1+V+v6bsWqypUc/oHinvvVw9ALwcPp+vVqstsurIyEhXV9cffvjBxcUlPT3d2dl51apVFqnkhSr9vtMlV8oueyrPuJddk5RedZBfdym7JlGec1Ge8ZBHTtdlaMp09M8pO7JOhdy2O5H7V9altN9iYgek7smJ9T2//sRymjFWvxrGqInraiz6x/RKl/KFLnV2I2wdkEXm3GtxKW91+j3fi+p7SkuX04Aa5/f9uYc+nJycpk6deuLEiWPHjiUmJo4fb4YRVJOTk4cPr+Yu8/Dw8AZ9ltaFTN3vd1QIIdKOVGbyGEaFGC1CiKXpvAVrdUnpor5dxW92Lt53XLHtEEKIwjitRJWvrwHAWv3+++9RUVHffvstQsjT03PPnj2rVq2ydFG1ggtbY3yBWj4178RgjEY8I6PLcc1cFGHAMWLwIMaD+1FxUda+YDLJzmHcBft+5Buy1ZnuCz/uRtyd+aD11FaXYk5W3y9GUF6L9elzEaNjNI/poiOUBzzfpjFIx7u6zWtesDrd72Aovw1cz1hfzw34+Pj4BQsWtG7detSoUb169Tpz5kz9V9a7d+/Zs2dXukpfLpdHRET07Nmz/v0/TxtHqlczLkKIsKd4ckZDOJpuhS+7cgfncx2/eF/QIVjYo4PLgpmq89eNMgVCKE2ddF1xseFKAqDpePToUWDgf+ebAwIC5HK5BeupPbrsHqvP43j+jndNxFiMxQ04Rrl8vY5DqS+mRr9fKE8xGIfuKJb/Ndlxwh6k/YGwf2uUY4f3uFnZoRlCjujulRiVUVltz4RtL5zfypC33pD2DeUxG/t/9s47vqnqbeDnzuzVpOlIJ92lgz0UGSJ7lSVDZCMqiAwHyJIhBSdDEMsGpSIoiAxB5OdgzxZa6N67SdNm5+aO94+U0pZ0T3jz/eSPnJNzn/PcNrnPvec8A3Vo5VNrW0pN9JBjxa0zV7KFjCMsFU3pmy7gca+8QHvJvmaguoGPiYlZtWpVQEDA0KFD1Wr1d999l5ubu3PnzldfrVd2iNqJiorSaDQKhcLPz69Xr169e/cOCAhwdnbOysrav39/0+XXRAcxOtqPAwBAZTimJjWos3UbnkjP5nQqd6Y1J6SZ4hJZfl5ERg4AgGCIy6pzLaeSHTvth549e27fvt1iKb/IHj58uGvX2iqttR8QYR9E2MeIuSrC3jYm7y/aPevvC1+zJQxB83hLX83OMr79g8aJhFFBsOHRK5T6LOa+0sIQSY8ODV801GGOq3Sy6+nCn2oSjntutOR+wVgKUXl7qf7ZapgpRku0UtiUlqbnF6vumsvD4W6bibeLVTp7NqHmoIoXvY+PT05OzmuvvbZ8+fJevXrBMAwASE5Otn4aEBDQxMnkcvnZs2czMzPj4+Pz8/MZhnFycgoPD/fw8KjzWIvF8u233xJE9ZjIx48f03QdXwULzTxWkqGifECVcALTjDSb1l4DMBuVFdOGLNroDnP8qDKN7vINqkxrzUvvxwua5baw0Wdqx85zxM6dOwcNGrR//361Wt2nT5/k5OSLF2tYu25/YF6R4kejyJyN0ul3pmR0XPDySgZz/lMwKPbVgI3j0ggB4nShq4MDnvbmTFPKCEUXh4vxvwx5dzA1EfpixPrVsi82pazoKx3sxvZ8VjLE7oB7rIe5ocCeGKMl6cLCt8kc3leWfC2TIABaoiz5SioJfxFTrrU+VQx8WloaAODcuXPnztl4eGWaIw7+ypUreXl5r7/+Ok3Tu3bt2rlzJ5fLnTZt2rhx42o/kKbpkpKSioeMCnQ6XZ2KJZeQK/8tO+43gaH0Lu9xLYSaLEgDqt8xFwupyjE92MLtnoF38DDtPII4SHAvNwAABCAXtltTztSOnecCs9mcm5ubkJBw/vz59PR0FxeXYcOGiUSittarvsDcEEQ6ltLesuR+Kfbr0413S01g30tnvrZYqvYXHgnUYVDOd6jU+0Bw+vS4lHf+Fd4xOc10jhp20B33vlD820inidG5+z70WW9TOOo0u5VP5/8n3Vj4VpnDwmIVA4FtUodebFZba/SCUMXAk2SNoSPNwvbt2z/44IP169cDAFauXHnw4MEpU6aIxeK3335bqVS+9dZbtRzLYrE2bNjwbP/vv/9+5syZ2udlo5DRwqCKD8n8nQWpe64HHn9TYsA9NwIADDfHG+/p1Uc/Z0xmxkzK5k+C0PK79VRD4mPtg5FOz0fGDzt2GgeO4zNmzLh161ZERERb69JglAbzt9cTOdCYedxfIMMPmzxPEzAezZvlpixRJWCvDC4anDJInyuA82ACghQrAam3yN8EMGqeTgR6+29dlfDeYungf1UX75fd7Czq2crK3ym7JsUcvbntpbpovNLyyT9l1vckzaiM9JhflNYmCoEjo6XclowogwAAEAQYBm73YWtEyjxKe+fZfgjGWcG/Q1g7KihcxcC3dPDr5s2bjx07NnbsWADA3r17//rrr7CwMABA375958+fX7uBbwrWTHaobAJZuF8x9kom48QQ1wAAtPYWhCU6zL1BFushHFd9H02bnq4Q8BD+rdKrdgNv58UGgqCoqKgVK1asWLHCw8Oj4iLwXITCC0z/+4g3RUV5UDTCgnRyzEADZKrmiAP48io241Dayvn83oniCTqHKUO5nDJSvTR+tgBCPzL+exYIcx6/TTKWjckfwgD5o/hUyxl43RU17sXB3ap4jZWR6oPZOx0w2af+38BQkxK5mJMzDLcfApJkdfTjdg1ptJxAByyyn4hmAACgxEgv+1/pZ33LF3I4GNSi1v2umXhfWbJVKkEhaLGy5GuZpAer/T7EQ7xwyJzJ8o2q3Ekqj1PqixDWvooUVC8XS5Lk0aNHL1y4kJ6eDgDw9vYePHjwtGnTmuXXjiBIUFAQAIBhGIvF4uXlZe0PDAwsKipquvya4KDWVLUQ7rWZvDn+jdLPmIA8AGgiYznuuR7C+JgrHwAgWzAN5j1N3ufMUqzwtae2sPPiM2nSJIPBUM3RtVm25FoanB9mxN3VVB8V3DHEshVjyo4Uz+yqcJBatjP8lwUICrlv6poyme0yEQAgQiXfhvz44NF4Ha/TwqBjAACaobekrhomH9td9FILaaj+tTD7/URYhPqf64J7PLXxv+T/0FM4Yv9N37+lF16VDWu0/LLTf2nP/yN47WXA55b+dMZw/b5sYSNDmhEYBMvKi6EV6ikMhkIcW6M22gOCWKQs+Uoqsa7Mfy6VLFGqv3eUhuDtqzJbBZjzW1TRYdqYgohfK++i9GThAZb/EetKRPuhyp2jRqPp0qXLhx9+6ODgMGHChIkTJ0ql0o8//rhLly4ajabpk02aNGnOnDnx8fEQBE2dOvWLL74AAFAUFRkZ2aJhcgIcnhXGAwDAvHBS1dsZucQQ+WTRDwDGEOnYimGIWFit2B8H4bacVnbstBMSEhKysrIKqtLWStULSnMDEDm+9PHeWDSK8jWk6BXBv13oXShkfk1ycV4nHlfUCREPJHO/to4v0tzwMdz39I3iIXwewhegwmmKeb8V/ATVWlmu0ZSeKsr5KNnvjy7OSzyTht4lMozW/kxj6kPtvb7i0Qaj++nCn2oK1asTUqnWnL7kEvmBaPwQ0eiBLpEfEhm5xtjHzXcGrQEOQbscn+67v8RmfStzwFvXUD5WWe4W1LuqDYRjHhssmZ8ApnzF15L7JSIaAPPbXexJlSf4JUuWiESimzdvVs5C//nnnw8ZMmTZsmV79uxp4mRbtmxZsmRJr169RCKRTCaLjY3ds2ePxWIJCAg4depUE4XXAgyB97qW50yApasZ9hDaorfkRLICjtZ+w5Vtyvi94Od3vT5qOd3s2Glznt9CkTAvGAAIQkUAkz0iRv2tNCuw3AB+FgA0wg23jsE81poevIzKpwF2h9L0ZSWiwRKGBMZUAMDBGEzOEwo4wgvFp0fKJ9Q+14V00ytuLG69S5eWnirK/iDJ97dOnI58Tkc+ACBp+D3/c10wL3Z07r7xztPYCAeBDN3EL50uPDZVMbcRp0+kZrICfKyBPwAACEW4PcPNCWmc8BpL6bRDAp+pod6Z1dou9FeyCZWJ6upc33kRyRCycA9ZuA91fpsxZ5BFR9hhV1pUw8ZRxcBfunTp0KFD1WrMsNnsjRs3Tp8+vemTIQiyffv2zZs3X79+vbCwsLS01MHBwd/fv0uXLnUf3DTuFBBdnXEIAH5Pn3Pxs1+lv4FFr8C8ztWGqXZHi18fhjiIrU057lxEPB+PMnbs/D/kzwIvUje6D++c2XR/Q/rqVL1TtN+kUgubpAWf3Brzw2gAAIAwOeqygMhaSzi+4UiqDkISMnuX9fC7JQN4Bk2wO11krl6f4ll23tW5CZCOsnqtG6tPFuV8WG7drT2yOQqaoJNG3tf/ZDHj5pcdXi3S0wCAsc5vrExY2E86WMGuO1q4GhCbxZjMlXsYkxkWNUPydkcu8vmAOiIpqNK/AGVr7QHhIeJBTdehnYN5bjI/GoHIJhKZq1DXhRBeWy3WtqKKgc/KyvL39392UEBAQEZGRnNNyeVyBw4c2FzS6smiP0tPjpM68RDd9VJ8x6vkyj0s2dRnh1E6velxKu/l8pUWFsz+1P/r1tXUjh079cWd/lvK+QuFLAhC/uwzlGQQHCYgwBgApzuz9U7Bum7OOAAAc37H+KA3mrbEyXfnJw6jKg7/TKlx4SOz/W1cCpqI6kCucLC0wrpbcZjsnLst89fvicLhixZllppJptRML79sKTR/NCspq6NQGNlPxG6ILxvLz4vIzjPFJ7M7+gEAyEKl7r/bTqsWNF1/GAJ93OpwcyMyPoZgNsSpEgXAmNIYsoTT+UFL70aTRT9QZbbzq2KKD2Bui9cChTn+iHSCOXEqQxSxfPe19HSNo7qTnU1nuufCn7Z2ZoXyhFQsVaZBJQa5KEPN9uXobls/gmAWLOhtfc/y9TQnZ1YYeCtGymDfjLdjpx0S7N1XpxacUw0ZLDyto3gYRAIaSjYFerLSrxsGH/lDfXaizImHAJiFe0aSxdFIJeveongfCU0efT9nRbJbZLn9o8rIlIiYskHalPEx7mw1AACy4Azoggj+dRZQRkNKZ4WMhYobNAvM5TgumqHcfgh1kUMYSqRmSd6MwD1cm/98bIF7rLNkb2D57gFQhR2hTQ9fwz3WtYKvGVV2mSo5A+FVs5UwBEMUoLJJoB4GPkVNJpWUR4YnqEithTqXarI2fSRogEN14/gsmNvHptjuuPdWALdTn//q53Do0KFnc1zodLrW0qelmNeJZ3qwiKD0MOKh6KthiAJKeYLWXGMshQyRz+mSYP0Psfy81Ed/r3ygjtSsTVryZfBeqJ25R9qx00SWLFlSy6fffPNNq2nSaE6lQJdTF8+UHyghJQigCixOPqz0VJNXosFPDYf8N03GefJAjEiGIJIhAIBN1zQ388vdqQp0FIpAvyWX+76N9OXMC+dVln+/kDBYyqMJdBYmtpAoNZXnzQx1xISsGsPbEBHqd7pzhY2nysjk0fcFL4vFa12XG8u3zNPV+OVH2BuBCgAAAB4BfJdGXGLYIf6K7WvMSRkMSeK+Hoig9Qq0IA4jyML9ZOFB1LncgeBZz+WWA3WcTJWcQR0nIZKhFZ1E2hJAlsKCXvWRcKeAuJpTvsGRVkoRFH0+rfybEC7H62PgIVTM7nQPQlqwoG0TqXIOISEhR44csTkuJKTx4ZXtgbhiSwfFajhnNSvw50cDrmbevDGSbcE81poTJqDyGRX3X7iPB6VSVz6Qjwr9ecEEbWbB9uIHdl4o2Ozyr7RarY6Kiurfv39oaGhmZuaFCxdWrVrVtrrVEwcOLHSZiMPHH9O9e6M/itDSc+oRgyT/fKH/41UZK1tD+T9zmZ4dzrNWpgAA7Hugl3HgMU+aLvwqS5UMAHti9LonBl5poH5JMvEwCACAQOC9rvwutfpkISLU91SnlJH3cz5M0l0vFfR3UGz0BQA4s6wWHXAsBAAl3cUvN/GPALFwdqiNrdVWAPPaZH40CpGNg1AHhtJZciJZ/j+2TqgYIh4M8zuT+btQlwVWE0trbzDGBMxjHVS/hZDJQdzJQeVLs3ti9CoTtbyXsKFqtGfrDqoZ+IcPH7aVHi3NV7e0s8P692B5UiX7ATe4kHBk4NuU+g/GnF05GyXMZim+/bTasfM9l7WqrnbstAqRkZHWN+PGjduxY8eCBeV7twcPHjx9+nTb6dUA2AggKWhz/opIxfx4Y4gvK9kRVR4unvePWogjpuu5RNQwBzm3ynO2Mw9x5pUbchkHduEjNUV7QwDsGiKpaI4+oVz/irCeTnZWUAnme6Zzyuj7glcdFOt9G35+7R2YE4A4jLHkbMG9tpA5nyPiwTC/xT2mK8A7fGt+8IolJxL33AQATaS+A1CpPbtwZZqUQek5goNCjurNABZYste7frijs+YsVfaPOeVtiOVJZK4EzNMIyIpUtZXJNmW0nq527LQuly9frlwMYvjw4ZcvX25DfeqP3gLy9BTMC08k+6WZAncULnJjZcfDMwKkWE9X/NR4WTXr3vqgEizwvx4V1p0BIFtDWV8mC+BiUEWzQE+1raqNA3NfSalOUSVnyeKjmPsnLTfRlhtaE1kl+RLM8UPkU8jCA4wpjSzYRxMFuM/2Sg4Bdp7Zg39R4eEQRqQwaAkk6MnrWOruMJDR3IbZ3gDm0NqbAKrjrvyL1DUbAraJUEntw+zYeR5xd3c/c+bMvHnzrM0zZ87Up8Bje+BMivFeAQEAyMIWHfUZ00vAi8xdeVsPAUB4iOrlGvzsajKl+pUq++/ZkfPEJsz8LgChTVE4rtjy3p/lm4AUwxgtzJtnVNYmDkOnJshaNCNsSwChYkyxzJw8A3NfA2EtmFDhVJJhVhivWpQB5rGeVJ4wp75DGx7B/F6IqJFlzZ35MOtFNIYv4jnZAoPBwoSlvwZOZAX9TmvHyHihZJmR5bPLlPQmy+e7yj9zSqsrXLvd9esqt6Jjnac+F5k77dhpBJGRkePHjz937lxAQEBiYuK5c+d++eWXtlaqXizqxu/rbnWgEf2c9FYgfvOqpv8XA0QAAIWwbgM/MZD7bOIahjZSJb9h7qtBpfR2DFHQl/+Nnv1hExUOdcT+nlpejKRQT439VVXRfH5BnWYzFiXm8nbrTw0hAtx9NZG1BmIgls/WRssZ5cupe9BzyP8XA/+SGxtRXYd4wUTa20R2MEPPgCDMnLkcIAJaHwsLelaUfEb4PFJdRpVqKvJDAQAGSIfWINiOneeekSNHxsbG/vjjj7m5uWFhYVu2bLGZD6Md4ilCPUXlF7FJj+btzpsh48Jj/Ot7sX7WBQ8AgDpOIQv3Q4gQkY2v6CRS3+EqFoqkHRqhpCXfjIgxmPPi7odCaIsuztcO6jSHLNyHSAZDLM9GC6EN8ZTyhM2PYPFARNin0ZLblroNPEVRMAy3UK7mVqOXKy7J+B1QJsaUAaGKklJ/kUyF6e7BvK6WgijUed7ToRDE9vc2J6Rxe3WqLOFu2fVOwh4I9NynBLBj51nS0tIePnxIUdSqVatu3br1vBj4ykg4CI5A195swAPxoYd6OQ8Z1qFagAyMe242J8/iOAwDMBcAQOtjqNL/JSuuBdEM1sBqpoYYbcqo+5wQvs+JcJhX5erxj+qChQlrkLTmIlqn789huzxJcEIBsF+je0PA47an67yFZg49NFB0+dKpwcIcidPzn6y4jPOjZViJ9T3utx+CWYw5w9qEcHfQ0As1ZbDk78AUywBc5e7QkvMF3vI5c1oO2zeVSUlJERERRqPx0qVLHh4eLi4uN27caGXNmpcSE7298GPGnIe6f4o55fysP0rBLFg0iKHKcM8N1fwyWIEdTAlp1SRcUp7Znv7ZA1tlgO3Yea45cuTI3Llzu3bteufOHRRFFy9evG3btrZWqsFI2ZCsgS51OVoqX2fDtQ0WdIeFvSx52wEAADBExnLM49NVV+jkJ3lR6onxgTZ1fIzHziBWADclIoau5EZXYM79S3XKzJQRtLkWCS0EA8CMImU+RQEAKAA+Uamvm82tnf+9LhgG6C10GcFYXzQA2ifv9SSD5W80xvY2PRprejTWnDjN9Hii6dFY06MI4/3OlMaGC0XtwILuiDQCAAhzXVzxgjA5zA1GKy3kPHfYfoKfO3euXC7HMGz9+vXLli0jCOKDDz64cqU9JtOvhUdKy7sXy/1ZNGa6zBxwTjXApHzYEYd9+VsQshQwBIRKEYeR1Q7kdA7WXa5+Q9NV2Pto3r5UQ8K3IUdbQ3s7dlqLXbt2RUdH9+vXb9euXR4eHsePH58+ffr777/f1no1jCnBnB4uzWCkaN1d2vAQ5oaQuV8CmMWYUhkiFzDGgfyfYCoCALe6RQAAADA+0KaMjXHfGige5Sge5Zi1JCElIsb3VCfrc3x07r7xbiM7Sf48V/QgwnlK09VuEFP5PASAN4uU+xxluzSaPIqKcpSi7enxHQCAI9D73Z5GmZ9MNLzbhV8RFsGYF5nKfmUH/w6xnv5HyOKfyILvEVFjapPiHutND/qijlMhlgcAAFB6S/bGdlgBtkHYNvB37txJTk6mKOru3bvnz583Go0bN25sZc2aTpAM+3GU1LrAcy3PvPLvsp7d1knS+pTp1/oLFyXyIwI0f7BD/nz2QNzLzWF29dJSZ4p+CRV0TtLF/5S7f7LCHmpp58Xh8ePHwcHBFc2goCCVStUskrVabXR0dFxcXGFhIUmSLi4u4eHhkyZNEgobnFGkTkIc8RDHZpBDlf1D5n+LOIyB2H5kwUGGVCGi/rTm+lzxiXyyZ00GntZeIXK3VmpShvsa7wM8VI6ThcNQpzke3wRW2Pg46n4hkf+e7JPu4rK1SUv6OAyU4a3tajeJz6MBGFdY5INih+RSTjuz7nUCsdxQp5mW7I247+7yLkpvyV7P8tvfuPBvCHdFnecRWetYfvtAO64A2yBs/yGkUqk1oVVoaCiPx8vLy+PxeDZHtmcgABQCxF2IuAuRzk44BICbgxNLsVgiOJm+ZQugtBAmrWdNgvPFp/Skppu491CniL9UZ3WkpqWVt2On1ejZs+f27dstlvLi1ocPH+7atRmua1evXlUoFLt376ZpOigoKDQ0FIbhvXv3enp63rp1q+nyG8eeWP17f6qtr/9lmn5NMlY0f0k0AgAwl3cBIkBlE9kdz1GAJgTDi1wOaYH7v7rRWWRYRdg6SVcRS5vS6dK/GKIAIjUQqSEyilhuBOZgoksv02VXAAAAAu6f+xO5pqJ92T/lHXhDMQ+FUAkmfU024nj+odb/O1AAxBCEIwyraEpF0XUf0P7AFMso7VVaW77aaslLhaPyAAAgAElEQVT7GhH1q2eeWtsCXd+ndXdozTVrBVjMfXUzadpm2H6CX7BgwdChQ2ma/vLLLxMSEiIiIiZOnNjKmjUvFqo8yg11nkcWHOQ5P5Kb7kCYQ03jGQtJ6w1WR3qSJk/l/zjcaZySKGYAI8Ik32V++aHP+tbS3Y6dlmXnzp2DBg3av3+/Wq3u06dPcnLyxYsXmy520aJFGzduXLRoUbX+I0eOLFy4sHYbTxDEjh07SLL6nvfjx49JktyyZYu16e3t/frrrwMAjEbjzp07KYqqs7/XgLEKPkIQxIULFyjEiwIEkZUjl8t79+7t74Bax/tIO3dRzf7p3syHLl+VYd7sh3E7FAciM34uTc6GGRoAwELRr4Yr+rixKsuf2VNoMRNu/a4AAIxMycMhN1nLDwR0h3b81sHd8+eJ4ydmvvMY82HvkO3Q5unP/fbnOfCnt7d3xISxqxIXPlDdu3jgcn30b5Z+zw4d7g98LY+kjooEH124ODYoeMilPwV6XUvP25R+eenjPd/+gDLk036Ywzh9lH1z9rF70wWsssldD10pWjvKBzRGfmnctT/WMAwj4/MV2sk0xBfJOpHKnywWyy9/5OeVOjX7eYFWwbaBX758ec+ePc1m89ChQ9PS0lauXDlz5szWUagZiVdaIn5R0pXC13125wMAXhF+sGvm20e5H03VbweAsbnFYnqcqvntT6fVCwEA32d/jcOssc5vmGijntR1EnX/LPnjFH2CLy+wtU7Fjp0WxNfXNyEh4fz58+np6S4uLsOGDWsWsampqWPGjHm2PyIios4NfoZhdDqd0Wis1m8tfKVWl/vWuLi4VIzXarVms7mif9c9XZAU6+lYvT/UEQt1xAwG+r46LoPH45Baiea+nyx4uM8AAIDBQGi12ptK50A57CW44cK4Tp882Jw0k8LnGe+xXlKeFpuLAADBwcF93KZXm/enG4Pm9fuV1t6ABb1wH/btyXfmdvlfzJX+xSq9o6Mp861HpMoi3q8oTM+QXPJUa9RWfXAYn+Ay/UTBIUgnNpvMdZ5Xs/Q/cPdkUfQeRyltNCpi7pcZTX8MeLX/sWgXk6lF521KfyfV/7TP9APxOLMl0o13K8Al81pyqBrhNFK+pbCX598300KUpSxnPs3GSxFuECDLQGEUF35JrcZb6LxaHKYGzp49O2bMmJEjR6ampkZHR9c0rM05ffo0i8Wqc9j9QsJ3d15FM33uhpSUEsNtH5oosjme0ukzpy2jSarIXDA7Zuyd0muVP41MXvHRo3lN1NyOncpwOByDwdAmU3t5eX355ZeVe9hsdtPFjhkzZvLkyQUFBZU7lUrlvHnzxowZ0ziZ9fy9Mwzzyd+l0Y/0tY/ZeLVsX6zO5keULtZwJ4AmyyjNdcO9EIYyjDpeHFdM1C7QGD9Kc7fjUa2OYRhjwiT9Td8Hvv/dOpx9fWpMcsR9ykj9kv/D2w8mrUtcWvn1aeLS2TERd0tv1Oe8moX7ZrORpiv33DGZyVabvlmhtLcNt70M98IZyth4ITRjTJhK5H7DMAxtSqd0DxiGIZW/Gh+8wtAt8odpnd+77Sf4I0eOfPzxx++8886uXbusYTOFhYXPnVdtZXhV81WZU4e6qWCI48GYMyHMhmcOzOOijg6WzJzvyG0AYqLz9kXn7QMAEDTBABoGiJYsu1X6Xw/xK610AnbstBhZWVmHDx+OjY2NioqqKDHXdKKiombNmqVQKLy9vaVSKQRBJSUlaWlpAwcOPHq0xUNRSAbQTUg+CfPCEMlgMucLSnMF99wIYA4A+jqPYvsfpu/6qfO++1EyYGzpn7j/Mf2JjuYxDwThgg7HOsFseIjjmC4i25vECrZ749VtIJ3w6uEGXVntLUquvsD8bqh8Oix4CTSh4OfRRwaL8ZPJ2jGo4xSI5QWxAKBNRNanLJ9dDQ6pb0/YNvAvRthMZaCqv3bZbMURITkV9sRMmYDfzeYhrIAOpsdpw/qMT9UlVHRqyNJEfVx3UR8AgA/XvkRv50UAhuFr165NmzatX79+J0+edHV1bRaxcrn87NmzmZmZ8fHx+fn5DMM4OTmFh4e3TqL72CICADA1uLYxg7zZQrxG73HMfZUpphvEC0McxgAA2CjEQiDGYqHKdKiDCMC2PJRRMeY0d2bR1wXaH1WskGzOKx/I1FuvdO0s50I4DADgIXwep/VKtv8/AfNY10QJRgujIt1R1zctWRtwn28BAJa87TC/KyxsajHftsW2gW+5sJm2AoKr7LTze4uSNGUqkTvHnFHT7Rm/Xw9Kq+suCusueqlyP8lY0LqK09ix83zB4/F+/fXX1atX9+jR49dff21GyZ6enp6ejc8h2iB23NUllZTHAuRrKb3FXFHZZZAXu6IMfAW1x81DmBz3jYLYPtbmkcF83aGfs67cQQQ82kxIJo0QDLURb415fWYp/tGZzJrl9FeysmS3o0MPFqupJ2anVUAVy0yxPWjdXQh3JQui2KHPR03FWrBt4K1hM2vWrLE2mytspg0RsWAx++kdt/pEYTeNsfhtd4X5QU2HsAJt5522W3c7LyQQBG3cuDEkJGTo0KEV3kDPF33dWT7i8mvavXyLQoAM61Bu1MPkNn62vyUbpRy4j1uNBhiRPC1CoTtykjaZ3fdFwmyWpaC4KHI34iDm9ng21yzM9j+UqnmcCZylCHTbTNgN/PMChPAx95VE5icwywN1mlWe8aaxEBnGpOH3pG+4uKxsTAmDZsG2gW+hsJm2hWYASQMUBgAA1IklSdXmYO7h2tMNlVNMFH6X8fka/6+aX0U7dtqCyrXjJk+e7Ovru2/fvjbUp9GEy7HwJ4Z8622tgo8M96ltXzau2OLCR/q4saJ1+qFcjuTJqjvJMId1+ul83tPkbjStv3LHLWojzGYBADBnR8nUUbp/blYYeErzryVzrfW9jmb0FHkW/ZkFoIx88n8OEwd4VY8VrD8MWWK8GwAYG1lyYUF3dseWvTLrSN2pgqPT3N5q0VnahF33dBfSy6MGlAaaZJjruQQEDdoo+16OX5H3aFK2ZiLDmDTinmyWQn28EADQVjbetoF/NmxGJBK1smZNJ7OM/OqW1rrzbqaAxkzPPV8iwCEAgCML6dRfkg7BtCm9FglkgZImCNyjypakDJdzEJ6ZNrGa4NNhx077YfTo0ZWb3bp169bNtmNKO2fZ5dK7BYT1fYGOyjdQ/Y8WWZtvdOTNC68xW1cJTc8qUh2QSyUwTDLMEpWaAWCW4OlmOW0wAQSGOU9/8ohUQqlKK5owJ5AxpeHeX2dinutLyj4SCyU4BmhTh8TJh0GHAp1+Cr+RucIgVAwLekO4AnWcVKmbITJWIuJBjZNZf77L3PJY99CZpXjNcURLz9XKTO3IfVJrGJxMMpaZ6ZmhPAAARnyHsbTWOkOVyTSm7svasdJvc51XfiLblDzqvtMiT8f5brJZrskj7tMmWrHBtyXOonZsG/iK+3ehUKjX60+cOAEAmDNnTuvp1RxIOcjQJwt0WoK5mmMe4cOxutOj3mxOclkKJGdIJaDNALa9hmZKSjfeeei4tEpiWghA9iw3dl4AMAzbuXPnW2+9hWE2lq8rEts9R3zaR1RqKk/KNuNMSYAU/aR3eU5cOa+29KULhAKaAW8WKffKpJ+VlhEM2CGTVPbagflcmIUTGTm4V3mqWtPDxIr3AAAIESDyqZaifXL3jesdxG4oAgAgi3+GeV0+celRijR+lZ4hNbTuNqCvA3NGhaM4YylkTCl02T9A0dQS9bWQYUxJ0MeFC7qeKDjUXzoEhZ/v8uJm2kQyJA8pv28Ts2CxY/m34nougSEgxNH6Q7CR3pQBzNHcvSbaeLbol3HOb9QyC5FtSh5+T77Qw3G+GwAAleF+Zzsnj7gPAGh9G2/7H2a16AAAhmEKCgpiY2NHjx793Bl4Pg5VLNApjfTXt6CJgRwAwAcqdT8t7LggOe9aIIS7FRjS39FJ98ulkmfcYtnBvurDvwKGAc8kai405zmxmsfZ2I6dNiElJUUqlVrftLUuzYMAhwR4uddsBzESLMPchdWdaH9LNiY+KQp3p4DgYbDSWH5P4O+CjSwsCsVY3ztK8Gd+8pLpY4u2RIknjcCcHY1xido//nOJ/KDiU6rsHzJ/N4BYSOpbckRAAAAYC21IBDDKK4oSu69q9ElBqBhzfZ8qOQ1YCpbvXgAAoI3G2O4QJsGaILY+7EzfEsDt+H6H1Qvjph7I+Xaex+IWna6lOZTznYooWu67CWp4/ZhbpVfMtHm572efJi3t6/CaDHeqaWTKuBjJ685W624FleF+Zzo/6nKD11UojmjVogO2Dfz58+crN/fs2RMVFdUq+rQgJM0ojbSMAy8UCWYblV+UkRozaWF5bi96OM5x+LPWHQCAyiQQi0Vk51dbpQcAfJuxea7H+54cn1bR3Y6d5mfr1q21fPrNN9+0miYtgRMPcbRVPZaHQaInoXE4DHFQYG3SgImFKRmMFNGkhmZkSHUbwHu5KyIRav/4T1tSinu5uWz+EJVJKj5FJENgfldY0JsuvcgOuQggjEiZBwt7UarTqMs7TTkRhtLQxkTanAlM6aZHoyFMShseM5QWQkRU6cWmpF6vnX+Vf6otqtX+XwAApru9G5X59TjnN6R4c9TzaQtS9YlJung+KrxVeqXns/lLarX4BE2cyD88z2OxAyYbJBv5c96hd70+qmmw0/ueeetTHSY7s/2eLvIXfJ2Je7EF/WtMjt5C1KvqzrRp0+7fv9/SqrQ0JgpcyjABALxQ9LszZmCh59y2/Ek7j0SK3hTUuD3GCQswxSU/2z/SaSLNPJcVGuzYscJ+gtFo3LZtW2xsLE3T6enpu3fvlslkba1dk4EAbOuq/ZoXe35nvvUVJsd6K1jzO/PndOLFu1sEbPi0s+NQDmdmsVJpq/4KO9jPcelsl41LpXNfr2zdrfPhnpGU6lcIdyUL99Ha25TmOmNKxRTLIFTapPOAuYwhAeF1h2E+MGcigpeBOQeiKQgVM5ShKZJrgQZ0dP6+AY7DhKgYANBD3MeJ7bozY3MLTdfSMIA5mrd3gsv0NxXzj+cdMtOmagOGd2BPDKy+6V7B+aJffbmB/ryOAICh8ogMY2q8NoaxMEXfZdMGqtpg6TQXt01+ycPvmRLKMyPlrU3V/av2/a0zIm7tPY6652MYJjo62rqU9/wiZsFdnDAtwQAAivfkGFakQTAUMC+l5LRrN35eLQeKxgxiqOr/QgCAjXtAO3aeKyIjI61vxo0bt2PHjgULFlibBw8ePH26wdEl7Y23wnkidn3Lhn5SUgoA2CGVYBD0nkjAADC3WHXCSdagEumwoDsifBkgQkvu1xDujEjH0qUXUacml5aGUMxrM5G+BGL7MGSxJX87YHsgor6U8lfMrcbnyCZyIPtbCAJTXJ9uy77r+fGaxEVx2nshgi4tNGnLcbXkMgzBPSWvQADy4wX9UXRqjPPkygMUghrT1aktqr9U59b6lYdNYRD+uuuMn7MOvvHRdP3V0pIj+f6XusLcKodLJjgBAJJH3ff7vXNJdIHmksr3986oQxvEV9s28AKBoOI9RVEmk+nrr79uLZVaBBQGPVxZOoJWHc7P/CjZMkGWrDK5JZo9tyD5KxK+E5QuFwvFNlfpnWt8lEnVJ4owSesXcrZjp3m5fPnyzp07K5rDhw9fvPj53m0FAHiKGvC0NI7H7cbCreac0lETvy3u8rYz0vAS6ZjHOtODVxDJUNqURKn/wL02g+ZIm4GI+sGcQIjlQSmPA1QEKA2jT8DclkNoiyz56kjN9ZK/BagwMnl55X4URvdm7dja8UBLTNpymGjjyYKjC72WW7feX3eduTZpSR+HgfXcbvg57+BA6fDKg7vye5csydHdUTsv8SzakZ044I7/hXDaUIZKJdCTjL+SCU60iU7od5vtz/U726X1n92t2J41JiamclMikTg4tPbmQbMjwCHwc1HWt5mmCbK318g2fKnK6S4U3PN0yolmdGWiIeKGCkw2PC40581we7cltLVjp9Vwd3c/c+bMvHnzrM0zZ860TjbZFmXzDU24HB/WobaIpm4uuIQNAwB6scu93CkdlTLmPqUm5f+omZOdIF7D8pBDuAvqMp82xCGiwbT+LiIe2Gj9q4F7fmaKG4SIB5Cq05jzPKr0Miqf3lzCq4HCaJiwK8VUX7kM5IU6VnqeMcUNZCwlzx4OoQJ26D91bGu3ImcKj3cUdPLm+iXoHmpJTXfxywOlw0/kH57vuazOY/WU7m7ZdZIhfyv8ydqTxRq/fEumF5vK2JEdPnQke6BW+Xlm5tzT/O45tM6I+4wnp45LoOm+bJZ0mgvuweaGCXL5oNBMdG+LbP9VDPySJUtqGfq8O930zDIatmZIp7vsX+c0CUGODzGFc/BTr/c6lFs4ISJFnaNw4Nq+3TElpFKqMt7L1Rem+jkMTjMktbziduy0LJGRkePHjz937lxAQEBiYuK5c+cqp755TtGamTJzHV4yQ7yrmH9aT6VOiOUE8z22BWQvTUoZc9/nVGeE3zAbj7m8Z4ztSZf9ww651GClawZie6PyaQyRizm/RarP495fAqilHgrZMHeR98o6h8G8LgypxD3WVu60ZEcCmNUS1n3rbe07XfisZ5wfa6eIKLikPDPd7d0HmrsHs7810yYIwJ7cDueLTybrH/vxgmo/nIfwo8JOVDQZirk+8q7viAPsTipPpy4m1akHXIN0vl70GKAuWrZvsu6f7iXn/lnTI3SxSBDB4wr6StJJcnaR6kOxsDHn3GSqLEq/2E43ngoWjABzhnG7WLJaInqrs+OtMqPZhAETh6fQSpAat+sYs0V7/p9n+zkIt6OgU0uqbMdOi2M2mwUCQWxsbEhISFFRUVhY2MOHD0eOHNnWerU2tJ5KGR/L9uN6bA8EMOT+TQAnRJAacZ/S2XDBqQ2YjXt/iSk+gtjNHPSMKpZRZf8xlkKYG4yIBjSv8EaAua+kNdcYygCxvKwvwDBU6V+Ye903B43g2GNDmbnB9QGLzQW+vKBr6v8dzdtLAZqL8qPz915SnvXlBRYThRXDKOUvhhsSmy9L7tOkpUS2iX1Fk1m4SMsyJLt+Plv05dnCd/2DvgPILiJLxFDzJW/O5F+6clAu3VamPaU3ZJDk7CLV20L+cG71OgitQ5V7wBfb6QYK4ZMHQnRz4pNH3Hc70+ni3ZKZXxXzcghinZPPLw4Qq0YDzw72Kc7MpY2mynmsrKgtqqvq/42UT2hh3e3YaSlwHJ8xY8atW7c2bNjQ1rq0GRdUes9Jj3khAvdvAgAEGAB+NxhHfu2f/V5C6rgY39Od4Xr76wEAEPEg0AI55iBEgLmvIjI+4ITfaHbhjQBCxZhimSVjBSv4N2sPkbkCVbwPYTXGiLc+HQWdOgo6acjSVYmLVvpuFqLilYkLJ7hM96oa4QwLekGoA+a2HOL4VXQyZIklbQki6lfRw/Jgi4ZJNbsgY+9+N9I3KVzWvHfykmHYW+rjfyjWJHI7RwMYp9QaLwQ5IJe+WaSyMMwSkWBSY5MYNh3b39rLly+PGzeuojl8+PDLl5/7ujrJJeQmBvY9Ga6/XfbXkNsyNqJ4YGSb6Lv+PpmCrFoOhDAM9/U0xdsIluMivAtFpwj6uazMYccOAACCoKioqBUrVqSkpBAEQT2hrfVqDJHXNdPPlFhff2eZ9z/QVzSPPa4toiymxKhM1WM9hFbrvqGw5Jhez8AQv4fInG58NhSqlbHkmpUHcgEAqHwqO/RviOXVtvpUgDrNYUglpT4HAKDK/qaNSZhTe8xa/3Peof4Og51YrhyEO9ppUnTuXgZUWQyg9XcZUk3kbCTztj19Za5gaAOluWYdU0QU7Mja7Hm0I3egpGzBiHGlp4p1jwrZYRlzk5yXHuCEb4I4fOODBNzb7UliNIYBgNVwV81mxPYuzgvpdOMtRqcEcwWB3Et7fQbMSXWbkAhweNVvHaLkQZfVjxydaJu5bqxwwoOIlExut9Bq/SyYvT5gG15Dpls7dp4LJk2aZDAY9u/fX7mTYRq8HNrmTA7i5j1ZUT/wQO8lRgd4lP82/WsNUvrIT/b1L4HUpCSaYc4glsGL0+cfDCktzs/bkOZ3pm0CnCogckzJw+7RBE1kmFzX+cCcwDZUpjoQgnlFEqnvc0T9LZmf4J6RNaX9bgQUDX58pDdayr+HBgtzJE7PxyAAAAxDEwI4kvotq2QYU+N1MZsCyuNE+kkH/6u6eLfserdKdcARh5EwvzNDqhHHqahsIgCA1t42J70BYXLMea51zLG8A0m6+HMlF49u7T59ASk7EPHF6PWZPy4XRpxB5QypCjXcvVx28k/5R/MySHJWkWqhUNCTzZpVpAIARPBqDLJvUWwb+BfS6UaAQ68HcgEAM8YoWAJ+7seJ8gUebGdUyvadRFwU1WzdAQDCUQMAafsuXoI93xkC7NhJSEio1qNUKttEkybiLUa9n8QjnUs1+UrQl2suBVsZCIClXZy+PgbgMY/DSMY90r9gVjyEQv5/dmUHttn6Knhi3aUzXR3nuCWPvs9YaMUmv7oPa0UQYV+YG2SKHwlhLohkSDNKJhlGaaDJJ/eZNABagqHKm4zBwkjqUe2LAUx07t4Jzm9ykHITCwFoimJuVNbXYYKulZ7NYNxzsznxDUvWWtRhBIDZROYKgPAxt0+sVWce6x7kGDM+8tk4pSBjoJAZeqJrxhSIY5khnHqb9ebvxsQ51OULqJPU+dNFSlf5rELleyLBOB4XALDPUTq7WMWCoGFtsQ1v28CPHDkyNjb2xx9/zM3NDQsL27Jli7+/fytr1hL8nWXu78GSIwgY5CAa1Ptxr5uHuweJOvgRhd/XfiCEIACp0Zl2e/qm8S7TFOznfpHDzv9PnJycYmJiVCqVtanX62fOnFlSYiMC6sWGm24GAJA4bLqjgTgwRDPmVGMbGvgK6+68zAsA4He6c/Lo+7mfJLc3G497bjQ97I+H/Nm8YlkItLTH04wsJxMN73bhy22lH66F+2U3Uw2JQlT8QHu3cr+O1P6tujDY8WkdRVjQHRH3ow3xlrxtEMuDIUsgTIbKxgMAaIaOzt032XW2O8frDfw615gAS9/2ju5Sdmq157QFZskkyZsfV8hBKGqdg6gvu/zuowOGHpJLi2wlRmwFagy0CAwMfPGcbhZeVN+e6cRBn2SidmNjuQQU7MuYUgBgGh3a4c8PTtEn2A28neeUtWvXbt68mcPhoCgqkUhSU1NXrFjR1kq1KgwA3/+SFbosPfBk50OM0WdlVv8LXXA9nTo+xgMEiUbIAAC/JRsHerL5eItvqRIME09YQlRM8rB7jm+5yd/zMDJMioUMFWO+pzolj7gHrUt1XduOqmBA7A6crsnNuDjfjPjyguZ72Ih37yZ6yZtb/T4J81hvevAymb8HwBhgKNx3j9UoXFadF6CizqKeAIAZzqM/SVjQTzrIi+MjHj/JkpvDkb9ZWYgcQeRVnwY9UdSzjerwVZm2FcpHarXa6OjouLi4wsJCkiRdXFzCw8MnTZokFLZGmKCQBWvMNAct/+vj7ux7KdqiV+SjYQ5DFEK4cy3Hmh4l6/66LnvPRnKJoY4RLaKuHTutwp49e86ePSuXy9evX3/ixInt27cTBNHWSjWVCYHc+j/tfRNf/MrcVO/T4bJuoiVAtOYn9q+k8YCfzGtvx5QJsSFxL2EurH2xel8J2lFWx358moW8TxDjK+253jUTpTQ98JkYnJrQM8xSlfqDOMZNbREOkhoY5q1iVSccD8Ux1AET9HPQXFK1KwMPAGif1h0AIERF3cUv13MwhLugzm+TJachCIa5YTC/KwBAT+nOFB7/wGeddQwX4UU4T47O3WutSocp6s6W04ZU+QGkpKRMnTrV+uZZmj7Z1atXFQrF7t27aZoOCgoKDQ2FYXjv3r2enp63bt1quvw6EbKgypGUuAcbyjbGEBaI7cOYUms/FnNzMdx5aDMvPQDATJt0pLY5dbVjp7UoLS0NCwsLCQlJTk4GAMydO3f37t1trVRT6eyE1ZJgvBoBXnzpLFfVp2mUjoIBWO8gjuBxKQ2ZtyFN+oYL5twA6yWC4T0a3V6Nztq8ZTYvUpbU7uJTDQkMH5RLvwyBsj/3Thp5f9XVXC8UXSoWAgDyNqRp/y7xPdW5/tJsskSlPqV/GlaQRZKjCoo0dLuuntXHndUKyyeY6yJAaRhzDuax2tpzsuBoD/HLbmzPijF9HQabaOPdsustrUzTqfIE7+npWe0NAICiKBiGoebw9V+0aNHGjRsXLVpUrf/IkSMLFy5sBRsvxGEN8fRLjHuw+bdL80gK5vjRpmRYWNuNHiLkY86O5sQ0drCNDbBH2tjLqvPLOnza7DrbsdPSBAUF/fTTT4sWLbJYLFlZWTRNFxcXt7VSrcoIHgd8FZC9LDFlzH3f3zojfGQMhadExHBC+B7bAhu0dydF4B/lshnFShIw3VmspUr1V1JJtwamKfVE0YNy6Yz+qu4r5BOmp4Wf6QI7gLwNaWVni/3OdkGlTfXqf18kmFWkMjPMJD4vl6TmFKtmC/jChtyFtD5fDGhwNvHGALNx728YUmmN5tdTur9VFwSoMFZzp/IoA6U/V/hLZT/89ontnYGkpKSPPvooOjr66tWrM2bMoCjq1KlTvXo1tfBwamrqmDFjnu2PiIh4//33myi8PswI5XoIn54y7s5m5ZpzKRLi+NFGG2Hu1eB0DTHejbdp4EOFXZIN1V2R7dh5Lti0adPYsWOHDBkye/bsbt26IQhi83f6ggMB9yc2vsPh0LQ3H3JC+JkfeMellZcWLTPTV3OIzDIKAABDoJcCF9eQHUuKwIccZZOKivdodDtlDhWJ7huEHEGcEOTyEEE/FM959xfcHaNNtO+vgTA7ntY/URkVNy4m3gtFD8ils4pUpTR9Qm+YLeBPaXgyFnPSTECV2fgA5tOBI58AACAASURBVLICfmg/uegbSuUKAjyE/3nQ98+m5QcAsOG2SU7XIGwb+Llz58rlcgzD1q9fv2zZMoIgPvjggytXrjRxsv79+y9fvnzr1q1OTk9THalUqhUrVvTt27eJwuvDCJ8q/xJOmMB1qWcxRQN+AFNmIxltNbi9Oun/vmnzIxTCXneZ0Txa2rHTugwZMkSpVGIYtnTp0uDgYLVa/frrr7e1Um0BBNy/CshanBAXdk02w9Xtq4Adf5eansRpaczM1VyTEC836k48pLNTjY+8GSRppBgxAscQRCMMvJFh3i4u8cPQzVLxusDHOzd+aClyhFlCSsWmymMdAEPkQewO7I7nG3yaAAAAvFB0s1Q8p0g1lMtuhHUHAAAIATAXdZ5XuY8sOgQYy/Nr3Z/FAWtYmvaHhCVKo/1CKmE/WfY+oNURDJgv5LeAdnVg28DfuXMnOTmZoqi7d++eP3/eaDRu3Lix6ZNFRUXNmjVLoVB4e3tLpVIIgkpKStLS0gYOHHj06NGmy6+TFDWJI5CHsHxnDmbDjJqS0bAK9xUZE+s8HPdwxaePrWXAfyWXekv6oy1WBMKOnWakluJSd+7ced6LSzUSCHhsDRSPkQsHOACoyrLw6BPKj3oK63SyAwDcNRPvK0u+kkn8MGxGsRIA8LZQUOdRFZgZZl6xyhtF1zmIyUzT4tmaG+smeQZZqPfmcjryPbYHAggA2mx88FJT6sHnktSqktK3RYJfdIZjOn0j0qninhtMD/rCXpshlru1h7EU0WX/sUMuNFqrF4BgHOPB8Lxi1feOUi4EHdDqjukMh+Rtky7FtimSSqWZmZl3794NDQ3l8Xipqak8XjMEg8rl8rNnz2ZmZsbHx+fn5zMM4+TkFB4eXs80eVlZWSRJVussLCysf8qts6lGCECLuj29kyr8JjM40CPbyVVIqhlKByF13GSRhcqy3y5ZcgoQqUQ4oj/L17Pyp7dKr7BhTv2dNu3YaUPYT0J11Wp1VFRU//79Q0NDMzMzL1y4sGrVqrbVrS2BgPDVxlfHTrJYFilLvpZJerJYAID9jtLpRSoZgkyody4zE8MM4LBnCfhkpilpxD3FYk/pqA28uN7Cn2anTtZlLUrw2B5oyf8W5oY0uuRMLknNLFZaV+ZHcjnWbGsNtfEQ7oo6zyWyPmX57bP2WLI+RZ2mQ+xm8/A/VRDtiDu97PBqcwlsBRAAPnMQrywpnV+seoXD/lVnOCSXOtWcRqVFsW3gFyxYMHToUJqmv/zyy4SEhIiIiIkTJzbXlJ6enhVOfAaDISAgIDs7u86jtFrt8OHDjUZjtX69Xl//vNkDPdkmqsrdAMuT7V9AaWgAs30ZYxLEr14TtjKWvMKC1Vu5vTqLJ4+05BYWfR4le3sqp0vHigHTFG8h9sd3O88JL3ZxqbbCCUEOyKX+TyKN5Qjyo1xW1hAHdREMzxHwiUxT0tC7zh96yWYrAACk+0qyeJXvqbMpY2Ly1tyQjPmOHXKx0UpuKi17SyiYyOMCALxQdB8ivDHlYeGHPk59G/agibkuNsb2pDXXYOFLtD6GKr3M7nS70VpVI8+UfVl5DobgTqIevLoevdoVVhs/qVC5q0x7ytmxraw7qMnAL1++vGfPnmazeejQoWlpaStXrpw5c2bTJ0tPTz98+HDlHoIgcnJy1q1bBwBYu3ZtDccBAIBAIIiLi3u2//fff6//zUeIY/XlNZdPOszrwGZxWBZOAG1Mgms18KXHzoknDFVHn5FMGckO9sUUTqo9xxSVDLwTy7Wemtix0364fPnyzp07K5rDhw9fvHhxG+rTPvGRoMKaa05WIILhakFxUgSW1lyN+lnUNH1Qq5uTR9M6ittFCABIslhucEa/zhwA5tPc8BDcdRXqNANid2joKVSwU/Z0iYJUWcwRD14K4hdOf8Q9EiJ4RdIAQTAb91hLZK5gh1wmMlZgHmshpAGbEbXzU97+0c6TC815pwqi31DMq/uA9sRhrU5L08PLkLWw+jsXGbeNSs7U+LUzGo27d+8eNWoUAIDH4yHNcQ+CIMgPP/wQGRkZExMTFxcXFxf36NEjAID1fdPl14mWYG7mVcngAQsRS4IBAgDiBDB1bcMTmbns0AB2iJ/hXjwAgB3kQxapmKr5f5L1j38v/LnZNbdjp+WwFpeqaL4YxaWanW8Git3rHVjfFPgQlEBYPvVn3Pd2TB0X8/hmydziEmcUxz02Gx+upEqu8Lo9RF2b5w6MKiVTIu4LX3Vw+sDT+4eQ9Olx2n/UDZKASMcCmGtOmgYYAnWc1CxaAQBiNLdUluIB0qERzlNul17NMWU2l+RWwLrvvicdnTTw0bilOe/kKw1tVLrJtoE/cuTI3Llzu3bteufOHRRFFy9evG3btqZP5uHhERMTM23atKKios2bNx8/fvyHH34AABw/fvz48eNNl18nCSrLN7erpKMxxuoyD+W+pyyBOf50XQYekYhIVSm3e5jx9gMAAFWmg9ksqGrWPyeW6z+qxi+d2bHT+kRGRi5cuHDs2LHLly8fO3bsO++8s2nTprZW6v8vGAR9K3MwMcy6bjC0w79o0sPV6fBgNjtvg8iYFCRfsArz+rRZHpSpUjJ51D1BXwknXJDw0i3VwTzvIyHpMxtq4yHcK5Iq/RP3jKzlibFBkAz5c97BKa5zEAjhIfxRThOjc/c1i+RW4AFB/Kwz7EnHSqfEeUZ17EjD4xZnfa/StIkytv8fu3btio6OXr16NQDAw8Pj+PHjW7dubZb5eDze3r17ly5d+uqrr+7Zs6eVS1KKWHCpucpmGO7BhjJN9wkC4gTUaeD5/Xqoj5zCOrgZHyRQZVrV99H8fj2qjRGios1B3zWz3nbstCTW4lIhISFFRUVhYWEPHz4cOXJkWyv1/xoMgrZJJSUU9UY4TXzp6/xmQsaceP3tMsnrW1HHyc3yoMxQTNLwe4L+DtyuwtxPUgL+6mYpJJRRuV77O6ZPe2iM09VfFMzrxOkUAwuqXwwbzZ/Fvzuz3EIE5Qn7BkiHacmy+2W2Q5TbG2E4/lMGrp7y0PP7YPFoR58fQkMAOmpxFmNpg4d42wb+8ePHwcHBFc2goKCKSlPNwvjx469du3bs2LGxY2uLOmt2RCxIY67yV8bd2UyO2UAzRpYXQ+QB2lTL4fz+PXl9uhWu2QahWO7CdYhULJ4y6tlhKIQZKP2z/XbstE9MJtPNmzfFYnHHjh2FQuHvv//+1VdftbVS/99JJck0kvTD0LMDuIptgaTa4nu6M+rgiXf4plmizCEE4gTyNH+pcj5M9j3didtV6PNzOKm25K1MQUQoWo9owCrSWG5NV8mKhiz7o/jk664zK3pgCJ6imPNT3n6SaYZ6KC2N/kZZ5uQHnt8Hi4bKAAAQDnf4IRQi6PRZca1v42072fXs2XP79u1r1qyxNg8fPty1a9fmnVihUPz55587duxwdq6txEvzImTBZVWf4DFXlqWY8GSQbAp4sb1pUwrMDalFgijiNeGoVyllCeIggmyV5LHyXeYXg2SjwoTN/EezY6clmD59+qVLlwYNGoTjDcuoaqeFSLBY5harVohFgznsxSr1+pfQr0Z1avb9f+FwaenvxZxwHsubCwCAObBwoDRvY6qwvwMqq/ubQJX+BShbBTgQLiIe3Git/qc6b6SM36Stq9avJIrulF7vJWmNlGhNIWNevHyBh9W6W4Fw2Gtfx/jga+oThQ5TWs/egZoM/M6dOwcNGrR//361Wt2nT5/k5OSLF5t/XxmCoGfz0rcoHBRa3qtK2ToIhTAnPEhJZUpJb04gY0gEtRp4AACEwKhTHbmNhjiOKbM0zFfFjp224ty5c9euXQsLC2trRewAAICKoucVq9ZIxIM5bADAN1LJe0r1JnXZaomoGWdhKCb7/UTFFj/99dK0ybEdfgpX7s8t3pMdcLZr0oi7uitqQf86kgEQGcshmAVxqqTuZkypDFnK6Rzb6GWG4fJxL0lsx/dLMcfGyWxNPPcEp015yOsuFAwo/wMyBJ0xN57/ikQywan2Y5sd2wbe19c3ISHh/Pnz6enpLi4uw4YNE4ma87vVhrwZUj3dBO7B8SmkMn0omBNIGx/X8zZZ978buJcb7m17Yapi98iOnfYPj8dTKBRtrYWdcsQIfMBR5ouVX5xxCPpWJski65vto55ACORzPDztjYdeBzqWHM5P6HOLsdA+J8Iz5z92nO9Wp3UHAOCe6yxZ61i+UQCqWMukTQ9fwz3WVbbutN5YdvKiKSEVxjFur86C114CtVa1wSDcEW9tQ9iM8HuJO0SHpk156L2/o2CAA0PQaW8+hBDY+1AIhLV2sFyVPzTDMEePHl2wYMFnn32Wnp4eERHRs2dPrVb7/fffd+rUqZU1ayEuZ5qNZJWNEMUGX4fOogyShLlBtOFRPeXQOoP2j39rGWCmTffKbjReUTt2Wot169bNmzcvJSXFZDKZn9DWSv3/BQGgwrpbwSDIB2v+DFr8l8QdfgzNmBXvMN3FYZKzz7HwzPmPBa+IFZ/ZqKdlQ0/JcIjlThYeqOghiw4DGEekERU9DGEpWP0NbTI7TIsQjn5Nf+Wuau+LH0XM7yXu8GNo+qz4sguqtDfazLqDagZ+zZo1b7zxxr///vvPP/8MHDjw008/ffnll7du3frHH3+89FJ7r4tXT3bd0yaoqntq+N03WBgG5nakDfH1lMPtGW64/YChakxQRTP0weydZaR9od5Oe2fFihW//fabn58fh8NhP6GtlbLTGvBfEnsfCsmYFc8O5KXPiRcOcKindbeCeW6y5H7JkCoAAENpLTlbcK/Iyo/v+v/uoC5y6dzXWYEdOJ2CnFa+Y7jzkCxqTpft9gn/JXGHo6Hp0x9CWJtZd1Btif7AgQNHjhyZNm0aAODYsWOTJ08+efJkREREDcc+l8wO43sKq5y1KVHP/1f9xdBgAESALGEobX1iTFG5FHWSmeISOeFBNgdwEO4Cr+UcuL4JqO3YaStSUlKq9SiVyjbRxE7rI+gr+T/27ju+ifJ/APjnuZWdZnVvuqAFyhZQhuBgI4IKP1S0LhTFiYILBRT34iuIILJEEUEQUEAUFBEBZZfdUrpn2jTNuvX8/ghWKC20pSSlfd6v/pG73PjcNcnn7p4Vu6R9xpiDQY9Ghr3esG7kKVUibR4l5LzFxb4r5L5NGwZRmgtKJ/nsPGVyfPUkUnCKuCj+bD7jp8FXrpxdlnUXFjFcPMdL29uQ/E9PNkSBGL+NrXdBqsvPz+/fv7/3tXf81qFDh/o+pqtqSFzNWxNFG1XZknwBYxZRSJWEncdQ/Rp0avv1cB08XleCB4AkbUpdbxFE82E2mw8cOFDdFNbhcNx3331Wq9W/URE+o+trbH/8esbUsKZxXmzENPfB66SAflLJN8rUP2u8S5uNYskF9+tisZUJbEhvuM3Mo6XW/krlg/+O/fq72/2itWJbaDBbW2e0XISfn4RdkOAxxuy/Tb+8DWbYuluCXaNOWkUASDT9d+Ca7gHhsxMml5VP1GvbqVNkZ3o9e2zQ3drnEo/ovf4q/80hVQ20tLTrJKIlmT59+ltvvaVSqRiGMRqNGRkZ06ZN83dQhE81LrsDAGIMbPgUz8kJbPTriA2q8a6mZ6eCF99XpbZTdU7GkmxbvQmxDBd1DY/Z8bHZNKGkVAQ8Ua/b4fa8aK341GKqNbs3B61u6LNtZ90VHvxCz/8ewouFbyLxvfdPAwCIAABL+TPPeN9iQyex0bPq3BZCiLlMpftoddyCsx+SBE80ZwsWLNi4cWNQUNCMGTO+++67Tz75hOf5y69GEAAAwATfh8VSNuThWt4KMgc+k1a2YKX8yVIsCIqUhMApD126Fn0zZ6apJYGWCSWlWaL4h9vzqcWU2ox7j6iZ4OfMmaPT6QDA4XAAwNtvv1391gsvvODLyK4So4rKtF1QyQ5jGQB5IJBnuQAQZKmcYkMAizJfgOHyP3Oyy02p6nwOE6qIeDWRdApGNGsVFRUdO3a0WCynTp0CgAcffLBjx47PPfecv+MirhGIYSOm1vWmMjk+/MOXZLsDcSxSNN9cWH9mmnpUr3/Bah2jUTfn7A41atHfcMMNv/3224YNGzZs2LBt27Ybbrhhw3n8FWLTMqsoq+uCFqWM+TYATEt4eeCbTJsPASM29kMAGgAY8+UGosU4b9JrsqPmKPU1FHsKZNyAAaEJwpfatWv3zTffUBQlCEJ2dnZxcXFJSYm/gyJaFEqnaRnZHQB2uD2zKyrmWsx/e/jPKmvry6/ZuOAOfseOHf6Kw2dMStrquiDXUpoO7lP9lQm/8XwhY3xIUITJnjOykEsbBlLa7pfZHEKKdvHO3Qe0A3pdYqnVhcvbaTv0Nw+68vgJosm9+eabo0aNuvXWW9PS0rp160bT9MiRI/0dFEE0RzvcnmnWcu+T+ZRAy4SSUgCYqG+Cwf2uhmu4LKRxkkzMA6na8+eUry7Kf+N+LDITi6Z7JDel7iBmTwdguTZz6rNBbb8eVb/tufQyI4LvUtGaxgdNEFfTrbfeWlpa2qZNm2eeeWbx4sXvvffeF19cM6NzEoQv/eR0zf233N1MU4sCzfs9vOCn4d4vq9UleDWLzm8pV/59ce6UU0Cby1aO4ZC7/OcpWCzHkpMJfRxx9RoVQNUlRcgtFIsu1W44XBl1naHPlYZOEFfH6tWr1Wo1y7IURQ0ZMmT8+PFr1671d1AEAdmuzBK+yN9RXOBNk6HjeeXuQTQ9P9BMatE3Iz+ccg2NV9EIyr8vzn32pK7fP8bbfhQK3QjT+oCv5EoAAFy113NsFBN8H226zLNKxNCm+0bXZ2D7lflfjgoZz1EtpCCKaAH27dsnCMLDDz8cEfHfqAo2my0tLe2OOy5XAYUgriZe5udkzTaxlqnxb6KmGCG3FWqNCf7tv+zdQjn1r2W5z51kAhkhLp4JPaiI/chx+KCqzXzACANTGZimOfMoG/VqfTao6dOtPouV8sW7K37vY7rpysIniCYzceLEkpISm802duzY8+dPmjTJXyERhNfmkrWxqoQSvmhvxc4ehhv8Hc41qTUm+Bd7680qynrMKVYKAUPNp5/ttC/j9r4nfzN0fc+TuUsucwgRdsn2C2O5o0a3i1fogajJ5DqUaFb27NkDAP3799++fbu/YyGaF4+EFbTffq/KhbKfSze8mvBehWD97Ox7qfpuCoqMj9Bgra4MHgCGxikVNBIK3FwQV/WHrbLQs9L4FGfccnz7zj8/nHr441dtxTE661o28qX6b1Oy2uybL9MGQUmpFJTSJTmvLHyCaGLV2T0rKyszM9OvsRDNgtUlD/7Wn+MRrCpYMsA82MIFxWvaxmvabiohlUIaozUm+H2F/FmbGPJcDFCIDeMSRx+7yWnenXVPKPNGCTKI9gB9rJXVJCO2AWMSIwVbvmK9bHdcejGX5Jx2/DGHVHVlR0AQTeOjjz5KSTk3YsLMmTNjY2Pj4uL69etHBptp5QQZu0W/1QzPcJw4WXV0cNAo7+RdYff/UvpjGU/6Zmiw1pjgf8x0f3PMRYcpE37qwp91M6HcjXefVs8fCIGVKaY/zq4oC2AcIDesq05Ko1Z3Tan6fe+lF1PR6lsDR1p58utJ+N/cuXM/+eSTF198EQBOnz49a9asL774orCwUKfTvfbaa/6OjmilMOAV+QvHhN5b/UzeyJoHmod8V7DUv4Fdi1pjgjerqO9Puk5YBS5CmfBTFyHb7Q5kckyK0rn3Gp5a1hO/WRY5S3afAvky/dPVoL3lBvvmHXC56vSDg0ZFqmIaHz1BNJFvvvnm888/Hz9+PAB8/fXXnTp1SktLCw4Ofu211zZu3Ojv6IhWalf59gJ3rlUo+bF4TfUfBrzXtjPDccLf0V1jWmMlO5OSEuVzeZiLUMo/dCwZsj8My+6yTg5FnAd5HhZ6b1QmyY6DlK5n/TerbBtHqRR8TsFlx0qyi5V/Vfx2s2U4AGDApxxHEzVkYFnC1w4cONCxY0fv661bt95+++3e1yEhIUVFzavxMeEDh0uEJ34u976WMC514v4rir2THIXWjrGofTKuuZkNGmAZ7JRqFncOCrxNSat8EEBL0hoTvFlFOXhs85zrsLYshP10VZvblOqed58sEz74IEzUImRXd1FX7W1QggeAkDeevez4cgCgoBSbitd20HUJUYT/ad22KGfOtPjZ8Zq2jTkYgmgsg8Fw8ODBm2++2Waz7dq16/33z42KdObMmbi4OP/GRvhe+0B22XAzYACAUqc0cUvFsmFm71sqFvkmuwNAkjYlSXv5Gx4hd7ZY8m2tbyniPqX0vZs6rmtSK0rwTgELMgaARBMjyHhvgSfFwgJAGS+NjAq4X689k6gJy1SUR6hGq1U26BJo3wyhDdtFfbI7AHCU4uWEdwIYg1t2rSn8qpex/4r8ha8kvEsa0RG+NGLEiLfeekuj0cyfPz8+Pr5Lly4A4HA4XnvttQEDBvg7OsLXEECk7twvGEcDjSBSX68fNL+g9f3Ewi8UKRvQec3nJPteIXs6pe3kx8CaldZSBo8BRnxXeuOKkhtXlNy2ugwDfH7A5Z1883t7XAULAMq2atVp1yC1cnKALtnUS67a3ag9YbG0/LJLGVkzhejvC1eEKML+qvgNY7zT+mtjdkcQjTVr1iytVtuvX7+//vpr7ty5FEVt2rQpIiKisrJy9uzZV759jPGiRYtGjBhx2223/fTTT9XzCwoKhg8ffuXbJ1ozSt+bCugrWdcjRcy/f9Fi4Xw26nWg1P6OrrloLXfwCGDruMDqyfjPCiZ11TzW+YJRZ1Qp2rKvC6cZ2gAAUsQAljGfj7jLFKjXINmqCqa8HfHZjMuOjXjSkb61ZIORNV9n6FPgyV1duLxrQC8VTT6ahI8YDIZ169bxPM/927d2u3bt1qxZ07dvX5puglu3N9544+OPP544caLVah07duyiRYtGjx4NAE6ns8UMP92CNf/HiVz0TPehfoxlHFJEAIBY8jUAYixj/B1XM9JaEjwALD3icAkYADAABvgrz1P9GzY4ThWlp1UddfxbZ8ok2YHlKIahtF3lqr2X7Yu+BtqgU7RtU7Vjr+6m6y+95JaSH4IUIUFcyINRT808+VyoMmJj8XdjQu9tzLERRGNx542cER0dHR0d3VRb/vzzz1etWtW/f38AGD169IgRI9q3b5+UlNRU2yeuniAN/b9bjP6O4jIQF84Epwk5M7j4z0FyCDmzFInLroUrE99pLQleBvj47yrXeV037C4Q/i4UvK+1HDU+Ra2IVSVt677R49nucr9vNlLabrL9n4YmeADQDe5XvmTNpRP8sapDZ10ZHpkfH/4wAjQu/MHPst/Lc2X1Md0UrGjYMwOCaJ7sdntCQoL39YABAx599NFJkyZt2bLFv1ER9YEAOgez/o7i8tjwZ1wHe8j2XVL5FjrgRkrb1d8RNS+tpQyeAthxd9Cue879IYBJXTTe13vuDR6fcu7BeO5zJxJP8+m8AACUrrvUqGJ4VYdEwNh9uM4mmzKWV+QtREC1USccsO19N+PVfHeOjg5QUupVBUsad4AE0dx079595syZgnDuMnrGjBn5+flTpkwRRdG/gREtB6VkI1/hM58Ri5exka/4O5pmp7XcwQOAmkHwb0sPlkI6jgpQ1Ly+wSI27nOU3ITssqzTdvc400F2NrjKBkKWp+6jNHWuJYMcpow8WPl3EtM+35OT485S2BWhivB9lbs7cT0aeFgE0UzNmzevf//+K1as+Oijj9LS0lQq1Q8//DBkyJDFixdfdl273X7zzTdffClgs9nI9QFxPsYyRiz6ggm8C3Eh/o6l2WlFCf58EuAT1lp+JtRd9GK+ux2nT+eFnkolpe4g2ffSAf0auv1L93VDI7qML+kS0LOjrisAJGpTGMQgQDaxwiHaG7ovgmie4uLiMjIydu/eHRJy7pc3Pj7+yJEjGzduPHTo0KXX1el0ixYtcrlq9ib5+++/T5069aqES1yrkDL5B0DXQIGC77XSBK+kKb62oRQCH4rgc90P3HXozNNhPUdE0Prr5cqdjUjwlyZiIUgRKmHxb9uf1TOrRLuO0atp7SVWJIhrC8dxffr0qZ4cOHDgL7/8MmrUqFGjRl123eTk5Itn5ufnI0RqUREXQpdpstRqtdIE/3AnTV6VdPF8Ps+dnvqnYlxQ1OOZlWqVpvv1Qt57jb4yLJr5P9NDd7EhgTXms4h7OOrp8+dgwC8cmzgseEw7bcfG7o0gmrs///zz8gsRBNFEWksluxraB7LFjpoJXijiT488QKnpkHa6hR+EZz2Q7tgVjx2HQHY3bi+KtnG21ZvrsyQCNCnmeS2jb9yOCIIgCKKGVprgu4dyD3e64GG4UMSfGrLPdFcIG8RVzsh8ZJ41dkmHrEfOSJ52UuXOxu1FP7S/6+8jQkG9hjGOVsVFKmMatyOCuCbMmzfP3yEQRCvSShO8hkXXhf1XbINFfPLWf4x3BMseWaoQzRPCwjvq82dmxC7uYF2Z5MnY1Li9UGqVbnBf2/cNaPj7yZk399sa1UUuQTR79913n79DIK5Vo9eUVv47QhhRT600wQPA0iMO79gzAIAYpO0VULY037a+OOLdRPdxJ/1OvDVZffbRdE9OD4R+a/Re9ENv5CIa0HhjWPCYM85Tjd4dQRBEi5RfJblrqTdFXErrTfDLjzizK//7vLDBCqlKojSMpkeAc19lqSzt1smSXZYrkkCuxJ6cxu2F0qj0IwbWf/k26sTbQ+9u3L4IgiAIolrrTfAz+wZE/Ds2IhZx2VcFYdNiVanarLR0dSed8c3sxA0VEd+0d6U7sNRbsvl0qLd/bLt2V+zw5R4JgiCIFqaVNpMDgPPL4BGDEn/qcmrY/tCXY6tcYtG2MlOB+9d3IoMfSI/9pN320P4hxZu6BE1o9L6E3EL7z3+Y7q/vMEdhisj3Mqf3MNxARognCKJ12P89XgAAIABJREFUcgp40pZy6d/+Sio88uSfK7h/hwh7qbc+ydR681c9+foE2e32r7/++siRI0VFRaIohoaGpqam3nXXXXq9r1uIHS4Ryt1y30iFd1IRr07Y0Nmb40vc0iEbP+qJs1nTonYO0exyXPeF+1XAQqM7S2KCzc49hzS9uijatqnP8qHKiLfbfUayO0EQrZaKRY931br/7ZEsvYS/p73aoEAAwFAoNqAJRjRu8Xya4Hfu3Dl48OD4+PjevXu3a9cOAKxW68KFC59//vnNmzf36OHTbthPWcU/8zzVCR4AFPHq+PWdTw3bFzfEoltbtuzd4LGdJ8ZnetJYBsmi51A/+LebOczolW2/q/++EMsax48o++LbsHdegPr1w8Uglpc9+2y7exr7Nui4CIIgWgAE0DXkv+esHI26h3JB6tZbrNwIPk3wkydPnjVr1uTJk2vMX7Zs2eOPP75nzx5fBhOmpfMv6sxOmaDWrks9ddv+oFSd/bbgxKyDDKUu5TpytNmIXYgJAwC56m+EG3xvrbm+a+WP26t2/K3t273eK6Efir7RMNoOui4N3R1BEATRyvn0aigjI2PkyFqGV7/ttttOnz7ty0gAIExXS4IHgNi2usO7Oz/zWXhppTDfMBUk22TDW6jN/xBjUrb9jov5EEQ7G/t2g/eHkPnhsSA1oJ0HR3HPx81qo05s8L4IgiCIVs+nCb5///5Tp04tKio6f2ZZWdmzzz7bt6+vH0SHaKhih1zLgDMAANDzW+uAeUUP2D+jsLwi93r96fFy1X7X31Hug50BgZj3XiP2yMVEaG/s2aBVDKxJQ2tzXFmexnaXSxAE0QK8ekOAWUWezzeMT8/X559/XllZGR4enpCQ0LNnz169eiUlJYWEhGRnZy9atMiXkQAAR6Olw00XP2rPFsU/3Z5H+oUm/uNUCGWAOApLDlAhSoUxBiwBojFf6MtQ91T8sTjnU1/ukSAIolkZ3EZJk2rHDeTTMvigoKCNGzeePXs2PT29oKAAYxwcHJyamhoVFXXZdauqqrp3787zfI35DodDEITGxdMtpJZBBqMY5ptgy2pkDz/q2ad/qLN9Ia1O1UsVbNTbnoyHaf31kn0PEzmtcXsEAE9GdtXPO80Tx9V/lZEhY4/aDzR6jwRBEEQr5NMEjzH+8ssv165dS1HUI488MnjwYO/8goKChx9+eP369ZdYV6vVbtmy5eJc/uuvv06aNKlx8Ww547aoqC4XpflVDudnkvPDRHWHyqfs1NelotBGyBWL5gMWZE8BYi1syEON2yMAcFFh7vSTrv1HVZ1rGe66VgxiOuq7YcCF7rxQZUSjd00QBEG0Hj5N8G+88cbHH388ceJEq9U6duzYRYsWjR49GgCcTueGDRsuu3pkZOTFM9PT01H9Gp5d7HS5eKgY10jw2aL4eaV9caAF9bJV7XLuu3PWDblPn1b3ja/chrgQzGcr2q6Uq/ZJ9tpHtmZMw5Ey7hI7RSxjemhs2Wcrwj98CSlqeYRQF5fkfDvj5SdipsVpkuq/FkEQBNE6+boMftWqVTNnzvz000+///77CRMmnDhxwpcB1PB/yerxKeoaM6MY5ufQ4EiG1vQKqPqrIibwjhw2PoE/woQ9jkUbovV0wADZdVTMex9E2/l/mC8QsmdgwXrZ/ao6JinbxZev3NigaNW05qnYl5W0qkFrEQRBEK2TT+/g7XZ7QkKC9/WAAQMeffTRSZMmbdnSgNFUm5ZBSRnqflc/0CxZRTPHTjC/81nh7XLZRoQlwAIWrUzgPWLhQkrbhTYNr15eyH2LttxO6erVzN2UNtp9+GRDA45RxwNAlWinEa2ia16aEARBEEQ1n97Bd+/efebMmdXl6DNmzMjPz58yZYooir4M43wzd1ZKdQwxTAcwymSNmOeJ0aXk6AfL/Fkm+D7KcJNk/QEQzcbM5s++Av+2XsN8vli4kIucXs/9Uhq1umenxsW8r/KvdzNflTAZOpEgCIKok08T/Lx58zZu3Gg2m72N4lQq1Q8//LB+/frevXv7Mozzbc1y59rrvLywbSotXZLfT6X8n+klOuAmNvJVxnKnWPINAND6PpSmg1Awz7skf/YVJuQhpKillsClyQ5nQ1fpa7r5JstQDHW14T/HLlY6pKqGbpwgCIJoGXya4OPi4jIyMjZu3NinTx/vnPj4+CNHjixcuPC1117zZSTV2hiYzIo6b4V1/U32bdbeSgVPmRVtVwKtoQ03YfcZ2XUKALjomWLBp5gvlO17ZPtuNuyJRgRQ8OL7rkMNrojQ23gjg5izrgwR13l18mnWW/OzG9MhD0EQBNEC+LpjII7j+vTpU10S750zatSo6dPr+3C7acUamExbnTlSd4MhaHKUCqH5gaZzsxBDW0ZLxUtlxwEsVtCmW/nMyXzmE2xwmuw6KTsOgOxqUACmB+4sm7tctjsaEfyvpT/V1QHO37Y/s92Zp6uOH6r8pxFbJgiCIK51rb3nvzgDc6hYOGmtPccjjqJ1dNWuChkg69+KAkzg3ULJMnf6YD7zaanqiGTbJvN5onU9n/mU+/AAqeKXBgWg6pikub5r6ZylntNnPScyZben/uveEzGxt7H/xfMFzC/P/VxD65WUanHeXBE3siMggiAI4trV2hP87UkqloZVx+ssCBfyPcVzsssl+Z7iUm9tPEqdTHGRQCm5uP+pOv6mTN6g6rBd2WEbGzaZ0nSgTUMaGoOqa3vX4ROlHy+xLl2b9/jrjl3767kig5hkXSoA/Fm+7fw6dz8Wr+Flz4SIR++OmOiWHFtLLt/HAEEQBNHC+LSZXDOkYlCOTSpm6qhJDxAwNDDnuZMxbhxE0/94+O4KDgCYkAfFwoVC1ouK5HWU7joAANnNZ7+uiJvX0Gsm2ekq/Xhx4HMPqrukAEJ8dn7RjP9xMRFsaGD9N3LAtvesK3Nc2AMAUC6UbSpeG6dOaq/rDACRJbHrilb2MvUPYIwNCowgCIK4prX2O3gAOGMT6xxUDoAOYDTd9JW/lA1WqTY6z93oM5Y7MV+AhULJeu7mWMj/iNJ2o/QNbg7gOZnFRoaqu7YHhACAiwrT9O7i2p/eoI1MjH52SODt3tdf53+BAd8d8Yh3ckLEYxjwd/nLGhoYQRAEcU1rpXfwWTYxz37umXalB2dWCDtzzxV+R+mZSD19/sIBIwIrfigZNrTtqMKSlwyYRQgoFW0ZA2IFf/YVleEmLFrFwoXKDtsaEYnscFKac13WyC532ecr2UCTXNWwtnMUogNYo4jFOWdmH7Uf6BjQ3cqXWPkS77uJ2uS/yrffHDg8ShXbiAgJgiCIa1ErTfCrjjt35VVXPcPlbnh/z7km4/2iFU921Z6/sGFEUP5rGdESimOZP9yeG1VKAGCC0zzHRlLazkLhZ7LzCBPyYCMawQOAIi7aumSNN81TKiVCqHLz74FP3d+ITTGIkUGUQS7hC1YVLKmeL4EkA2Q6T5IETxAE0Xq00gTPUkjCFzyXr55UXlRqwQZxqmSt66hjZIJ6rcN5o0opla0RresBy9idLVZsxcCA4SbPqfsBgA15rJ691XoxIRbdgF4FL76vG9QXMQxfVIIY2n0so/5jzZ2vr+kW72HIGFPnjcGj1xkMLCmDJwiCaEVaaYJ/qrvuqe467+ueS4vcEqwbbbnE8gnrOyMFNUiW362otMuyGjFyxRbaMk6yrqeNgxBjRupkEKx8/gdcRIOHijeMHaZoF+/6+zAWxIAhNyo7JBS8+L4yJV6V2q6hm+puuL674XoZy1OOPTQ+/KEuAT0bugWCIAiiZWilCX7VceeB4nOP6B0e7Jbhpd9t3sluIdyoxJojtmEZjnbalfxPzy8CzWqKkh2HsewWy74H2SFV7gZKDRXbsVAGsiA7j9KqxIbGo0ptq0ptWz0ZOuPpBo0kWwOFqOfavF7E5zd6CwRBEMS1rpXWoo81MB0srPdPySIMODbg3JyYgFoueigVxRgY+/byDhxLAzAhDyFKjwC42I8QF6LqvE+RuAQoGimjaePgKw+PNgVQmisaFjZUGdFJ3wMANpesqxLtVx4SQRAEcW1ppQm+Wwg3Nlnt/WtrZo1KKt7IeCc7B7O1rhI0KcqT4QSA9yoq98kGJuwJYALkqn9A5iXbNv7sVISUXMzbQCmaME7rl9/xZ3KvZAsuybkkd25TxUMQBEFcK1rpI/rzdQ5mMeCzNhHgUrnZeEcwn+XCAo5lmUX2qk9DJ4nFy8Sy77jQp/is55HkotRJtOGWJgxMyH1HmfyX46+3pMp4Sn3+DT3iYt5CbFB9NnJbyDjvC6fkUNOaJgyPIAiCaM5a6R18DdeFK+5uf/nkV/BuVsmC3KFq1X4PnyMzXPTrCCkk2xbsycaSjY2e3dRxYUqZr4i9274Rg9yXMY1kTCMRGyxX7Ud0QEO3tTR33mdnyeByBEEQrQVJ8NA7XNE7jKPR5Zc03x1a9FE2nHDcpdUsslfRphGUup3sPgOMBQNLqeKbNjA27Eksu5TJPTR9p5R9UUybb6NNw+XKP7jomY0oCHgw6skehhuaNkKCIAii2SIJHnqGc11DuImby4+UXGbUNalcFIo8JwbuG5eDNzldxZLERs8E2Y2wSGs6ikVLLr16g1FKLmo6f3aa5vrOoW8+BwBi0ZeIMdOmYY3YGINYb6u5XeXbv8pbIGC+iaMlCIIgmhOS4M9pZ2bOVkqXWKByS9nZh9MpDokOMWvogf/LgaV2B6VuLwdO+Mr4XF7km0LeO1gsa9qoaPPtQGvFkq8QQ2OxQsh7F6meucJtdtL3oBFdzjdxqARBEESzQhL8OU920w2NU9b1buWWsqy0I4BQ/LrOEMBIHrn72JO79lsLROkB5aTDAWOCtclIfZtr/ytNHhgXPVvIeQNLlULubNo4vPjdX23fb7mSDapo9diwtCBFqEOq+rZgiV2sbKpQCYIgrkUVbrnEWeeYotcuUov+Px4JK2orirf9VJr1YDqiUNzqVE2PgLzJYfym0tCj7mfvOztzBaNPVP3j5ksL3K4HhoQ+/2hl5Rr9wNsv3ohs/0t2Ha91v7RpOGLMF8+XKn7BfA4AIFWs5+hw7M5kIqcGTaEqf1xk/TzfmHYPYuiL16o/JaVCAL9btwwNGlP/tdLtB5J1qQjqUWeBIAjiWvDdCVeZS36hp87fgTQxkuD/c/d664MdNRsyXHNuvqDb9uJPsmW3nLixs6ZHAAAMfypu3nDDoZ/Khk/PS/rOumpK0Fykc4w8aBwdzUZ9KBY+bvsxNWBIXI2NC3nvSZU7ERtSYzb2FCi4SNow8OJ4+FMPYskGQAFgABmAErKmA8jqrth9OLr4zbnBrz5xJcdLI/qO0Ane13+Wbw9RhLVRX6YPvuP2Ix9kvjY69N4hQbVcxBAEQVyLMAYMdY8afs0iCf4/7czMliz3X3k1a5/RJpbWMcXzcmO6ByAaIRqNPiEf/6DQraEK9CjCKrNpRwyjg0OnxgLEOvcOs29/HvD8gKEXdG5P6ftLtm1s8L1I8d+QbkLhXCwUU5qOtcZDaTpKlTvYqOmIMYFQDIwFECXkTMeCzZT2qiejuAmPXUmpluXOfzFhNovq7CIXA16Q8yFDceuKvrnRPEhFq5swAIIgCKJpkTL4//SNVPxTyNv5mtdxMQuSlUnqqj8rstLSsYTtdnH3u2e0FdKprqrjo43j7zmzb5A+dGqsZBc9mS5119m6vmesy76wbSw9fyOM8RZEG4SiRcAEAGMAxiBLVXLVAdpyO6K1UBtF0nJASCz9hgkaz4Q/zQTfA5wFC1Yu/FmgNYrEcxcKQkEJlq609KhLwHXTE99nEWcVSn8oWumQqi5eZkvJDzaxYnri+zTQpHc8giCuaYKMbR7Z++cWsUf6b1JuKTfz5A7+PzdEKAodkizXLF2m1HT8951OjzpQtbPi7CNHM0/aTXn8qR7qrmPDtdvcKpc0825dpxInPSbdc8oZv6aTKmVe4CPji+Z2CRh6c/VGhMLPAQvYky9kvYS4YACQq/YBYKl4pRz8KKVJrSUgWseGPiHkfyJX/EoZBgCWhFMPIlrLRL5w/lK2tVuEs/mWyfeyYcFXfhJUlLpSrNhd/vsAy5Dz57tl1/eFK/oYB4YpIseGpS3Nm5fvzglTRl75HgmCIHzvgz1VK485va95CSQsrzvp9k7eEqt8q3+DOxNrhhDG1/a1yvr16++44w63293oLezM9cw/4PC+PlTMV/G4d8S5bmQMCvTJv+XxskM6NWI/n+PmZfxXH02feSmJ2aJULv66JJs/aDcjqk1Xg2FEUFbaEcNtQbT6M9Odh1WpP1b3SIOFYvfB3pQiUnJnqrselyq28qcfpIzDAECR8EXd0cmuPdFA0apumULOm0LeB4q2K2nDzRcsgrH9550V32wIGD1IP6QfoCar/ra+6FsZy8OC76ARvTD7o79tuz5OWaKglADwwvFHFEg5I+njptoXoVary8rKVKorGmSoxbvy7ztBXGzBAUeZW5raU++zPfrm+04e0YNHwhVu2funoBEGqJ50Cv9d/VAaOuGHzoo2atctxn7z2ndUKiSblHnP4VunJUTkiSjXbX81Rj/QpB0eWPJlPur+AqUJ489OA4BjvPBORSV2n6H0PbHsBtnpOTHOk/EY0Ca5fDOlScGite7oKC5+ARZtfPbrQv7HSJ1UM7sDAEK6W24IefNZ1z+HHX/804RnprfxRh57PLK7mC/8q+L3u8Lu82Z3AJgYNaXAk/O37c8m3B1BEATRhMgjekAINNy5u17WDQhAzSLvbbCSueACiNLQiZu6VFc013TXB9xsPtF3T+jIoGxePDn6gP7mIOefts/fC7/nyRP08jcCjOPycj+5B90RwTCS5yfZvgfLHsSGSLbfAVHAGBAbKeS8SRuHIcZUV3i0aRCljBXzPwFAyqRVdS3GhgReYaX6i5m5QG81+3cyXsEY77ftpeC/hnlmNmhJ7twu+p4UIpeJBEEQzQ5J8JBi4e5JOfd67SlnQZWnrZntEcoBgJa7zONuLl4tlouGYYFRN5m/n3ioYFVR501d/s8Au/Y5Sl/Ji/1xue74sGEBigdiH2TQs2LJN1zEs2LRV8DoKG1P2ZlO6W6gdL0RdZlO7BVtV7sOdKUttyFFRH2OCAti/rNv6ocP0A3sDdSlsy8Wzk7HUi0DxiPWxEae67enXChFCB2vOnS86hAAlgFTQGHAMmC37FTXUUmQIAjimsBQwDRd4WbzQRI8BKmpIf/2YXeohN+O+DFJqvaBtY8KX0Pw5Cj7z2WZ4w4nH+w5an5HzMuIo1IW52u3O6d+GZlfwbYPXPhF6f0qW4Q7vx8b8YJY+i1ShNPmEVLFL0zII2LBJ5jZerTH75YHwiLeqrMNOlLGKttvqb0iXq3Ls0zgsw9YF31n/+l3w11D1T061l0wj6Sq3Ygx0cZbz58rWX/E7tPVk++0+1zEovd1kTt/deHynsa+XQJ6UkCRxnIEQVzr/i9FLV3btdFqRxL8BYLVNEuhemZ3AEA0il/f+Xiv3cev39P++A2Ukvpl/tmAD3PU61NL1Q4Zy1aujZz4jeuf0QXvpgUMv1t//VLaNJTPmUVpUuXK3zA8fnJgJmNhSz7LBQwRb9eZ4ylttwYdCBcdHvL6k65/jlSs+glxrKpzcp1LxrzjOX4XF/85os/14oRFm5AzW9F2ZfUyCkpZPXpdG03ilLgZ3tdLsv+X78l9PPZFHeO7yikEQRBNq9Y+TFsAUnp6gTZGOkxH5dilD/fW8tS6VohGSTt6KOPUZ+45XPJZjvnD3NcXR92tcqgRzDIayyR5+fOQ++qrwY994fp7nmP/00LhfC5iGmO+Xao8e2posiJBnXygd+QHSSXzc3OePdG0h6Pq2j70rSnV2Z0/mwdyzRbzlCaVNgwU8z6oniPkvUObhlKaTpfYspBbmD/lrb4vnOy1sLzilXlCXtH3hSsOVO5t2vgJgiCIRiN38BfoGMg93lUbpKZWHXcNbqNsa67XrTyloBI2dz3SfmfVXxUxv3XP4yrdIH9gMvdXKMJfyvJsrMiMSoxN/Dng1jvdx7OEwLZMoFPIWZr36nguVp+0rTulpCxp4QCQ88wJAIh8P+kqHZ118Wqx2Kof0l87oCelOlcq4TnzPMhVUuFq2X0C0Vos2eWKXynjICFrKhvzVq3bkV3ugpc/AIoyjhpkxLhy047Cl94PfWfQUfuBTvruEpaOVh1M1CRXV7knCIIgfI/cwV/AqKRGJqgUNErrqJm7r5be3OqCGJSwoQutYdx7bTEMPcNgeKWsfN8zx9RrSzSx6i1fJSgS4tQ3bGbDi4W8Yj77Y8ceo1jWq+327pTy3L/AkhYePiuhZEFu/rK8Ghu3X3Tb3Tgh0ycHPnWf52Rm7mPTq7b9BQAAslS8WKr4BSidVLFDsu+XKnYApZfKfxGLvqxrO1W//Il5IeJ/0w13DjHcNTTif9NlXkj+m/q/8IcAgJc9v5Ru/CpvAQBIWLIKpXVthyAIgrh6SIKv3T3t1X8XCsfLhPqvooxXJ/7cVd9W+y1rGKNVzy5gYWE+H8p23tj1o2gLAHChFv2QDVV5MVKl0rF3sCfXw+d6AOCJUuuaKieWsG1LKaLQC2bP9w5n9WZXOpwjikqa6rgUCTGBT6eFvTtVkRADAAAIIRokBwAP2Al8DmA3AA+yE6g6O6X3nMhiQ4Mo9bkuGiiNig0J9BzP9E6qaPVTsa+kRT4BADnurNdOPrOp+HsAkLHEyzX7+ScIgiCuEvKIvnYKGj3TXXvCKtbzKb0XF6nkIpXH++01DA/sOshyDCF7MX+wwNkl4Vz9NbFYKv08zbUr2fL6Z4q4G0/04xetaf97GDyp1WaMPli1ozxuVSpOxTPLbRjgdo16lcP5Vrmts6LOXNs4jKV6uDwkucZQ7NdiYQxtuQPjVxRtvxWKvsC2bUz4lLpWp4NM7iMnAONzlfMxlsptqq611OOLUcV9lLzYO0zTXtufS3LmPhj1VJeA6zyym0YMgxj38Tuw43At+6BoReLXdQ3DQxAEQVwWuYOv05i26pEJjelHMG5lx/LVRcVzsiPfTTTxiB12kD/rBgDXIfupYfsZAOvEe0eFrCkc5YyY+/SD07/52qaBsUftv1njvk3V32xeGGhuy7Ezy23TrBWzy20dOHaepc5ucKrlz8io/LmsEdFq+7wLnIm2HHbsLLFtHIwFi1zxM9BGNuSRulbR39pHdnmK3vrMcyrLc/JM0ezPZJdHd0vfWhemEEUjGgCuM/T5IGVRR30XANhWtmnykXsynSeRVIHFMlnfg9L3rv4D1og9hYgzN+JwCIIgCC/SF/1lfLa/ami8KlJH3/F96fAE1b3tNfVZS3ZK2Y8fd59wBAy3lHyagzX0gpdC7n8xj5EhZkFKwBDLDrfnkZKyG22/v3H6DSHXUrL47oh3b9ffeO7G2o3xyKLiHEGKYejvQ4IUl+uBIeuho+XfFGAaxa/sqL/VcumFLyZZ13lOP4IUbZTJv3mO9saeXC7hS+uSQi4qVNU5hYsJv7gZfdX2PdaFK4GmAQAkyfTQXdp+PRq0U17mWYp1H+yBXacFRDO0FgGSsQwAlFwJGClTfqR0PRt6LNcu0hd9fZC+6ImWgfRF3ywEKKhXf7cBQEaFdKCo9iLkYudFbc/UdMyiFMuD4SWf5eluMiOHlPZENi/iNR9GldxsAIBl9ioawXZD37ndtoolfaPnvKcIe1x27Peuvt7pKhZlDYUKJHmj03XpCLMnHSv/tjBmeQfTHcEZdx6q2NDgAnvaNJJSd8CuE3zOFNmVgTSptHGQ7pbrpcqqkg+/zEmbVr70+xqraPv3CJ/3uuXxeyyP3xM+7/WGZncA4CgOAaL1AwAhFihlzLuK2A9PmMfnUnrAFFDKrc6zv5Ru9D7ev6Zh0YY9WbX88fn+Ds3XnE5nZCQZgZAgfISUwV/GuGT16CQVAHhEsPG11GYvckoDVpSkPxhy8Vu6e8PaDDBJx52KWFXhh9nJiztkXa+cUFzKIlQmS98GBZbK8qOlZfxTT78WME0sXuY5eR9izAcM//cxvrGDMmCOxfRoqXVmuQ0AbtfU3mFc9qRjZcsLYpa2N44MMo4MAoAz4w/HftXBMCzQu8CUMiuLqDdNBu+kCHBnUfH/aTVjNBc8iuDafOg5fJNUvARRSi72AwBQJicokxPgvtGS1SZVnmtQIBaXWZes4WIjFQkxioRodfcOjTqp5+03eoZY+g3ITrHiV0X8vC76610lnwMgLmpmoDImy5UhY5lG9LsZr3IUNzn2JQQo25VpYE16xnCFu/YZT/rN2JWJ0YWVORAG2aNou4o23OSnuK66M2fOLF269Pw5PM/n5ua+/vrrADB9+nQ/xUUQrQVJ8JfH0aiKx4KMa+3LMMcmucXa7zIf21TB0DB/kJkN4So2lOS/njH66477tYofnS4FoG8dzunGgI/N5sllZYks+38hDzEhD0gVvzqy5691v6ENHMWp/29RYOf7i8vnVlbVmuDPPnqsbEVB7LIOxhHn0nnMghTAOHP84fjVqfqbzABwh1abVlIqYPlds0kEGFZQXC7JN130XIhSt6cD7xBLv6UDx1PqC6rL0aYA2nRuaGTabNT27eE5eca2ejN/Jkc/fIDhziEAgEVJrqyqXqwBKAUX8yafMVkq/RZHTvOcfggQhbhgJiStM6DOAdd5l3ok+lmrUIoAAcC6opVVYuW0+NkY8OKcT8OVUbcEjgCASrFCS+soRF9qd/6ARTsGCRgTOn9UHqECAwA0u2ibEE3Ty5cvz8nJGTx4MMMwACCKIgAcOXLE36ERRKtAEvyljFlbllMpeV9LGP7I4bsuLvaOMHdHkuq563SXXv1kucBQCADUqbrk3T3tO8ppIzP+lcIHopR3U0yfNAUApHDMOK0mkfX+IyjacFMR00vPVqLSr/iMJ7BoXWS8dbeyH8iDgbogx/dS0CFsAAAaGElEQVRZXjLnpxIuXGEYdEFltOCnostXFVesLfYm+B4KbpnFcm9pqVhWfkoQymTpp7BgQ20j0LCR02XXKTbypUscEaIp9XWp6utSAQBkGcvnrmw8xzJKPlmC3R42IoSNCtXdfIMiPvrSJ6caEzhezPsIe7I96UNlPh8QUsQtALig1F/PBOiZc1cPT8RMOxcMoM4BPZySAwBELL51+iUdo58WP5uX+a/zF8ao4vuZbwGAAk+ukTUrKb+VbTOmwULRlxRjpgPv8s7BkkPMex8QRWmu9BFIcxYVFXXgwIEnn3zy2LFjS5cujYuLczgcWq121ao6B0UkCKIJkQR/KdEBNPVvJUSrU1LSUMVLgWoqVEOHaRtYfQGBrq8RABLSIkoX5L6/stDwU3HJmGD6JiM2wrNlFQEUGqBS9lAoRmnUHNJA+HNs+HPYkyWVb+pVvtyZ9ySlbk/relK66yhdD8SY86tE+3ed9MP3H+2xu91f13k7zHEdsR/v97emqz7qk7bVe+6s5BZbLPeUlNIItoWFWOoYXw6xFmXKpgYcEfXfOLHKDomRC96QHU4hp5DPLUDMuRvTqu27K1ZuZEIsjMXEBJm1/XowQbXUjecSv3Qf7o/5XIQ4St+L0te3bl0n/bmyfwYxb7b91Puapdh22o7ewnuX5Fxw9sNARcij0VMcUtWnWW8lalJuCxknYmFX+W+RypgYdTwAuGXX1bsCYKJmCCUrZNcJhWkoUiYAgDt9EAAw4c8itsE1Iq8tGo1m4cKFq1evHjBgwMsvvzxu3Lj6r5udne294z9fUVHRtV4vmCB8xte16O12+9dff33kyJGioiJRFENDQ1NTU++66y69vpGjlVzVWrXfHHNm/3sH/8UBR5ie6hOhOFYqhurQj6c9tZ44joY/7wkxKhEA9FlezFBo2/8FXrxY2zn53wfS6t/KKreU6W40hX/U9si6gn+SuB1h6IgsJrDsi0Z9R44TMKYQogFAdkpVf8uVf8lVe2T7HmAtmwqT2kd1jOISM8Z6ACLa/nGD57TjeL+/Nam6xK1dgfrvDlgGGFlYkieIAsK3qFTvm40Xx3O1YCwWlwlFZVKJVSy1qnukcrERAFD543bbd5toUwBtNtJ6bcDIm+SqyVLFLwA0E7KVNsRQOi2im74G6BnnKRoxUapYl+T8rmCpkbMMCxpjE8tfOT65na7jo9FTbGL5wuyP22k7Dgm63S27tpdtjlUlJGlTMOBsV6aZDdQ2algdsfhLPvNZStlG2elvueof95GbEa1VdT0FVPUgPi28Fn1eXt6ECRMQQlu3bq3Pb47dbu/Vq5fLVbOGqdvtLi0t9Xg8VydMgvAR33zffZrgd+7cOXjw4Pj4+N69e1ssFgCwWq27d+8+efLk5s2be/RocE1s8GGzmZQFRT0jmC8GmwFgZ57nqa22CrespIGjqQqPZFGfu2et5CWQvIXFIMgYALH/5qnOIczb/c8l1/4rihcPNccE0IABe+RQhPNeOOX42xYw2KJ/IPz0O2eMyVpTomatRV6gE4ItiniGiWSYCIYOZ5gIGoKFrMk//PFM+6xwJkOyH5NduVKFWcgLxmKEYWQHpIxAbAjiwhAbiFnzyMLSQkn8KTQ4hxfvLS31dY6vg2SzS+U2qaxCqqxSdmxLB0jufSmSZ7BtTbxsr5IqHZRKGfTCw8rkeABw7j7gOnicUqsorZrSqDU9O1E6DQDILrdcWYWUCsSx1b3rXwkM+JTjqJJSey8CfipeY+aC+plvKRfK5p59p406cVzYA2V8ycsnnuhp7Dsh4rEyvuSLnE9SdKlDg8bYxcofi1fHaZK6BfT2yO6/yn+PVsfFqOJkLJ11nQ4+NgqEAi5xiZD5HIilXOIy2jT0/F237AQPABjjOXPm7N27d9myZY3eyNGjR++444709PQmDIwgfK8FJviuXbtOmDBh8uTJNeYvW7Zszpw5e/bsacQ2fZvg2S8G/9fnjFvENo+cUyndta5My6G2JjbOyBwu4St44CjAALmVIgIUoacBgJdxQaVUXbJc3Quc11v9Asa0PVfELjuk8u+L3Uer3KedfLbbk+OWRVw+JTKjhxbey6k00bsH6wRBY/jdRiXRjmBaZCi1Q3j2f3tQ90rhJaSXi6PkIiQUSZ48LJaCYLXRhgDOQjNmxBjLkHYzz3VSWdqrjIjRA6VFlApoDaJ1gBjEBABQQOsBANEaQA3oxe/Kya5TlDKmeqeyw0mpVd7TJOQUuI+elp0u2eGSHU790BvZiBAAqFz/a+Wm37Hbg3lBdntC33hGkRjrnV+1429Ko0QIURq14a6h3uVd+9LdR09TahVQCHGcpk9XWqcFACGvSMgtRColACAKKdrGeUsZvBcQAAAURalVlObct1HEAgbMIg4DPu04rmP0IYpwj+z+w/pLIBfcUd+tUrT9UPRNmCJygGVIGV/yefYH1zGankUfIqAwlihVgjJ1d43Db/EJHgAGDhz4yy+/XMkWSIInWoYWmOANBsPBgwejo2tWv7Lb7dHR0Var9RLrut3uWbNmXVwmd/r06XXr1glCAzqNb5xbV5YMiVM92U1bY/7fBfxd68r23x98rEw8UyHm2qXOIezAaOUfuZ77NloDFNQ/9wXvyPG89kelW8IIY46hOliYDRnuYfHqU1a+nZlta2E7BbFrT/33NHJQrLJPpOJYmbA83TmkjbKngTldIX55sKr4x1K9XcyMVAlqavCmUp0gKwVJwcsal6wV5N/HB+YlKtMePyOoaXgyOr0jG/X8GZmSsbkSmapYdSWnrjoxEIVphZj9+aB2FidLSOsKOVvJcm5W4aQokeYcNGBG55Qo4LCTokQMtBtrGAEDRrxHizAgAFFiK9VqiqM0VlEGKNdogeMCigQFII+IRYRlBLxH6wpV0GpKfcYtyuAO5WIDOM8pZ4Uoy/9e2ciC1hOldCspzXGXEVHGeHUWjeXjjuqm7wwFNIUqwzkEdGiuW6RwcRhnY1FQhtu7DEMBQyEtRqUWTjZQihy3hHGZmRUUyJTtBgwsgmBZkllFsSQ7wzhaISsz3AKgSgstKJClQDbTtI6lypzuShlYLElG7FZSihIWIWQPYhUePvhMpcQhJ8vyiOY4hmPpqmC2XBSMZz0Kl+BRy7IO6wStRNG8iBlJpHmPhKgyC6f08AanwsPSlSFmBlyKk+6IpJ0aXTEAUqbuolQ1xwxsDQlepVJd/NS9QUiCJ1oG33zffVrJrn///lOnTv3oo4+Cg4OrZ5aVlU2bNq1v39o7Oq3GMExwcLDT6awxPzw83Gj0xQPn8SnqbiG19AnvvRHXK6jrwrjrwv5b4IYIRaiG9tai7x2hWDDY6BSwiMHBy/FGZkOGe0IHdWGVIsXCRAcwZS65QyBbndiCNTQAmFV0BwtrUdO0ljYg6BSrgkmRANAToFMwO9Ss+HBAgFOE5GC2rZnNr5Jysj2xACf3hFBVUsdoRVoQlx9n2JXHJ2vpaCVV4pS357o9QWyOgikPtScGstEp2mwK/3agJEbPBFF0sUc6ZRXMEu2KU1sNKGm/TaOgyroojwr2ToccRiUI2O60YQojF+0uagdGoyL8VJVHwmfCRIsBhTmdQUBnV4hVCLsorGDtHKPS6hiHVGV1yRyjjg9QC7gy2ym40bnjNGscnEYpcKiIdmgVNK3VYkououzV51CnQCoG8WoFz1OBSo/IYl7FWTHGlPv8BVRA2xmGU9OUWCVKYEWcwFCU24lk0HIIKRhZwnanKHBqjYHhUVWlSy7nlKKKot2OAJbFCPEglQuSmqXkUJVLQVXl2gFDBa006bkgg0umsVOU7bysFikFRjZOWcwBxo4AhvaEKdlg2pHuEAVs52U1gxQciDIuUbMmLc1U8C5elpQiozPJBVUZebd0TFyBjSMuzu4EQRBNzqd38MXFxffff//mzZtjY2PNZjNCyGq1ZmZmDhw4cMWKFSbT5Xtcv5jfr+hLnPJN3xQfTKulo5tLVLKL+6xg011BCcbGN4OO+6zgyyHGvlFkzPVriVy5E2k6IrqWBpat4Q5+8eLF991335Vswe/fd4JoEi3wDj4oKGjjxo1nz55NT08vKCjAGAcHB6empkZFRfkyjKYVqKZqze4A0DNcwdF1dCOPgCXdBLc+lP56f4fgT1eY3QmCaBA/tIOPjo6+uBi+RXr3xjp7dhvbVhWpv6JezDQsFRvg00pwBEEQxDWEdHTjH2/0u9Ku1A89EHz5hQiCIIjWijwmJgiCIIgWiCR4giAIgmiBSIInCIIgiBaIJHiCIAiCaIFIgicIgiCIFuiar0VP03RmZma3bt0ufqu0tLSgoIBhmtExYoxFUWTZZtS8DWMsCALH1dJJnx/xPN/cQhIEISUlhaavqHHjJfA8T9Uxki9RrZl/3yVJAoCr9yG5VmIQRZGiKP9+ngVBoGna7zHExcVpNJqL3/LN993Xw8U2OYzxwYMHvZ/pGtasWfPHH3889NBDvo+qLunp6evXr586daq/A/lPYWHh+++//+677/o7kP8IgvDII48sWrTI34FcYNKkSatXr756/SKrVKrk5OSrtPEWo5l/3zdu3Gi328eOHevHGFauXKnRaIYNG+bHGBYsWJCUlHTZDsivqg8//LB///6dO3f2YwwzZsyYOnVqamrqxW/55vvejO5uGwch1KlTp1rfOnToUEFBwd133+3jkC7hl19+2bt3b7MK6eTJkwsWLGhWIbnd7scee6xZhQQAzzzzTGpqamBgLR0PEz7TzL/vBQUFJSUl/o3hyJEjRqPRvzFs27atZ8+e/o1h1apV/fr1GzFihB9jmDt3brt27bp27eqvAMgjQYIgCIJogUiCJwiCIIgWiCR4giAIgmiBSIInCIIgiBaIJHiCIAiCaIFacoJnGMa/jUEv1jxDalZdBQAARVHNqqsAr2Z4oojzNYcvV3OIgaZpv39QSQzNJIZrvh38JXg8nqqqKrPZ7O9A/iPLcnFxcUhIiL8DuUB+fn5YWJi/o7gACYloqObwfXc4HKIoBgQE+DEGm83GMEytnav4TFlZmVarVSgUfoyhuLjYZDL5N78WFBSEhIQghPwVQEtO8ARBEATRarXkR/QEQRAE0WqRBE8QBEEQLRBJ8ARBEATRApEETxAEQRAtEEnwBEEQBNECkQRPEARBEC0QSfAEQRAE0QKRBE8QBEEQLVCLTfDFxcW333670Wjs2rXrzp07/RVGWloaOs+6dev8G9uxY8dWrFhRPVlrJD4Or0ZI/j1jmZmZgwYNMhqNUVFRs2bN8s5sDmeJuDR//Tvq/4Hxgezs7DNnzvglBkmSpkyZEhUVFR4ePmfOHL/EcODAgX79+mm12rZt23711Vc+jqEZ/rRCC07w999/P8dx+/fvv/feewcPHmy32/0SRkZGxscff3z8XwMHDvRjbJIkzZgxY8eOHdVzao3El+FdHJIfzxjP8/369YuOjj58+PCSJUs++uijRYsW1bX3ZvIBI7z88u9o0AfmanM4HAMHDly6dKl30scxPP300zt37ty8efOcOXOeffbZP/74w8cxiKI4bNiwlJSUgwcPvvjiixMmTNi3b5/PYmiGP63n4JYoKyuLpunCwkLvZOfOnefPn++XSMLCwg4dOnT+HH/F9sYbb3j7wJ84ceIlIvFleBeHhP16xn7//XedTsfzvHfy5ZdfHj58uN/PEnFZ/vp31P8D44NgHnjgAYVC8dprr2GfnxCHw6HVao8cOeKdnD179sqVK30cQ2ZmJgBkZ2d7Jzt06DB37lzfxNAMf1qrtcw7+PT09NjY2ODgYO9k7969jxw54vswnE5nfn7+tGnT9Hp9QkLC/Pnz/Rjbvffeu3Xr1rvvvrt6Tq2R+DK8i0Py7xkLCQn56KOPqgeys1qtFEX5/SwRl+Wvf0f9PzBXO5I1a9YcO3Zs8ODB3kkfx7Br166AgICUlBSMMQBMnTr1zjvv9HEM0dHRcXFxc+fOraio+PHHH0+fPn399df7JoZm+NNarWUm+IKCgvMHlbJYLEVFRb4PIyMjg6KoW2+99fjx42+88cbTTz/9448/+iu2iIiIlJQUi8VSPafWSHwZ3sUh+feMJSQkpKWleV9v27Zt+fLlDz/8sN/PEnFZ/vp31P8Dc1XDyMvLe/rpp5cuXVo9cpqPYygqKrJYLJMmTTKZTIGBgdOmTZNl2ccxUBT19ddfv/3220ajcejQoS+//HLHjh19E0Mz/Gmt1jITPMa4xgh9oij6PowOHTrwPP/EE0+EhYXdeeed999//7Jly5pJbFDHWfJveM3hjLlcrueff3748OGLFi0aMmRIMzxLRA3+/XfU5wNz9faOMZ4wYcLLL78cFxd3/kxfxlBRUXHw4EGj0ZiVlbV169bFixfPnTvXxzEUFhbedtttCxYssNvtf/zxx7x58zZu3OivD0bz+dFomQk+ODjYarVWT1qt1tDQUL9EQtN09eu2bdsWFBQ0n9hqjcTv4fn3jGVkZHTr1m3fvn3//PPP6NGjobmeJeJ8fvx31PMDc/UCmDNnDkJo3Lhx3qHoeZ53Op0+jsFkMgUHB8+aNSsgICA1NXXcuHEbNmzwcQwbNmxo06bNAw88oNVqr7/++kceeWTx4sX++mA0nx+NlpngU1NTMzMzy8rKvJO7d+9OTU31fRgrVqwYP3589WRGRkZcXFwziQ3qOEv+Dc+/Z0wQhMGDBw8aNGjLli1JSUnemc3wLBE1+OvfUf8PzNWLYe/evVu3btXpdFqtdu3atW+++WZSUpKPY0hMTBQEQZIk76TBYFCr1T6Oged5WZarJzHGgiD464PRjH40rnYtPn+59dZbH374Ybvdvnz5coPBUFFR4fsY0tPTaZqePXt2Xl7eunXr9Hr93r17/RvbU089dX6V9Voj8XF454fk3zO2Zs0ak8l0/Pjx0//Kz8+va+/N4QNGVPPLv6NBHxgfGDNmjLcWve9j6NWr1+TJkwsLC7dv3x4YGPjdd9/5OIasrKz/b9d+QqJ44ziOP5ougds6i5isgrrU4Em6rKZEoFSwiFRQkpAEXaJOFQhiehDr0KXFQFMhyEN56A9o/8AgA1lwKQiKoiiKNt0acxYROkhbPR3mx/4WW3/Z/mpGnn2/TjPOzNfvzs7zfHZm1+129/f3m6Y5OTm5cePG0dFRO3tYg1OrlFLZgI/H47t379Y0LRAIRCIRp9qYnJzcunWr2+3esmXLzZs3He9t2VWYthOb21vWkoNnrKenZ9nH3+bm5pX++xq5wGBx5O34rQvGBqkBb3MPhmE0NzcXFhbquj40NORID1NTU3V1dQUFBbquDwwM2NzDGpxapZQ5Usq//pQAAADYS83v4AEAyHIEPAAACiLgAQBQEAEPAICCCHgAABREwAMAoCACHgAABRHwAAAoiIAHAEBBBDwAAAoi4AEAUBABDwCAggh4AAAURMADAKAgAh4AAAUR8AAAKIiABwBAQQQ8AAAKIuABAFAQAQ8AgIIIeAAAFETAZ69du3bl/ETX9Wg0mp+f73R3AFarpqYmOYQ3bNiwc+fOFy9eZFCns7Pz5MmTf3YGsGr+qWr4LQR89rp69aphGIZhHDt2LBgMWsvT09NFRUUDAwNOdwfgN/T29lpDOBwOLy0ttbS0ZFzqlzNAbW3t48ePM64P2+Q53QAc4/V6rYWCgoL169eXlJQkNx05csShpgBkwuPxWEO4pKSkq6urqanp48ePPp/P2iqlTCQSLpdrNaXcbvd/zwDz8/OJROL/94y/jTt4LJd8QBePxzVNO3XqVGFhYWlp6eDg4PDwcGVlpdfrPXv2rLXzmzdvgsGgpmnbtm27fPmyo40D+FcikZibm/N6vffu3fP7/dPT02lH6+3bt6urq71e7/79+xcWFkTKDCCEiEQidXV1Ho+nvr7+0aNHQoiGhoaZmZmmpqYbN26IFWaAn2vCGRJZr729fe/evcnVd+/e5eXlSSlN0xRCtLW1xWKxUCgkhGhtbV1cXLxw4UJubu7nz5+Xlpb8fn93d/enT58ePHjg8/nu3r3r3OsAslQgEDhz5oxpmqZpPn/+fPv27bquf//+3TAMl8t1+PBhwzDSjtaXL1+6XK7h4eFYLNbf35+Tk3PixInkDDA3N+fxeC5evPjhw4fTp08XFxcnEgkpZWVlZSQSkVKuvqbDJyhbEfD4RcDPzMxIKa2P4U+ePJFSWk/nZmdnx8bGSktLv3z5Yh3Y29u7Z88eJ14BkNUCgUDyni0/P7++vv7p06dSSsMwhBCvXr2SUqYdrd3d3aljv7GxMTXg+/r6duzYYW369u3b4OCgaZoyJeBXX9OGk4Cf8R08fqG4uFgIYT2yKysrE0Lk5f1z2bx9+zYej5eXlyd3Tp1oANimr6/v+PHjaTdVVFSIFUZrLBarqqpK/qW6ujr1wGg0quu6tZybm3v06NFllTOoCTsR8Micz+cLBALhcNhaff/+/devX51tCcAy69atEyuM1pGRkWfPniX3jEajfr8/uVpWVnb//n1rWUrZ09Nz6NChTZs2JXfIoCbsxI/skLlgMPj69etQKGSaZjgcrq2tffjwodNNAUgj7Wg9ePDgnTt3Ll26ND8/PzIyMjExkXpIS0vL1NTUlStXTNM8d+7c+fPni4qKrE2Li4uZ1YSdCHhkTtO0iYmJ8fFxv9/f1tbW0dHR2trqdFMA0kg7Wquqqq5fvx4KhTZv3jw2NtbV1ZV6SHl5+a1bt0KhUEVFxejo6Pj4uKZpQogDBw7s27fv2rVrGdSEnXKklE73AAAA/jDu4AEAUBABDwCAggh4AAAURMADAKAgAh4AAAUR8AAAKIiABwBAQQQ8AAAKIuABAFAQAQ8AgIIIeAAAFETAAwCgIAIeAAAFEfAAACiIgAcAQEEEPAAACiLgAQBQEAEPAICCCHgAABREwAMAoKAfH8bBc3qM2S8AAAAASUVORK5CYII=" /><!-- --></p> +<p>Using biexponential decline (DFOP) results in a slightly more random +scatter of the residuals:</p> <pre class="r"><code>plot(mixed(f_parent_mkin_const["DFOP", ]))</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> -<p>The population curve (bold line) in the above plot results from taking the mean of the individual transformed parameters, i.e. of log k1 and log k2, as well as of the logit of the g parameter of the DFOP model). Here, this procedure does not result in parameters that represent the degradation well, because in some datasets the fitted value for k2 is extremely close to zero, leading to a log k2 value that dominates the average. This is alleviated if only rate constants that pass the t-test for significant difference from zero (on the untransformed scale) are considered in the averaging:</p> +<p><img src="data:image/png;base64,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" /><!-- --></p> +<p>The population curve (bold line) in the above plot results from +taking the mean of the individual transformed parameters, i.e. of log k1 +and log k2, as well as of the logit of the g parameter of the DFOP +model). Here, this procedure does not result in parameters that +represent the degradation well, because in some datasets the fitted +value for k2 is extremely close to zero, leading to a log k2 value that +dominates the average. This is alleviated if only rate constants that +pass the t-test for significant difference from zero (on the +untransformed scale) are considered in the averaging:</p> <pre class="r"><code>plot(mixed(f_parent_mkin_const["DFOP", ]), test_log_parms = TRUE)</code></pre> -<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAIAAAD17khjAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdd1gUV9cA8DOzvbG7LB2kCIiIXUSjxhjRqMSCGktiXntM7H6aotEkxm6MNTEao8aKaGKJiu2NRqMGe6806XVZlrJsm9n5/lhfgoiIsrA4nt+zT57ZOzP3ntksnp1y7yUYhgGEEEIIsQtp7wAQQgghZHuY4BFCCCEWwgSPEEIIsRAmeIQQQoiFMMEjhBBCLIQJHiGEEGIhTPAIIYQQC2GCRwghhFgIEzxCCCHEQpjgEUIIIRbCBI8QQgixECZ4hBBCiIUwwSOEEEIshAkeIYQQYiFM8AghhBALYYJHCCGEWAgTPEIIIcRCmOARQgghFsIEjxBCCLEQJniEEEKIhTDBI4QQQiyECR4hhBBiIUzwCCGEEAthgkf1V9u2bYn/kclk3bp1u3//fs2rzcjIIAiCpunnbpmSksLj8WreYqXy9ZaF/xTZqrZWrVoRTxo0aFD1j7Rq+fn5Nqmn/nj642rQoAGU+26ULdTqdwChWoUJHtVr8+bNy87Ozs7OPnfunMFgGDRoUB00GhYWdu3aNQBQqVRr166tpVby9ZZz6UYbVrho0aLscn755RcbVs4+FT4u6//xpz33O1D2bUGovuHaOwCEquLg4ODq6goArq6us2fPjoiIyMrKcnd3r9VG8/LyzGYzAEil0nHjxtVqWzZU9lmV0el09grGJkzJX9AFJypdJQiKIsXBNan86Y+rUs/9DpR9WxCqb/AMHlWX2WwusBGGYWoSBgBcuXKlU6dOMpksJCRkx44dABATE9OlS5cRI0bI5XJvb+9vv/3WYrEkJibKZLKyfXv16rVp06bytR0/fjw0NFQikXh6es6dOxcAunTpkpaWFhERsXfv3vKXZ59uMT8/X6lUrl+/3s3NTSqVDh482Gi05Rm5bT19pM8qvHDhQvv27R0cHN54443Lly+X1XD48OHg4GCZTFZnR8px6ARgEQb/Lmyyv+zFc59AkDxSFFAHAcCTt2me/g6U/7bUTTwIVR+ewaNqoWk6ODg4MTHRJrW9//77UVFR1dmypKQkPz8fAHJychYvXhwYGNigQQONRvPOO+/Mnj173759Fy9e/OCDD7y9vQHgzJkzI0aMuH37dmJi4rBhw1xdXbt3715F5aWlpf3791+yZMmQIUNu3brVu3fvyMjI06dP+/n5RUdHt2vXLiUlxbplpS2GhIQUFRUdP378zp07GRkZb7/9dnR09IgRIwDASDNrr5a4SjjDQsTWZYoBACgwWLJK6CZOPJqBf9KN6cX0dxeLaQauZZsIAtq48gkCPKX/7mWtoZqf6qRJkyZNmlT+o6v6SBs1avR0oYeHR48ePVasWBEREbFp06Z33303MzPTWsm2bdvOnj2blZXVoUOH33//fdiwYdUM7LnogiOW4suVriKAMD36nJQ0L9uWyt1Jyjub0xZVuj3XYwrBVVan0QofV2Rk5P79+6vYvtLvQPlvS3UaRaguYYJH1UIQhIeHh0ajsUltbm5u1dxyzpw5c+bMAQAejxcaGrp3716CIA4cOODv7z9jxgwA6NOnz6hRo7Zt29a/f3+xWLx27VqJROLt7T1nzpwtW7ZUneB5PN7169eDgoIsFoubm5tYLLb+mHhapS0uXbrUYrEsW7bMycnJycmpe/fuarXauj2XIFQiUikky5YNFAMAJBBGipTzCQsDDnyCQ4B1WSkgOCShEBAAUH4v63I1LVq0aPTo0WVvxWKxVqut4kgrLTxz5kzbtm3HjBkDAF9++aWTk1NhYaG1kqVLl1qP9J133ik7UpsguErgyitdRTq+S+duJWRtCFIMAHTxPwTPiZS0qLweQkAQ/Go2WuHjEggEVW9f6Xegc+fO1WwOobqHCR5VC0mSf//9d923u2rVqqlTp1YoTE9PDwj49wptYGDgoUOHAMDDw0MikVgLg4ODU1NTK+xY4dYAl8s9fPjwgAEDOBxOYGAgST4zmz6rRQDw8vKyLvD5/6YWDgkjmkmeXi4v3Fc49c+Cj1tJK23xWXtVoYqbypUeaaWFKSkpgYGB1r1Ikvzkk08AwPq7x8fH5+kjtQlS9gYpe+NZa00WPVDFPN8vGXMelbVW0OQIKQqseaPVvAdfporvAEL1EyZ49Orx9PSMiYkpe5uUlGTNspmZmaWlpWKxGADi4uKshTRNMwxDEAQAZGdnl6/n6NGjS5cuvXz5sjV1lSW26rcIANaa67lKj7TSQk9Pz5MnT1r3Yhhm7ty5w4cPVygUAFDFD6BaxWvwleFmO4vLcCr7Z67TUJtk95dQxXcAofoJH7JDr55+/fo9fPhwzZo1BQUFx44d27Rpk/XOd2lp6ZQpU7Kyss6dOzd//vwRI0YolUqDwbBlyxaDwbBx48abN2+Wr6egoIDD4Vh3XLNmTWJiYtlj52XXpatu8VVR6ZFWWjho0KC///57586darV6+fLlq1evVqlU9g2e4Cp5ntNNiR/TBUe5Xp/Zqlrr4x3lWSyWKrav4jtQ4duCUD2BCR69epycnI4fPx4dHe3t7T19+vR169a99dZbANCkSRMPD4/WrVsPHTp0zJgxn3zyiaOj4/Lly7/44guJRLJ+/fq+ffuWr2fw4MHh4eHNmjVr1qyZyWSaOXPmyJEj1Wr1kCFDBg4c+Ntvvz23xZpwlZB9A0U1rKSaKj3Srl27Pl0oFosPHTq0YsUKHx+fqKioP/74w3r6bl9c1zFgMfEazCY4Draqc+bMmU5PSktLq2L7Z30Hnv62IFRPEDXpsIRQ/RETEzNz5szbt2/bOxBUOyxGIJ/zHBxCqDw8g0cIvQowuyP0gjDBI5bw9vYePHiwvaNACKH6Ai/RI4QQQiyEZ/AIIYQQC2GCRwghhFgIEzyq1+7evdunTx8PDw+lUhkeHh4bG/usLcsm8D5//nwVQ9awlUwmK5va3MnJafjw4UVFRc9dZTV37lyCINasWfOsypOSknr27KlUKr29vRcsWPASGyCE6h4meGQzpVduF2zbr42OMSVV1Z+4+q5duxYWFtayZctjx46dOXOmdevW77zzzsOHD21Sub3oGObnohK6XEkGRe8qscG8rlFRUdnZ2ZmZmUeOHLl58+bnn39enVUAEB0d7efnt3v37kqrNZlMb731lo+Pz+3bt7du3bpq1arNmze/0AYIIbvABI9sI2/1Fu2eo6TCAUgid+nPxcdsMHD9pEmTPv300/nz5zdv3rx58+bLli3r0qXLTz/9VPOa7UgAcMlonJlfYM3xGRQ9Ik9tk9FuHR0dXV1d3d3dw8LCRo4ceevWreqsun79emJi4oYNG2JjYysd6eXixYuFhYU//vijl5fX22+/PX78+AMHDrzQBgghu8AEj2yg9MptKjPXffEMed9wxeAIt0Wfan87SmuLa1JnampqbGzs5MmTyxdu3brVevZZ6UTmFVAUNWjQoN69exuNxsTExJ49eyoUio4dO1pn8rYXLkGsc3IsZpjP8gvSaXpUnnqkTDpU+mLzylQtIyNj3759AwYMqM6q6Ojo7t27d+vWrWHDhnv27Hl6Fzc3t1WrVpXNia7RaCoMSv/cDRBCdoGTzaDq0l+/V3rp37Hcpd06Cvy9reXa3TEEh6PZuKesXBDgU/zfc7RG+/T21XT37l2VSuXk5FS+0NHREZ4xu7mzs3P5LSmKGjZsmF6v37t3LwB079592LBh27dvv3v37gcffKBSqXr16vUyn0L1GBjmO20RDRX7oDbgcMc6SC0AriR52miMyMptyufFm83fFGjL1lbYtzGP93710n/Pnj3LlkNCQoYPH/7cVQzD7N69e/78+QAwcODAPXv2WKdDLS8wMLDsmYa//vprx44du3bteqENEEJ2gQkeVRfXVcVv+G+G5irlZeVcJ0eGpq1rreUWo4nrquIoHJ7evppomn7WRG2VTmRePsFbs3tMTEx2drZAIPjjjz+MRuPXX3/N4/G6dOkyfvz4n3/+uVYTPI8gWvB5xqfGmPDgcqxrG/K4/9UblCRpZiCYxyefXFt+X39edf9Io6KiunbtCgDFxcVz587t3r172eQ6z1oVGxubnZ3do0cPo9H47rvvfvfdd48ePfLz83u6cr1e/8033/z0009bt26NiIh4iQ0QQnUMEzyqLp6HK8+jkvmzeR6uikE9c5duEI0bynVSAoD+1kMqK1cc1oIUvvzwok2aNFGr1Xl5eeUz9759+7Zt27Z///6q53FPTk7u0KGDu7v7mjVrvvzyy6SkpPz8fG/vf3+dhIaGvnRg1cEB6CcRP2ttLk3vLNFNkMsGS8RT8gsuGY3LVEpO9fatgvVGOwC4urp+++23AQEBSUlJDRs2rGJVdHS00WgsPy36nj17vvjiiwo1JyYm9u3b193d/erVq0FBQU83/dwNEEJ1DxM8sgF+Q2/5gB5ZX3wnCPCxGE1UVq7T1JE1ye4A4Ofn17x585UrVy5atKiscOPGjXK5/LnzuHt5eW3fvv3kyZP9+vUbOnSou7t7aGjouXPnrGtTU1MpiqpJbDVRYmH+k6seLZN+IJUAwCqVcqJas7Cg8OsXvMJRNeuPnpKSkipW0TS9Z8+eLVu2lE17OmPGjN27d1dI8GazuVevXn369Fm2bFmlN9efuwFCyC4wwSPbkPV4U9yupSkxheDzBI38CAG/hhUSBLF+/fq33nqLpulhw4bRNL1p06ZTp05dvXr1xo0bZROZb9y4sfw87lZCoZAkye7du0dGRk6YMCE6Onrq1KkrVqwYPnz4gwcP3nvvvVWrVllPbeueiCS+c1S2/t/nIySIn5wcH5rNNa+5sLAwPz8fAEpKSr799ltvb+/GjRtXser06dMFBQX9+vUrq2HQoEErVqyIj48v/5vp8OHD+fn548aNe/TokbVELBa7u7vv27fP19e3devWz9qg5keEEKoRBqF67NKlS+Hh4U5OTiqVKjw8/MKFCwzDmEymYcOGyWSyhg0bLlu2bNasWUql8saNGwBAUdS5c+cCAgKsu2dnZysUiujo6OvXr3fu3Fkqlfr4+KxYscKux1QrpFJp2R+1QCAICwu7fPly1avGjh3bp0+f8pVYLBZvb+/58+eXL3y6k0Lv3r0ZhgkICJg5c2YVGyCE7Asnm0EIIYRYCG+YIYQQQiyECR4hhBBiIUzwCCGEEAthgkcIIYRYCBM8sqUcY6aOrqTvNUIIoTqGCR7ZDMVQKx/N35H+s60qfO5E5rZVNqN87TVRXuHhvLhuV8zZRpvUZt/54E+dOtW6dWuJRNK4ceOoqCibHBFCqIYwwSOb+TPvkCvfPaH0Qbzuvq3qrHoi8xcVFhZ27do1W8VWE9pDealTH4hayOJ7XbNVjrfXfPAajWbgwIHDhw+Pj4+fPHny8OHD7927Z5MjQgjVBCZ4ZBtFlPZo3v73Pce+5z58Z8YvFsZik2qrmMj8WRiGMZlMla7Ky8sz22LMuBoqPKZOm/rAf0+LBsuDVKM843peM2faIMfbaz74CxcuuLi4TJs2zcPDY+LEiT4+PhcvXqz54SCEaggTPLKN37O2d3IMdxN4tFO8KSRF5wtO2bb+ChOZX7lypVOnTjKZLCQkxDq/e05OjlKpPHHihJ+fX2xs7NKlS729vSUSyVtvvRUfHw8AXbp0SUtLi4iIsE4ga68Z4guPqVMn3Pf/rYW4jQMAuE7xdhrtGRdhmxxvVcfzwXfu3Pn06dPW5aysrPz8/JCQEFsdC0Lo5dl7KD30yriqjf0p+bv4kvtPL3+f+PWkOx+WUjpr+bn8k9PujozVnK50++orP8YqAISEhOTk5DAMk5+fr1Qqv//++5ycnIMHD0ql0jNnzmRnZ/P5/FGjRmVnZ586dUomk50/fz4tLW3AgAH9+vWzVujr62sd7NZgMPj5+c2ZMyc3N/evv/5yd3c/cuRIeno6AFAUVfPPitZRSSNvJ314K2NuQtly0oe34iKuXZOfjOt9rXx5xtyEjLkJN9xOJw27ad3M+sqYm1Dzz6rqVRaLxcfHZ9u2bQzDfP7552FhYVW3curUKQcHh5iYmKdXabXa0NBQqVS6YMGC6oeNEKo9eAaPqstH5B8q7+Am8Kyw7C1qqDHl93MdIuKIreUtHNo2l7V5qLtX6fYvxHrzODs7Oz4+vmXLlt27dweAAwcO+Pv7z5gxw8XFpU+fPqNGjdq2bRsAmEymWbNmubq6Go1Gi8WiVqudnZ137969a9euCtUeO3bMOkO8s7Nz2QzxNf+IypAijuMgN8UAV3lPp7JlxQBX5RBXriufq+SWL5e9qSw+rZF1UigGPt7M+pL3dHqhRiv9rKpeVWE++EuXLpVNGFOBXq///PPP+/Tps3nz5kqne5dIJEuWLJk8efIPP/xw4cKFF4ocIVQbcDY5VF0qvrOK7/z0cmLpwyJK68BVXNaet5bcL7nlLfLbnbmll3OklCursP0LqXQi8/T09ICAgLJtAgMDDx06ZF22TiDbs2fPZcuWLVy48P333w8NDf3666/Dw8PLV1vrM8QTII/4Nz2XX1b0cop/97o2Jk/STi6PcKKLqIR+10XNZN6rGwNRozbtNR+8VqsFAIVCER4eHh4e/vDhw507d7Zv375GB4MQqjE8g0c1RQDRRNbiamHslcJ/yl5xunut5GF6S6kNGyqbyNzT0zMpKamsPCkpycvLy7psnUY2KSnp7bffvnjxYm5ubt++fYcPH16h85t1hvis/7l48eLq1attGGoVuM78wJhWRcfyM75OsGF2r+CF5oMvu6A3ffr0p5+lt0733rNnzxMnTjyd3QFgzZo1ZTPKA4Cnp6fBYLDlwSCEXo4dbw8gVDWpVLp79261Wq1Wq5OTk0eMGOHt7W00GvPy8uRy+erVqzUazdGjR2Uy2enTp7Ozs+F/d9A3btzo5+d369YttVq9ePFiFxcXi8XCMIyvr+/x48cZhikoKHBxcVm+fHleXt7Zs2ddXV137dplw3vwz2XKMd5tE3sr8Gzq9AeMxQYVPuuzqmLVn3/+yefzCwoKyiqJjY0FgLi4uPI179u3z9HR8cGDBwn/k5mZyTDM3r17r169yjDM9evXxWLx3r17NRrNiRMn5HL54cOHbXBICKGawQSP6q8q5ji/cOHCG2+8IZVKg4ODd+zYwTBM+QRvNpsnTJjg7OwsFovfeOONf/75x7rXF198IZVK9+zZwzDM0zPE12WCZxjGlGPM/SXNJtmdset88AzDHDx4MCwszDrQzaZNm2xzSAihmsH54BFCCCEWwnvwCCGEEAthgkcIIYRYCBM8QgghxEKY4BFCCCEWwgSPEEIIsRAmeIQQQoiFMMEjhBBCLIQJHiGEEGIhTPAIIYQQC2GCRwghhFgIEzxCCCHEQpjgEUIIIRbCBI8QQgixECZ4hBBCiIUwwSOEEEIshAkeIYQQYiFM8AghhBALYYJHCCGEWAgTPEIIIcRCmOARQgghFsIEjxBCCLEQJniEEEKIhTDBI4QQQiyECR4hhBBiIUzwCCGEEAthgkcIIYRYCBM8QgghxEKY4BFCCCEWwgSPEEIIsRAmeIQQQoiFMMEjhBBCLIQJHiGEEGIhTPAIIYQQC2GCRwghhFgIEzxCCCHEQpjgEUIIIRbCBI8QQgixECZ4hBBCiIUwwSOEEEIsxLV3ADXFMMzNmzdpmrZ3IAjViEgkatKkib2jqO/w7x2xQ938vRMMw9R2G7Xq4cOHLVu2DAkJsXcgCNXIjRs3dDqdQCCwdyD1Gv69I3aom7/3V/4Mnqbphg0bXrlyxd6BIFQjYrHYYrHYO4r6Dv/eETvUzd873oNHCCGEWAgTPEIIIcRCmOARQgghFsIEjxBCCLEQJniEEEKIhTDBI4QQQiz0yneTq8BSfMmcvrjSVRzHvlzXUXUcD0IIIWQXdZ3gi4uLd+3adefOnZycHIqi3N3dW7RoMWTIEAcHB5vUTwh9LCVXeD7zCYHvv6UMZUr4iOv2iU2aQAghhOq/Or1Ef/78eU9Pz/Xr11ssluDg4GbNmpEkuXHjRh8fn0uXLtmkCYLnyvWYTmtPcuRdyl6MPo6UtOQoe9ikCYQQQqj+q9Mz+ClTpixYsGDKlCkVyrdv3z5p0iRb5Xiex0T9zQ504SmOvCsAMFSBOXOVoMlBm1SOEEIIvRLq9Aw+MTGxX79+T5dHRkYmJCTYrBmCz/f+xpz8JTAUAJjT5nOcBpGixjarHyGEEKr36jTBd+nSZebMmTk5OeUL8/PzZ8yY0blzZxs2xHHsQ/A9byfN2JEwg9Yc5nl+asPKEUIIofqvTi/Rb9iwYdSoUZ6enn5+fiqViiAIjUaTlJQUHh4eFRVlmzYsBov+AQAYVZHujz6VkCKN47vuxhTGmELwvQiek21aQQghhOq3Ok3wLi4uMTExKSkpd+/ezcrKYhjG1dW1RYsW3t7etmqCLjpnfDCIlDQvMqnFBNfZUpqrOWYsvcvo43lu43je39iqIYQQQqg+s0M3uePHj5fvJpednW3DbnIcRTgpa1+o6LVce71Bshs3vdTQTdZB5NsmcwnXfbxNmkAIIYTqP7Z1kwMgeL6LyPSl7TJadPykc4dv+zQ47CXJXGZxH0/wXGzUBEIIIVTfvUrd5IqLiymKqlBYVFTEMEz5ksumYoHBp92pa7oBncgMTpOTVzxaWA5YBMNqfgAIIYTQK6JOE3wV3eSmTp1a9b4lJSVBQUEGg6FCOUVRpaWlZW9NFuP+c5ciZ89Qrp1668bb5gSvt2dtTV81Jv4NRdrE5AZC3xofBEIIIfQKeGW6yUml0szMTM1Tdu7cyeX++zPl/uWbfSc13j7ZIUre2ylsvW7KoUSBeOaknl1/VNzfeq1WjgohhBCqf+o0wW/YsKGoqMjT0zMwMLB9+/ZvvPFGUFCQm5tbamrq5s2bbdKE5x1PWsRNC1becvrS11waKdq31vVHF15pegjRMNbfJk0ghBBC9R/busnlDcm/d+va1HGhv88kP5rFv1eiG/GpwesOR1aaf3ducQC0sFVDCCGEUH1mh+lifXx8fHx8aqlybyp7yLCt0OPXUCOnNfkopcjURzUmYBiPUVEMiXPFIoQQel2wbT54sbQtYTGnrB932y2YU/h/AI8u751ooZxC5s8VKbraOzqEXi937969dOlSSEhIWFjYhQsXoqKiSktLBwwYEBERYe/QEGK/Ok3wUVFRZ8+efdbadevW1byJ0huc3M2Dzf938k/6TeaYBIph5wCle/O7Xteb6eN8nMbWvAWEULXs3LlzxIgRPj4++fn5n3322Q8//NCxY0dPT88PPvhg5cqVo0bhFTWEaledJvhWrVodPHhw9+7dH374oZNTrQwLr7lToj0XoRjzl69jCteFgDQI4urf4P+edfFHgUjrNNazNhpFCD1t3rx5a9asmTBhQlZWVmBg4NSpUxcuXAgAvXv3njZtWtUJnmGY8+fPP90tNjk5WafT1WLQCLFInSb44ODgHTt2nD59eubMmSEhIbXRxDo/CfWmKnLp8CFTFv3KlwKAKn5ncv67N3Odj37ksr82mkQIVSYtLS0yMhIA3N3dGzdu3LXr43tkoaGhaWlpVe+r0+kWLlz49MBWarU6PT29NqJFiH3q+h48l8udPHmyXC6vpfpVInJLWyf65JtT7xz2liQnALiZEu8eX/LTB15NRXXaJxCh11xQUNCePXumTZsGACdPnpRIJNbyU6dOPff3vVQqPXr06NPlhw4dGjRokM1DRYiV7PCQ3ezZs2uv8qmhsuHKwvwHx0niy2BFxCmAvHth/YfkveNFOPXoVHvtIoQqWL58+cCBA9etW3f69Gl3d3cA0Gq1w4cPP3nyZHR0tL2jQ4j92HZSyyVBosl3dFV4TXzTaPYHgALPIJW3ozgvT8gl7B0dQq+Rrl27JiYmrly5UiaTlRWGhoaeP3++T58+dgwModcE27rJAQDP09X0KJ2h6DhBN4CbBadSjS0fCZs3tndcCL12HB0dy/eIUygUX3/9tR3jQei1wrYzeADgeblxAn0z527veJQCAHNuad4vUn6H1sxz90QIIYTYgnUJnqEZY/Lf4c3y/hBIXY0AUNAkz8LQxz8+flVfbO/gEEIIoTrCtkv0tPa/xofv98x2pRcTrnF6mAF80T35j4u60rn6jf1h8np7B4gQQgjVBbadwXMU3U0pvgUHx8p63d3tvRoAErJ99vpvIPnSnNVdMuYk2DtAhBBCqC6wLcEDwSm+Mk3Zd6Puat40Lw8AMBtL2meMK7zc15TnouhdK8PnIYQQQvUN2y7RA0CDZSPSd+ylNs/fGfoeANAmrb+Gl7bgHWJvM0l7hb2jQwghxAaUeo85bXElKwiS5/UF12lwnUdUEQsTPACIB6wmPN4ePapVFIDJXFLw44yVa5Tfv4nP0SOEELINjqydmVILGv9G8N3KChlTrvHBQI401I6BlWHdJXoAADhIOyf6fOj02SEAKNFbfhvRJiusaF3OcXvHhRBCiCUIgQ/XbRyVu5UQ+Ja9qNxtXNexhLChvaMDYGuCHymTerr9xznsMpcDBhPj1Dr1DdPhguI/43X37R0aQgghluB5TKcLz1iKL1jfWnQ3ae2fXM//s29UZdiZ4AFgXc6RFQ5fiiVcAMja3nKA50cAzM6MDQzghXqEEEK2wJHwGnxtSp4FYAFgTMmzeN5fERwHe4f1GDsT/DHt9T8476RySCFXDACNtiadWKIXcJUZhtQLBWfsHR1CCCGW4DoPAYJLqX+j1HvBouM6v2/viP7FzgSfzCj8DceGXFbKaSEAnJ12IPSwrv22nhyCJ+cq7R0dQggh1iD4vovNqfPMad/yfb+rV1m1HoViQ61MsZ0v3PFZWWpWCQGgUJp6ZvVql38etd0RkGZItnd0CCGE2IOUhnIc3iRl7UhZO3vH8gQWdpNjzDnNkiYHOys5P1zznpqfnAQDsx5FtDDC0qVNHvqYd42DKfYOESGE6rG9D/UDg0T2jlNtnsEAACAASURBVOJVwvf/0d4hVIKFZ/AEV2UxtzOlBJ7iTy/g+QJA9j/BPPcvaFKU+6CTOdNo7wARQqj+MluY2WcK7R3Fq4bgAlHvTpjrXUA1x1AFpPCquBXT9qrZ36HoNoBBnK95sEREFspbXXMfsNDeASKEEEK1jo1n8DxnnvtkUhIs8pBJzrYCgHh+e5GpgDELXXssBpKwd4AIIYRQrWPhGTwA8DxnmLN2AWRRwY3gMvDy/jHo3am7DS9pXLoNsndwCCFUz5gtzOUsE8MAAFAMwzBwPv3x3UyCgHbuAg67Tgb190oeDb/jvSpI2onN/arYmeANDy05Pw0Vjt7j2+EGXAZalsa4qVY7rIscnagV8hV9nO0dIEII1SP5esvGmzrKAgDAAEMz8PMNnXUVnwNBjjyV6JXP8IlmKp2i3hIJ9fdKEvreUA1zj/vwDvNrUJu3XewdWm1hYYLXlBp1F3czJF8o5iulNAAU6ohzgk4c5Q33SEHBXkdM8AghVJ6bhLM5wtG6bLYwzTbmbOvtaN+QbI5LwFcFhV/G6zzfu++5ICC5v+Oq1sTUUQ+Kt/Jkb7HzPJ6FCf7bk9cXNv6U8heSJYSHuAQAdAZDR8Pf3XXHzT0Vik44HD1CCL12fLjcTVpRev8bOV/7Uv0dJ6k1i9/xCIpyTBp2229LU1bm+Ff+qsvTRrdrRXNcLWKve2d/JHICASC52OX69s9IYEp9IkkJx94BIoQQqmt0EaV/96b71/5zu/E/ytMsdlS8JRJKOyj8NockDr5pfKS3d4C2x8IEv/mW/kfNCr456a925LWM0QBgTiYDW8eUcoQpkjn2jg4hhOo1kiAaq1h4cZcj5cg6K0v35TAGhk9AoYUBALAwmt3ZktYOPDeBvQO0PRYm+NXdFLN79OLKQkc6fnawXwgAGCzF7iGXf3GcelKKU8khhFBVOAQcGOhk7yhqAUlo1wVellhWTcvcLnNcWVh0qLg0Zfx9Y5Le//cW5Kv/FOHTWHhIVtzAX2V0wSDTUQDQkcWMwNvM0XYQsvA3GkIIoed6YDZPKiho9ktTZ1ch85+7v4gVmRPu5yTqAva3ZOutW/YmeL473/k/E323AkCJjhY12jKn4bfdREJ7x4UQQsgO5CS53lnVQSL0Wd+Eo+Dp21zqkAfyvc3Ymt2BxQkeAPi+i8RiHocEnZ4xapz1f1+xd0QIIYTsw53DacbnAQDBJXw3hbh/2TBkX8sgOZvP+tic4IEUPpJ+IxUTAKDNyPwtZ8eNokv2jgkhhJCdEVzCaYwnKWbtubsVqxM8wKx7kWKxIwDoBDxxnkltyrV3RAghhFBdYHmCLzQyApEMAAoYqv0/kq6Sbj+nLr9VdNXecSGEEEK1i+UJ3kVEKJUKAMguLOS5OV/NOH254J9Nqaspxmzv0BBCCKFaxMLRDM6mGYtNj/u7G2kAsQIA/ozPpYeM2Fs4x0PsrKXUh3N+i3T7wK5hIsRyDMP8+uuvBw4cIEny448/7tWrl7U8Kytr3Lhxhw4dsm94qPpMieMZU3YlK0iBoNE2IPgvV22xxfKf3PwfnBwbcB/fC48p1f+pN6xUsXDUWLtgYYI/8cigNVqsyzmllkKuAgCupuanu6sJhdOwRkOuaP85mnfgbVUvOQ+/RgjVloULF65evfqTTz7RaDRDhw7dvHnzwIEDAaC0tPTw4cP2jg69CIIHBIfrPql8Ga3+jTGmv3R2BwAZSX4ok4zIVW9xUXlzuUdL9Uu1RRud2TbJjR3VdYIvLi7etWvXnTt3cnJyKIpyd3dv0aLFkCFDHBwcbNXE/M7ysuXOO3K5KpcUgJayArPvglB5h+ayNgHixpe157dlrJ/sO8tWjSKEKtiwYcNvv/3WpUsXABg4cGDfvn2bNm0aFBRkx5AYQ4JFd6fSVaRDB4LH2mlDa4jX4CvDrQ6kwIsQBlhLGEprSvhYELyvhjW/JxEDwMjc/FEyyS/Fuo3Ojo14vJqGi/6nThP8+fPne/XqFRAQ0KFDh+DgYADQaDQbN278/PPPjx8/HhYWZvMWSYLgSuQAkJJ0zpORRVz0NjcwiTmSAe7Dfs/c9qg03k8caPNGEUIAUFxcHBj4+O+ra9eu48ePnzhx4okTJ6qzr06n+/jjj00mU4XyzMxMiqJeOiSq4Kg5fSlH0f2JUoamNYcFjfdwFN1eumZ2I3jOXPfJppSvBEG7rCXm9CUcxz6kOKTmlb8nEd8xmZdoi36yaXY3WgzFVKET39VWFb6K6jTBT5kyZcGCBVOmTKlQvn379kmTJl26ZPtO6gTB8MRyACjKezRe9nXuldhNLc5/3Wh5uOrd47l//JK2cmHQWgIIm7eLEGrbtu38+fN/+OEHHo8HAPPmzWvTps1nn302bty45+4rEonee+89s7niw7BXr16tyT8UPNePqOyNPNexpEPHskIqZxNQWszuVeO5f6LP3U5rT3IU4RZ9HJ2/T9gi1iY1Hy3Vn9IbJshl32oKt7hwvbm2yUq7M7fcKb62sPGPvBrcRHjV1WmCT0xM7Nev39PlkZGRU6dOrY0WXSWcPJkcAEpoQ0zJYeY9vVlPTrrzAQBQDG0yG+NK7gZJm9ZG0wi95tatW9elS5eoqKhVq1aNHj1aJBIdPHgwIiJiy5Ytz92XJMnIyMiny4VC4apVq14+JlLI955rSpklbPoXEBwAYCitOf27ml9qZj+Cz/eZb075kiM/Z075kuc5g+Cqal7r0VL9Ym2R9cq8K4czMjffej++htWm6ZOvFV7wETU8nvtHb9dBNY/zFVWn3eS6dOkyc+bMnJyc8oX5+fkzZszo3LlzbbS49h1lv2auAGDSisVcicDEk5MOIo5ExJHIuA4qvrOQI6qNdhFC/v7+iYmJMTExb775prUkICDgzp07GzdunDt3rr2i4qj6A8eBytthfWvDS82sx1H2JPhexriRjDGN6zq65hUWWyzLtEWbnFXWK/PvScTjHKSLtEU1r3l31q+Rbu+PaDDhRN7BfFNezSt8RdXpGfyGDRtGjRrl6enp5+enUqkIgtBoNElJSeHh4VFRUbXRokpENm2gAgBuAb0s+Jfc7zcKurQShoQISDaPP4xQPcHn88uye1lJ//79+/fvb6+QAIDvs9j44D2OYyRjzrHhpebXAc93keFWJ0FQNBA2uFkuI8lTHk/cIx8qlQyVSmpY7ZXCfwrNBZ0du5EEp4tTz/3ZO8d6T3t6MwbATDN8Dptv0dZpgndxcYmJiUlJSbl7925WVhbDMK6uri1atPD29n7uviUlJYMGDXr6+Rq1Wl3FQzdZJfT1QjEA6IqLAUA1dsg58/m0rK0fen5cs0NBCL2qSEkzjrIXlfG9RX/fVpeaXxOkKEjU8hohaGDvQJ7JzJj2ZG4d4TWeJDgA0NvlvdkPJ8Xr7gdKgitseSrFcDjBsDJcYY8w60hdd5O7du3axYsXO3bsGBERERMTs2XLFrPZPGTIkPfff7/qHSUSyezZsw0GQ4Xy2NjYe/fuPWuvR4XUuXwBAJToSgGAo5A1MjTVaDU1Pg6E0CuM12C24WY7gudsk0vNr5X6nN0B4FjuAV+Rf4ispfUtnxT0dxu2K2PjV42+r/A8tYkGM83YI8a6U6cJfseOHSNHjmzSpMmMGTPmzJmzbt26jz/+mGGYadOmabXa8ePHV7EvQRCdOnV6ulyv1xNE5ddY6MJTLZlr81vqDgCU6nVU5nIAcAboTQjAUgqk2CYHhRB65RA8Z37DVQTP3SaXmlE9oTVrjuTu6+s65LL2fFkhj+Dlm/NiC053UL5tx9jsok4T/Pz58zdv3jx8+PCYmJjevXufP3++Q4cOANCxY8cJEyZUneBfgilpGmFM9wUpSUJxKWVIXU2SwFAGgjTTwiChsvvzq0AIsRTHsa+9Q0A2VkwXNXdok6xPSNYnlC9vLG1GM7S9orKjOk3wGRkZHTt2BADrf1u0aGEtb968eUZGhs2b4/t+Z3w47ITwD6mwU1FpKR18WyqXF/7RyORB36bNXW3eHkIIIftpIPQd7/NZFRtE3ytdf6PEulxKMXoT0yXq8RzijZS8Db3YNnh5nSb41q1br1ixYubMmWvXriUIIjo6esyYMQAQHR3dvHlz27fH0AzXub1ukELBLSoF9cMVIictz1Wt4/VQkSwchB8hhFAVBjYWdfQSWJfPpBlPJRu+ffPx0OYOAhY+Tl+neW7NmjXvvvvuTz/95O/vf/HixcjIyC1btlgsllu3bh09etTmzdHF5xmClJMahQxSAQo0uR7EIcYsk8MZd9lqmzeHEEKoPuORRAOHxzPXqUSkmPfvW1aq0wTfsmXLlJSUtLQ0Hx8fkiSvXr16+PBhk8kUFRXl4+Nj8+Z4np9TeXsv6DrKJf8FAK2OwyiNDN2c0TdKNhf6cFXWfhQIIYQQ+9T1lWoul+vn52dddnNzGzt2bO21RXAVHM9PedrfFTIAAHXSTm7z/nTxVWGTAz9lLOnrOiRYWgv3BRBCCNV7BMAzOmCxR50OVVv3BG6jQ5QlSpUvAGiLORZ9HN93ASGUdXPq7cBl8/gGCCGEyiu2WLLpf5+lf7OBYEwbSS7N5qfrWZ7ggeDIAxc7CDUAkEUFEFxHjmMfAGgjf8NT+Pzh8xBCCNUN7aG89C/iGBsNPkMx5iJKW77koZkanKNOMD8e+dRAMnNM2rMGo02aq5/YnuABNie1kTu6A4BJ/ZDnuwgA8pZvojWFN4psPzstQohljPcjSy8oK3upGFOWvaNjD+3B3LSpD0qvFad8fM8mOf63rG1LEr6kmH8HMg8V8GcpHEbn5ceZzfm0ZVSeupdYNFDC5hHP2N9bbPtd3TsefQFuZZeoSHFTALDoSk0pGVHCje4CL1eBh70DRAjVX1yXEQylue21YH/Obh1dvCDoRxnXwZy5ylJ8meC72zs6ltAezE2d8jBgXwthsDRx8M3kUXd9N4cQ3Je/Q55lTL9QcMZT6HNSHdPD+d85ynuJRQAwOk8jIaCvRDzRQVaTsBljuqXkSqWrSGmb+jCmL/vP4FeGKzzdAwAgq8jJWsL1cDVn5g5yHyHlOtg1NITsia6SvaOrLziq/gxHlpT67cgGE95QdtmXvYMx51JZa/k+39o7NJYoy+7i1g6kiPTf04IqMCePvstQL38eH52xubfLoJFeE2Jy9xZSBeVXhQkEPGDyLUx3UU3nFKWL/zEmjKPyD1CaP8q/jAkf00Vna1i5TbA/wTdx4jnI5QBQrH08gBHPw9WcmdNW0VHCkdo1NITsiVsle0dXj8RK3+5mfBgo8OrnOvRm0ZWCR59xnT8khAH2josNik5q0qY9DPijpbi1Q8FvOSkT7xNcwprj06Y9eLk6bxZdUZtyuzpFuAjcOyrfPpC9q2yV9cr8e1LJfKV8bJ4mzmyuSfBcp/dIcVOuqp8g8NeyF1fVnxQ25DoNrknNtsL+P+NJJwqkahEAmIuKrSV8Py/D3XgGmDP5x7uoeto1OoTsJj09/Vmr1Gp1XUZSnxWY8w8U3wp17G3OWCbyWTBU0Y5Om8cJfeYMlnXHYin5+7LxfiIh4IvbtxQ2eSV/cJASkrGApYTW7M7OmJMgaiJ5NOKO9w+NLcUUR/4y6Ylm6N2Zv77vOYZDcACgn9vQLx9MTC5N8BUH5NOWEXnqXmKR9co8DTA2T/Ors8qf99J5kOT7LjbGjxEpewIpAgCwGE2p3/L9vgeiXuTWehFErYoroBrzZACgK9ZZS4TB/sJgfwtD78nc2k7RWcRh80MWCD2Lp6dn2fKNGzfy8/OtyzqdbuTIkRoNzqoMALAnc0u4KkKmCjfc6mBxGdG84I8j4tYNiu+0VXS0Z1gMk7N4PUPR0k6hFr1evXaHrMeb8r7h9gzppUjbKxpGNUt87ybBIxodayPwFyUNu323Zawy0sVzQeBLVPin+rCLwL2ZrLX1rZAU9XMduitz08yARRk0NVgiGS6TWFf1Fot4AEkUVYMED6SsHZBNi/+cSmv6CpsFcRQHSXFTjry+TFvH/gRvpJl8kAOAplRfvpwkOON9PxOQAjvFhVB98c033yxZskQkEnG5XKVSmZiYOGvWLHsHVS/E6+4/KLkz2GNkKSkg3MYbHvQHjsrXZ0l05q8tHNrySb69AtPFXmeMJrdvp1rHapF0Cs2cvkjaOYyjqNFTY3ZhzjISPAIYMOeZeJ4CKtfIlXPMWUbGZCEEL3YTuYgqPJSzZ4DbsHvFN8sKVXznHGPmFe0/bRUdm/Of+F/WQyyqYfBFh0+V/OWuGLDXouus2bpJ0XenKPRMDeu0IfYneJoGo1ABAAUFhvTPHnotCypbVfYrD6HX2S+//BITE+Pi4jJv3rzff/99zZo1JpPJ3kHVC9eLLlGM+euHUwGAA5ZPqNLDpE9K2loASNbHN5KE2CswY0KKuG3zspHYOAoHvr+36VGaqFUTe4X0cgqPqtO/iG90oo052/Ro+B2eG1/aSem1MCDpw9spE+/7bnyxT1hjVvuKA64VXaxQ7iXyLXyyT7xNUHmawgN/enz/raXYmev6tyiEKDnXhFQxwmCbN/WS2JngZ50uPP7IYF0uMlpEObSA5BnN5uQNSdeyzX23N9Vs2y8I9OW0a/yX+lgvl/72jRYh+9Jqtc2bN3dycoqPjweAsWPHNm/e/NNPP7V3XPY32H3EYPcR5Usa2yuUJ5FikUVXWr7EoislJTU9H617PFc+QzGmVINDd5Xftqa6WK3b536UxmxKMyhaOb9obb4i/08bVqt3A5VvTv8izmNOQ77vy39oxvhkYUgAR+HAcZimvxEGYCFF84z3E4XB/i9dp22x8yn6uW86/PWBs/XVxsJ8typJzpECAOf/lA1iclOmPLjj5mRKTCWBPJiz28JgjyD0WgsODo6OjiZJ0mw2p6am5ubm5uXl2TsoVBVxaNPiU7HmrMf/m3T/XKOLSvh+XvaN6iWIWzv4726ePPZu4RG1rLPS7Qs/Kt8U/+41h24q9y8b1lKjVL45vvc1SmOOi7hmfKR//g7PQPB5jMkMAECK+f4/8Bv+YDGSBJ9ns0BrjJ1n8Ifi9ddzzQAgzjVNnhtnEhK0hxOkFPzc0DJqqg+9KiVTK2scks0nBbMCFuOccug1t2jRov79+/fo0WP06NGhoaEcDqdfv37P3w3VWImJkfJfZjgXvq+Xcljf7DkreB4uFr2RMZldZowlePUotVSfpJ3cf0+LxME3fdY1kYQ5xPe+7tBN5bmgtjoF0FoqIfK6Q7jKc0GAektm/LvXAmNaC/xe5jxeEOSXv36XMTFV4O/Nkb9N5ebr/lnh9tUkm8f80tiZ4L1kXMoCAOA/7GYpCQu/DoCfHCEFFET+trFBbTim9t9lFmYSLl+At8jP3sEiZGc9evRQq9U8Hm/69OlNmjQpKCgYPLhe9OJlvUn/LZgaKm3l+jIP60nfaicOa2FKySD5fJ6PJ8F5hS/HPs7xQ25xFFxlfxePb2rrEjetpeL7XJN1cfScHwAATiM9AOClczxHJnUa/0Hu4vUCf2/gco33ExTD+vIa1KPxDdmZ4Nt78tt78gHgniOPSjPwNXRbudMxgKumxGB+h16ZoCUJjgMDAAXm/JPqI++5/8feISNkN5s2bapQsm3btjFjxtglmNeKiWZMNbhDSIqEwsb15XZvDUnayf33NNddLXIZX1sjvFpK6bieV+URTh5f//uhOY30AJqJf/da0Om2PJcX/qUlatPUc/Ucw4MkoCjVR4M5ivo1Oio7E7wVXXQ24FD8w/mJq26dmCfKBwCfrP9O2FHCaHWeC0e4TOwKAHxScEZzIk53d6rfHBzYDr2efv/9d+sCwzDZ2dk3b97s27cvJnhUxyRhckmYvIoNGLqEzt8LUMkQtoTA5/m9z0mCEJCWEhoYgHI3RmgdTXAJgvOSQ9+TErG4TdOX27e2sTrBq/fShSfTB3UMulYoPlgAAAHxSW4tKcnkG/N9h30HAAASjjRY2jy+5N4f2bs+8PzIvgEjZBdHjx4t//aXX37ZsGGDvYJB6JkspabkWRx5Z4L3xGVwuvAkR/7WcxM8KSQDD7VK6Hc9ddoD71WNrTk+98fUvJ/TGh1pw1W9kk8wVO0Vvm1ThTt55iOJhr/pGSazfp9u7PCuy83xbwJATqxzSUOX7fIRUmEDYBjGTCXrE+N192YHLr2kPZ9uSLF34AjZ34cffnj9+nV7R8FakXvVrX/Nsb6uZJvGHS2wLrf5NedIosHe0dVzJCkKtpTGPVFGlzCmPEJYrWHvOA7cgD9a6W8Vp057AAzk/piauy6t0ZE2fJ+aTjxTP7HzDP5ylulajglAWsgfM9hh6XT9mnsfNoJVkOmlEbhnt/T556GFo78dV3T87139k9op3vwqbnIv5wHRGZs+9Z9n79gRsieGYXbt2qVSqewdCGtF9VWZLY8vMn90VPNJK2kbt8e3fqV8dp5x2QzBY4yPGIYiuFJC+Pg+OpXzK8GRgbm6sydwHLgBB1rF970e1+OqOdsYdCKU58na8UyrleDj4+P37Nkze/bs2o7GVkY1l4wCCQAAMyMltn2EOja0g+/gVUARqTmeP3SQOHUAMDspL/PvmC3cTEMabaGzjBmFlPZG0eWWDm3tHT5CdUom+3d8U5qmDQbDihUr7BgPu4l5RNkdYC5JSHik/AUHZH1tEVw5z+tLKmczXfi3sMFcIDh00VnGlAcEcD1nVL8ejpwbeLDVvZlx8V/6h7A3u0PVl+gfPXq0dOnS1q1bN2rU6FW9J0fwjxu/GK1YlCEVA0BhoflT4p2HZjMAMC6yk20y2ju8mWvMWtbklwTd/c6qd6IzNlFMjSYQROiVc6Oc27dvq9XqadOm2TsohCrBdR0FBAkWI5W3ExjanDwLeHJeg9kEt6qn857GkXPTP2t4yvTyU86/Eio5g09NTf3tt9/27Nlz6dIlAJg+ffpPP/3Url27Oo/NNh4yPRsT2zNNxwAgr5C7RqH4KE/zi7Pj3fx9DfPkf8uPD/UZI+cqB7gNO5F3yEPY4M+8wz1x8Fr0Gvi///u/KtauXLmyziJ5bRFE2XDyqHoIDs93kSlhvDltAUNpgKEJksd1/tDeYdVTTyT4NWvW7N69OzY2tmHDhgMHDly2bNlbb721fPlyewVnE01dOD8JP5unHbAMoFBr7iEWWQAWFhTK1DEmPwNYYFPqagtjMTMmC1gyDKnJ+kRM8Oh1IBQ+frCooKBgw4YNXbp0adasWUpKyvHjx+fMmWPf2OqhtclLWzq07ejY1YZ1zung4K9g54NQtqLWWxgGnMX/XmzmOHQmpa0ZY6Y5bQHBcxb4bQQcjfQZnvhuTZ061c/P77///W94eDgAGI1GO0VlS6OaSkfBWyXCVQDDtUVmAOglFvUSizTKH76Nmz7Zb5a7wKuU1q1PWTbIY8T6lOVfB35v75ARqguLFy+2LgwYMOCHH36YOHGi9e2WLVsOHjxov7jqgkVHM2aGU+3kerPoSrI+IUH3oJW8nZgjsVUYwWzsmmVb0fdKLQxMCX1ikBK+z3zD7XDSoRPBVZIOHatf2z21+Wza47z2UEMlaamfr5dY3zZw4Eb4s+1Z+ifuwX/zzTd8Pr9nz54RERHbt28vLCy0V1g2ZLYwP10rkXr9RywSGU2WYp3OWh6T81tLh7buAi8AEHMk0xvObSD0ayRu8qc6xq7xIlTXTp06NWDAgLK3ERERp06dsmM8tY3WUnE9rz7ofMmcUa1zGIqhdmduHu41vqW87aGcPbUdHirPwgDz1Mg2hMCX6zrCUnyR712tuePK6MxMoenxq5RizBYoe6vWs3DWsSd+wM6dO3fu3LnXr1+Pjo7+6quvPvroIwDYsmVLZGSkQqGwU4Q1ZaDgl5u6Ca2ljjLHUn1Gam52iJ+/hbHcLLpitBiuaWPLboIxwBBAZBhTceRa9Fpp0KDB4cOHrX/vAHD48GFvb++aVxsVFXX27NlnrV23bl3VuxcVFdF0xX9zS0pKahgVXUjF97su7ajkewkevnOlOn2g/8w75CbwbCZr7SsKmP1wUifHcE+hDT4fVGyxjMjL/1Hl6MF9fI39WKn+hN6wQqV87r48z8848nBC8GL/I9q689u6P+6UeDTJEJOg/7ydrOpdXmmVXKFq1apVq1atlixZEhsbGx0dPXPmzI8//rhHjx6v6FU7GZ/Y0FMJAEqRMh0ySnIfgZ8/SZDfN9kIAGkfzfZYPovjIE3UPdybveNz//n2jhehurZ48eKBAwceOXIkKCjo4cOHR44c2bt3b82rbdWq1cGDB3fv3v3hhx86OTm90L4lJSWNGzc2GCoO/GI2m83ml+/nQhdS8X2vS99QeC15PC5KXMTVqnN8EVV4NG//rIAlACDjOrzrMjA6c/OMhnNfOob6jwHYels3spnN7kQ8i4wk35dK/pOn3uLs1IDLOV6qX6Qt2ujsaKIZPfX4rN1AMUAwhUaL9a2IS/CtY8qSohe6OP96eiLBBwYGxsbGWv8UCYLo0KFDhw4dVq5cefr06V27dtkpQhuw/mRTKRwhBQrzHpVfxXN3MadkcJoFeYl8ZNz6NU8AQnWjd+/eN2/e3LlzZ0ZGRvPmzZcuXdqoUaOaVxscHLxjx47Tp0/PnDkzJCTkhfaVSqWZmZlPlx86dGjQoEEvF8/T2d1lkjcQRNU5fm/W9o7Krm4CD+vbbk69z+SfuF18rZms9cuFUf9RFmZJbHEdJHgAGCQRA8DIPPVoqfTn4pKNzo6NeLx554sOxD2ept1IMQwB0fcevx0QJJrTAf+hrq4nEnxCQsLT18Q4HE54eLj1sbtX1LrrJWOaS1ROTgCQmvPEeLR8Xw9TSoawWZCAFI73+cxOASJkZ40bN54/3/aXr7hc7uTJk+XyF+ujbFuaXdnSDgq+jzBr8SOChLLsbuUysUHJ+YKMrxL8tlUyX0iqPulW0dVFjdeWlXAIzlCPUTszflkQ9AOXnsic7wAAIABJREFUwAfgbWCQRHzXZF5UWLjOSdWIxwOArzs6fN3xcRZfc6WEAWZqqO0vpPs4cFq7vcxEvdVBMeZSWufAtfOt7dfiC7rzbmlkoMjJxQkA7mWllV/F8/YwPnx8Tk8xVIE535nvaocQEapzPB5v7dq148aN4/EqeZa7JlfCy7PvCJjZ3yfnrU8n+ESjI63dZ/rF97mWMSfBc0FA2QbqzRmlN4oDYyo/Hf9b82chVTDxzgdPr4rX3QuWNq+tuF8nx0v1p/SG8Q6ybwu01mv1ddNuEydeE6fa6sWwN2vHzaLL84LW2PdXYMW2R44cWdY7toL9+/fXfjy1QswlSinGxcMFADR5WeVXiZoGMQaTdTndkLI17advGr3a/f4RqqaEhATrmPMJCQn2jqVW5KxOzd+R1fhs28Jj6riIa4Exrf13JhceW6jdzxc1lQKA8ZHeUqrzj1ICxbEUf0zK2leo4UPPcR96jrNH7HXNSDOnUowMAwBAM4yFgbKZb0gCwn0FPLJWRuQ5XqpfqC2yXpl35XBG/u9+fG20VWeyjRn/FPzlKfQ+qY7p4dzPjpFUTPB+fn4SSV3ceqlLIh6hNzOu3m4AUKh+Yk4CrpuTQ+/Hkww2EPoGSV/sTiFCry4fH58KCwBA0zRJksQrO74aA3CoVN9bLMpbnar+NaPR0dZGYC4PVLYFiH/3WsA+qbjl1bytk6hcJUfBLTyidp/VkKPQmlK/4jd4rcf20RosRxMN1g5pFmAYBo4mPb7tLeAQ7Tz4SqHtvxLFFsvS/2V3ABgkEVMMs1BbuN7JsWwbHgcszCv2bYzO3BzhMrCVQ9iChC/aKzvLuc/vFFBLKib4b775xtWVbdeoJTyilGJc3F0AoFCtfdZmHIIz1GN0HcaFUL0QFxf3+eef79q16/z58yNGjKBp+sCBA+3bVzydrf8yS2g3Kef3El3hipQ2ewsaHW2tyzDc73udetNBtbMVQzEJA1L8d7Z3/z9x4oct6SKq0dE2fB+hKWkyz+2TstnJXk+uEs6a7o9vGJstTLNHOT90r/W0JCPJUx5PpJv3pZL3pU+cYY5u/oqdcN4tvpFtzJzkO5NL8Doq3z6QvWuE1wR7BfNazGI0u4NDM2ees7MzABSpiyqspXLUtPZxoclijNPdq+v4ELKrsWPHcrlcHo83b968GTNmTJs27dNPP7V3UC+j929qA8VscFYpTmnvtZPoso13B924uMQnGDiPRtyRdXG0lFrMObNo7ZrAow0anw3j+wgtult0wX+5ntPtHTuqnIBDCDivzBm8haGjMzd/4DGWS/AAoJ/b0JtFV5JL7XYL7IkE361bNz6/tp4qtKMQJ56IS1gTvFZTWmFtyZlLxcceD8ehpQo2pOBEmej1cuXKldWrV9M0ffXq1Y8//njs2LE3btywd1Avg2YYiwWEBNF1fxv5peIH71y5uMh78nA//+3N6GLqQadL3iuPcxXdGCrf9DDEFO9SekFpuP0WY87RX/alNa/kOB+oXvlTHSPnKZs7tLG+FZKifq5DozI3Pj0YX914IsHv2LHDZDLlPINd4rOJE48M99Rma4IvKDBVWMv39jClpFuXXfhuPV0i6zo+hOxKpVJZ55hp1qyZRCLJzMx81R/EoZL0Yg1V6MrzvFBCAJizTcZEvcBPVHwqjOAqBCEHCZ6zIGgnz2cRKW4sCNpJcFWkw5v2jrq+4BBE2XBvqPpKqKJDOXtaOoTdK75Z9nLiu+Qasy5rz9slpCfuwbu5uVWxKcO8qlPn/pNhDFDy3m+sIklSW0zTNMXh/HvgfD8v05Z/x+3q5tTbHjEiZDcTJ07s2bOnxWL5/vvvHzx4EBkZ+dKDydjd2KOaH3sobo28nfof54HTA89HXN4/9mbQ+RK3Gb6O77tlzp3IUMXG+8MIkm+MGw1AEzxnY9wYYIyWwr84qgHPb+AF6S4WFp8tcPvU1+Y11x6SgO19HJ+/XZ1jDImGO92hsrNhUv62IHBz3YdUntqc5yP2v1F06QZcKl/uJfItpArsEtITCT4sLOzmzZvh4eEDBw585513RCKRzdsrLi7etWvXnTt3cnJyKIpyd3dv0aLFkCFDHBxqcXCi0c2lIi7B4ZAKiVxTXJCfm+zi/m9HWK6LylJqsBTrSJkEAEwW42XtedtOColQfTZz5sx27doZjcaePXsmJSXNnj175MiR9g6qWrQGS4ftuUb633/xL2eZOt7LaTTFbfqc9Mww5zePtP3vkOtXJriOGOWRPPYuQw0muH8CQQiCok1J0whRI46ypzl9CQDDUb5r8/BKLmiTht7mOvOoPJPXUhsMDviaI4QNCVEQ17E3x3lYuWLaeLcXV2X/K6++Iv9PG77Y5De17YlL9BcvXoyPj+/Ro8fWrVubNm06ceLEU6dOCYVClUpl7S9bQ+fPn/f09Fy/fr3FYgkODm7WrBlJkhs3bvTx8bl06dLz939Z3g4c63TCKpEKANKz4p5YTRDK4ZHAe/xbh2boqMyNFEPVXjwI1Td6vX79+vV9+vQBAIlEwuG8Gh2RFULy3kduiZ+4W19SPuEs4bTx4G8b5Re2v6Vp6gPLjeKmMa0vDlb+P3vnHd5U1Qbw986spkmTNm26dymUvYeALAFRCqggIiBDQJAhDpAhU8QPlSEIBZnKcLE3iiIgIKtQ2tK9Z0abNPuO74+W0pZ00bShmN+T5yHn3DPe0Ny895zzjrQpD+hiyn97X9xzFkLKzemfclocJP0+t2SuQIAm/dYAyrGtbLprxSmj7/ltDQ/7vZPuenHWJwl2Ood9nkBI/7WWnI2AoAguLn3RyqMI4YFJXrW3bM8kbDXk5+dv27Zt0KBBzs7OkZGR+/btq65l3enQocOGDRuerN+7d2/nzp2fbsxjx45xOJya29zJN/+eZmRZtntIVwD48dhjGRizufjohfwvthV8vVP3z53SyujimwzLPJ08Dhw8HTweT6/X22XqvXv3yuXyFStWeHh4pKenu7u7r1+/3i6S1EoN93uGhmoRldttT/7SgqJReQXFNF3yT9GdgEvvHUy8OO5O4og7jJFmWZZlTPo7HfV3OlKK38wZq433+hpjI20uZ8k/RdG+fxWdLiwtUsWW+D43Mj9+6PhdaTim5NnmtCWl7xmLWn8zlNbdt69IT0HT3O/VusnJZLJ333337Nmzp0+fTkxMfPttG2RQTU5OHj7cSlifyMjIRo2lFauwXEw3AoCr1A0A8h6Fo2dpOm/ZJlNSulO/bvxOEUW/nlH/cBQA2jh3RKDZOGY4cNBAtmzZcuDAgSVLlgCAr6/vzz//vH79ensLVVcMFPvuafX4E6r3zxWZabbIxDy8SqmymBeTCibpLRvXe70zO9PHhAQdbINwUAAAhCR9lyGs2Zy+iMr/njFlEn6f21Yk/S1N8ph7/jsjRIPLcuhhznjw0fYl/xRnL30+gwY2JYTPYkpxgDEkAoAlay0mGYbyreQRcAA1+MHHxMQsW7YsIiJi1KhRffr0+f333xs+Wd++fRcsWFDFIF+pVM6fP793794NH786eDhSmnxQJnMDAEV+WTh63ZXbKI/j9sEkfqfWAOCxbE7Jn9cohZoF9kDO940njwMHzxRxcXEtW7YsL4aHhyuVSjvKUy+4ODK5rWBaO8ErwVwA4GLIuFb8le7irnzyQYi5Ux+3Nre6B/7Q+luDLtlSdu6GSYax3JA81I3lBmCur6P8cNuKRBVRCAqErJIhOspDCRlpyTHZdq7/IAjhhnvOtmQsYQwJtPIXwudTe0v07FJVwd+9e3fx4sVhYWGDBw9Wq9Xfffdddnb25s2b+/WzgdFZVFSURqPx8vIKCQnp1q1b9+7dw8LCPDw8MjIydu5sRANIPoHoLSwAlAazUxWUPWGYUzN57cIBAFhWsfUAoAgnxN+cloUAcrv4msJc0HgiOXDw7NC1a9eNGzeWZ5fZu3dvx44d7StS3UEAunqSPb05EW4EAHAwpLOcDJTjSZilG5dz3mDUe5IIifoT+KRCZamON7DscuF8Z0smYsokvD+xuUjO/SU+34QlvnJHf7ssghZrZlLevo+QqN/WljX3dVAXCI9pjCHJ9HAs4fkBgtvAPux5pZIVfVBQUFZW1oABAxYsWNCtWzcURQEgMTGx9GpYWFgDJ5PJZCdPnkxPT3/w4EFubi7Lsu7u7m3btvX19a21r8Vi+fbbb83mql7scXFxDMPU3JePIwaKZU0ZMjkFANrCbEZ3FwBw10JGn8EYfFBeCOnnaU7NotUaTOwMAB8GLpcQrk/5OR04aFZs3rx54MCBO3fuVKvVvXr1SkxMPHfunL2FqhNxSsvxpLKcKClFFADoKHbd/ZK/PPTdzNyNoS7fFGveKVDukklf4fNwgEmFys2uknVFGm9+S6HHOyg3GGmchJ7i4TIASBoZHfxbW16EU8rb9xEMDdgTgRB1OPujdeaMpcBa+VlDOF6EV7MMMmhjEJL0X2PJWIF7TLG3KM80lRR8SkoKAJw6derUqVNPNmVt4Qd/+fLlnJycN954g2GYLVu2bN68mc/njxs3buTIWjxQGYZRqVRPprAsKSmpVbBOclLKQ03xgyVkLgAYFLnmlHkAQMgtlDLLeG8tv3MaGeirPfUnYzSR/t4A4M7xfPoP6cBB88FkMmVnZ8fHx58+fTo1NVUulw8ZMsS+GdzrDoEiIrJMZZY+55tp5k8PfRcDpwfLAYB5Imczq5mjUO+RSYfweRaWfatA0YfLXSERo5IlaotSa0jx5QU2hmzi4TJgIfn1aG64E+aM11W7AwDKYTSXUUEb1LlnxWqqYB8KdGOI2hzBxAMx8UB7S/GsU0nBU1Tj+oZt3Ljxww8/XLFiBQAsWrRo9+7db775plgsnj59ukKhePfdmtIycjiclStXPll//PjxEydO1DwvD0dauhIUfOTmtgogK1eNc1tfLL2kvz7KcFun3v8lU6wFBPFY/QHyKFPhlrQvJ/m+z0VtHwzAgYNnB5IkJ0yYcOPGjchI+3sS1xcChW/+LWGAhUfRTygGiq/AeZ3pAphkAqyfH+cTsXOKhQIAI8v+pjO0J4losznZQoUQ+K7Mb3OMmZ+32Eza2keuFHGkDBDQnFf6fNOirtodABCc8F9rTv2ADPy23HmPNaZaMlYRYfsbQ04HzyuVFHxjO79+8cUXhw4dGjFiBADs2LHj999/b9OmDQD07t172rRpNSv4hqCn2H33ddPav+bmugEAilVlaRAZ7Q2EeCiZco0q1LEUzegNRIXURibGmK5PcSSQdfB8gyBIVFTUwoULFy5c6OvrW/4j0Cxc4f1E+IHhEgsNALD5lu5ytlFMYp93c3bhoYBCJw+S0d0xxY2UA+gB9Cz7NQAPQSwsa8xiE537KhFhkCDsdMHh4R5jqpuC1lxijSlWLiAcXDqyVu958XBZ6XZ9vcBEfVFeqCU/ipC/X1pjTv8U95yNEPZJ9UkV7GMpK3aXCMLBPd4FpBl8Vf6bVE0XS1HU/v37z549m5qaCgABAQGDBg0aN26cTe52DMPCw8MBgGVZi8Xi7+9fWt+iRYuCgka0aNOamL0x+mntnbzbrQTol6+kgTECSprTFpB+KxDCifB0erLX+wELMaTq/48DB88fo0eP1uv1VQxdbXIk19gkF1E3csrscgr0NABYGCZGYXYiURce2sWDBH4bhPTCPaZ/yvZ0RtGFLiIUgMuaqHt9v8AHz3BvEcT1/SxhXi9JfynpZnUKKm8Ha4hHhZV2y1lLLl38NyYeiDTC0p+lio3RXYEx0OrzVPZXAAjLUsDoGc0/TPGfnPDfGjJ4ioXSsWxrkiiviTNbcAQJIWr6raMVP7OMDnOu5OvE6KIZUxruMbUh8jhoVCr9UTUaTa9evfLz8994442OHTsiCJKamvrJJ598/fXXf//9d8OjyY4ePXry5MlRUVGtWrUaO3bs//73v5UrV9I0vWbNmkZ1k5MJsG8HiQFA5tuHwFG1hjHoMgnDP4ASmHREdb1K8/05cPDcEx8fb28RnhIjxapM7LVsE81Cnp4GAAMNZ9PMJAYcDGIVlsltnTz915iT3y/26veiwAXDeQBgyf5KQfo+INuHO4UJEKS/dOgvuXun+c23OgXpu8wYM4jwWYgQjxfipsR3CK+5SOPY4SK4CHVqj/IjgCpm6RLCb4Updhju9RFduA8Vv9jAwUtY5r1C1Teuks4cEgCizeaZhar1rrVEnif8VpviX8M95yL4I+MMljbe7036rQLHKugZptLfZt68eSKR6Pr16xWj0H/55ZcvvfTS/Pnzt2/f3sDJ1q5dO2/evG7duolEIldX1+jo6O3bt1sslrCwsCNHjjRw8BpAANq7kwCAoqirk2tuUUFy0qkgdisnbD9UCGhjfJAILMuNeBwyen3qyhEeY/14QY0nmwMHdsfd3T4bvw0nS0tnaykUAQTAQpeexLMIMDiCuvKwFhLCiUAw7guooM0K4/7R1Ns0wMuE1pSzZb5k62RnoQBBAGCIbOTih7MSdA9CBVbO4xBuIOY2xpK5hgz8prSG0V5nSm5ygrY03uci/T43xgzkRJwxPRgGmSRCeiKElDFlcNwbeo7ZhiQ3uErmKFTrpC5cFJlZqFotEXfi1JI7DhW0xlyGUNnrCL8yQygqfyeCu2IuQxsoj4NGpZKCv3Dhwp49e6rkmOFyuatWrRo/fnzDJ8MwbOPGjV988cU///yTn59fVFQkkUhCQ0M7dOjQ8MFrZsNN7ayOQgwBV64sFwrU0Sux3mNRQfuKbah8hfFBYkUFHypomWvMdih4Bw6eTUJd8KGBZb9XKy5rDDQNLDIylO/jjLsL0I4eZXqL9FvpfL//7pZjJhZpgooX/c2PDOciU8VeZVdRcpT87f3Z3y8NWYciVmJ/Ed6fGKO7MLrxqKA9AGNOW0D6roTGNL9FuAG47C0qZyPhs9Cc+iG39Z/mpKm2CpjfkUNucJXMLFQyAF9KXfryuHXpRfgsMt7rgcnGo7wQliqyZK/jhB9uuDAOGpVKCj4jIyM01ErKo7CwsLS0NFtNyefz+/fvb6vR6sju+/qJrQUiDuruIrufB5kat14+i6u0IYP9io9eqFgzVDaqCWV04MBB/cAxRGMq8xcv/QcB0JkZjYlBKhitIxw/3H2CZ+7qr13GC7L+OixZNhXNO1VQ8TCbLTTnXVH/8YJkwJOzIJiQ8P7EnLaQ2+o0VbAXUA4mtRJy27bgXh8ZozvjsvHcVqeZ4ksI4Y65vGSrwTEEGEAAWC5SV9t+hHDDPedY0hdzWhyyZH2OSYejfEfQnjLydbS74Fm0NKx6fGLVmK5Z2NPWDB9HzNo7tFHrIUcgDtLULow+FiAWABCUgwq7AwDp7UGrihmdARU8fjYvoTRcjI87zpkcOHj2UOjp6EJLrMJCs1BiZgGAQdhTqSYubuZiSJzSMrYl30OAAQDh+UFJdGdn9T/RsnkqrMcdNqktnVtxqBelg2uIbYXLxlP5e6iCHy1Za6sc7TUCDK06DawFEw8xJ03HPedasr4gvD6mlUcQQoY692jg6HfN5pmFqi+lYiGKlu7Vd+PWaWOA8HjXkL/HkvstrTzCbXutgWI8N6iNzMjflFferrevRBNQVW/t2bPnyRgXJSUlTSVPY8EnEE7mbDPoPb1LAECTm0DlbAAA1pLPmnN5HeIB5QCKkv5e5swcbovHe/InC34VE5KX3Br9gd2BgyZm3rx5NVz95ptvmkySp+ZylvlmnrnYxLAsUI/M/tVGBkXAjY8KcAR9tEJNZzm7hR9MMfz0iv+MVhZ6UiG/i6jXK/y6b7OjpN8qY+yruOztKkd7DeGPdFMHD0LMqXQuwNJ6c/IMhB+OkHKGUpozFiOEG627yRTsRkh3bstawn7UTJzZMqtQtVbq0ovLAYCvXV0+UKg3u0nakrUcwwOUxo9bbYofTfqvRfBa7PL+O1AM0M+qy0klBR8REbFv3z6r7SIimne6Hj6BKKWL5OplMvfJAAszi4SlB0im+Ndw2YTyky3XWW9jkkrPN53FPdP0yXaQ2IGDRobLLTt8VavVUVFRffv2bd26dXp6+tmzZxcvrnqA9WwSJMbauJZ5u1zOMquMNAdFuslJLo4EiPFp7R+7v65SFw/1HOslmAIAgQQe5SZ5T6HqTBhZWifnetdlLtS5J+m3CnN93Yby74gumUMIu3pWUq4I5oR7fcho/+GE7Ga010wJE7kRFxCEMER3IX2XNXBGHEE2uEo6PrKq68LhbHGTkHXeqMfEg8jgrbjUcXbZPKik4O/fv28vORqb1b1F3tIhjH6Xp1ciAKgKiwGAVp9hTZm4+6TyZrh71T26QH5oIN+KXYIDB82dNWvWlL4ZOXLkpk2bZs6cWVrcvXv3sWPH7CdXPRgaxBsaVLYKn3pKfSWHduagS3s5P3kgut2tUkqSUIK4IHffkLoq25i5usUmAqnD+hUAl8+or4R0CW3JNXFD+PXqRcinGwr20UW/Y+L+vA73ASEsmSsxUW/UqVN9BajCk/7ubeqydq8A7jq6gTI4aDL+K0fLEW6EJXM1oEIZ/wgAqBUl5pTZlPIYJuxiTl9E+q2C6u/wYovawBg8HNHpHTyn/PHHH5s3by4vDh06dO7cuXaU5+ng4kBi1leitKpYsSeaUZlcRvuVuskodmZn56TnvZZz7d700y7HX/VolCUpraWSht81PCgJ+qmtsI9LPXoiJOm30pL+KSa6DAjBmtKp/D3cNpcbQ0gHT0FMoWXEbwqmwsZ80NYykw4nErn6trug7pGJG5P/ioI/kmDoYXrojCk9fDsAXChQMqxFhXK8AeUx2utQIaaNctsB8ehhmFhYXpOsT/hLeXZe4FJ7CO7AQaPj4+Nz4sSJqVPLQpKdOHGiLgkenzWcOagLFx0SyHOufKRtuBObs+iU9nZbBEP0d/907n8VeP3zN2Wq8fzR+rFHAjzOFmzoKeldXSS7p6ZUu/NaOXmtCk55637ArlbCvhIAuFdg0ZrLLP+LTUxMoYV6pChauRJibpnwmMtgKn8Hlf897jHdnL4El7+HkB62ldDBUxPhRiROk5e+L9Qzr/xSeG38sxhM4r+i4K9kmaJyPvi1xWu+7X8C6JirYBjtVW7L48aHYzlB31W0iaWLNKb4ZH63duU1EcJ2JbTGHlI7cNAUrFmzZtSoUadOnQoLC3v48OGpU6d+/fVXewtVb9503TOL/F5MonAfDBXqLXkaFn8/5FgXzIVIHMalCnLNisTCn/X30QcvvT/orfv5oS+/dDjvxym+tty0KNfuvhtbAAKB+1unjC3T8bvv6xSGMgWfU0IfTzL+lVn2+zOlraC3z2ODdsLvc1PsywjhwejucYKjbCieg/8I/xUFPy5CcLj4KiJoSeTPE3BwnYnSmN2R9MWACRldNCrsWp4vgRPib0pIrajgSZTTW+LIS+jguWXYsGHR0dE//vhjdnZ2mzZt1q5dazUexjNOS9/upofrOWEnEOzxgXfx6T9xp8XyRa8KuogAwHWSV+7nJqeXVcc4xz8KWulx3DO8+/UOm9ptGf91oi4uRBBew/h6ij2TbBwZVrvhfRXtDgBO3cXlOv7r/o/tz8ceU87pVNXIrhyUF4pJR5mSpnBCdgJap3A0zx8X0owAMMD/P/rxG4iVsE1VoGm6WaSdqBkvITbQ+TjQWtaY5uHOB4AsNZfVXkcxkSUv6lGqSQAATkiAKSGtSnc9rbui+qMpBXbgoClJSUm5f/9+YWHhhAkTbt++bW9xngZU2BkTD6SVhxGOf+mr5B9nKnef4fYLgq6BAKD8MVe5N0f8KTf3kovghxlf/C2c+6929VS/5GNq76+mTT+bP/u8Ok9XbcL1HC297W6dHIZVB/PMWUbvtSEVveWduoulb3pkL62fSw7hvYDw+hiTvFqvXs8TsQrqgcJibylqQmdh1MZnVEVaV/AJCQmRkZEGg+HChQu+vr5yufzateYd1uBegfmbvE9YUw7us0wmAgDIzVfg8lksXUz6rayYL4EM9rUUKKp0Z4E9kPP9rsxN97Q3m1JsBw6agH379k2ZMqVjx443b97EcXzu3LkbNmywt1D1h9HjHpOpvB2M9gprSluVH52WvYv0ybHkeBmiYzIo6kuqhDYx6ux/ij20HRQGP7cEP7cEjoXhUkZ3uVEsuREqT5Zwa1/z1IrrJC9hH5ekEXeZCo8Lyr05qp/z/bdXiv6ms7AmuibdgOBiwvvjhovkoPEQEKgz55kwqXsS61v0U6ZMkclkBEGsWLFi/vz5ZrP5ww8/vHy5mdlwxios751Tl74vNrMaY9gp5YtGxX0XIQEAitwcBBcjuBSTDKvYC+VxvTcvrzKUAHOKdH9zf873t4r/+TZif9PI78BB07Bly5YDBw706dNny5Ytvr6+P//88/jx4+fMmWNvueqHJXudJXcLoHxj3GiEcJvM0E7BebRRJB69M/Nc0TTOArf2FNbiAbO3nfOLuowlcW4oEHnEku9MReMKTdOUr4JTW2e2Ojv8eoFgiN/WlunTY5NG3g3+rR0qwJR7c3JWpoScaM8NE1RsmVdC51e/Z+CguWCLb02jYF3B37x5MzExkabpW7dunT592mAwrFq1qoklazjhrsSPr0hLDVT3x+q239V17bTcJaXX74K2AH9kFossORusJldGrKVGPlHwa2th+4SSBwezd47xmvRkAwcOmilxcXEtWz5eWYaHhyuVSpuMrNVqDxw4EBMTk5+fT1GUXC5v27bt6NGjG557+kkwaaQl73vS9zNz5ue4x2RDycOckvs3nHuPzvsp5cRIl+4Wb3Uhmh0g6C7GYzjddAMwZzxh0q19XYXLl7/AqeYXWmNiLmeV5ZvP09ElZvZUsrG0KCSRF3yqjfBapuPfjU15855omFv+1+mhZzpwgqp6wwe54L7O/xVDqLpzPtV4p6BsW/5WrplFwERrS4sd3Iln5zyeVh6BPGR9AAAgAElEQVS25GzkMbDV12K8Xym9OCGfhbnaPxyQ9e+WVCpNT0+/detW69atBQJBcnKyQCCw2vJZBgHwEpaZzsmdMADwlrhTprk+vt8CgKpQjUmmoHwrCSKf5HThER2lEeLOIz3f/iln1zD315xw2/9COXBgF7p27bpx48alS8scQffu3duxY8eGD3vlypUhQ4YEBwf36NEjPDwcAFQq1Y4dOz7++OOzZ8926dKl4VNUhFadphmDLmMVh3SnMpZ5sIwHgoab7gGP7b5qSk8ThvAB1iGac29L35qROPwOykVdJ3sf45NV9+sqkKmlT6eUmeSXmNkSC1Ne5GBIDy8OVv2OPoIhflEt09+NzVuXFnrainZ3UB0cHBGRZY9cHAwBhC0vEugzsViOVVhmnlN7EZ4bveJX5W1K0YldlWVfBRlRtNhjFiJobV8JS7Gu4GfOnDl48GCGYdatWxcfHx8ZGfn667YM0Nj0CAi09KQL95gq9/gfABQWGgifBVYbs2ZLzgefe21aCggCABRDHcn9caj7yDR9spzjJSJcvktf91HQiqaT3oGDxmTz5s0DBw7cuXOnWq3u1atXYmLiuXPnGj7s7NmzV61aNXv27Cr1+/btmzVr1o0bN2roazabN23aRFFUlfq4uDiKotauXVtaDAgIeOONNwDAYDBE/Uy/3lVgwvDfUgdMkBYKeHCaHxmq+92MkFeOfNrlnMZ94GlORDohDyXECbIVIX8c/j3D+HcnJuybdQ8DK4yzefNmmqbLx9/0qH7Vdz/c577gfftolXmrtK9Sj4QA5o/F3cp5I6is/n9bdpqZMi2V49K/QC/I1NBGo3Hfvn1cSosBU6/xn/v6ToFDWWDHt8BK668BXHsG5PTjUy/k/sYwTDTSKRL/fpZh26bhXgBgNBoL/xl5NzPi74uHax4HmgTrCn7BggVdu3Y1mUyDBw9OSUlZtGjRxIkTm0YgG/JAYYn81UqwofbFbwD8L6eARarJ6IyQBMswltwCwtMdALZlfk2inBEebxkZA4mQ7/l/vDrxkyRdfLCgRZN8DgcOGpfg4OD4+PjTp0+npqbK5fIhQ4bYZNjk5OThw61kaYqMjKz1gJ9l2ZKSEoPBUKW+NPGVWl1mWyOXy8vbq4pMl++17hCR+JLXiSKzkGdJeUmzHVigzNzgQTNMI1gBqweGzT0iEbKZwmnd4uNSTWqTJ1wrAjBWGEer1ZpMpifH1+l0NEGXTi1/oj0XN4fI02RkNqUAAGDNJl/nfy0WCwAoNFKjUVbe/gzRvQQtW82bKMFnl/OFJAdYpJjTt7Xuby99stXxn5THUW/HegRYRJNLmUwXi0PfG3CopTbGx9kXAPR0ootLzOYLrxvM1r8nVcZpdNhqOHny5PDhw4cNG5acnHzgwIHqmtmdY8eOcTicWpsV6On2O/NK39+9egcAwgO5tD6h2vbrd2svXGVZtsCUN+nuiJtFVyteXZO48OPYqQ0T3IGDSvB4PL1eb5ep/f39161bV7GGy+U2fNjhw4ePGTMmLy+vYqVCoZg6derw4cOfbsza7nfacO/FrNvdfrw3tfC6V/E1WezBMdE+R/7K0fx8f8qdW7218TNiu16If+EMY2HqO3WiyjLoYEF1Vy15u3T/iPXXPbTXPXUVXvp/xMo73QyM9emG/Zoy6u9PfszaXl9h/jts+Fe7/l+NvaWoCVXmrhsXX2RZhmVZY+wIS97OuvRqmvvd+gnSc+I2UwGKYS2P3FF8wnwBIKuAZs2Z1bXntgg0xicDwHdpXwLCHsj5/sO4KR/GTXkv5s3ZD8bnmrILzQU3iv5uGuEdOGhUMjIy9u7dO378eKPRaMNho6KiNBqNl5dXSEhIt27dunfvHhYW5uHhkZGRsXPnThtOVAGU9F+DU4p+hvMkY8IY2iVR5Pp6WtsbPw40/OFBZZi4051aXWJZYfaixPoOLSSRYJdqDeJw94kIIcVE/XeHxY/3u0t1yuR3yea2OsYCulT8mdFaKBGKpQrNecNkr90oupxtzKivPP8R2rkTbWX1S4fzlNA6liqy8qJrCX5AS8fhYKGVh2nVUdaci8vebgpp64b17+vz4TZTEY2J1VNl95iLiwsH42hLTCplopuon9X23DYtTEnpADDEfVRySXx5fYEpt4hWh/JbAkAQ37FF7+B5AEXRq1evjhs3rk+fPocPH/b0tE1eJZlMdvLkyfT09AcPHuTm5rIs6+7u3rZt20YKdE8pfqHVJ2+bzB6Au2JAAw9h9MIXf2WZHqwhkS1ATYoO5u0HnXoPL/rT7NRTXN/x3QXY5kE1JYwhAreaE0a/55NrBo+JhcqdblI6fkIip/PH3gPEqJWl1JnCIxw0KNyplRc56kD2jg8dZj3W6F29q4JtMdzryVoKELTSdCxrAZbhd06vmK+kClIeXuC3ypzxPgBGBm2sGFXF7lgXpfHcZuwFjj4OKoUgiDvhlkFnpaQ/cAu03p6Qu7nOHAcAnUU9Oot6NJGUDhzYCYFA8Ntvvy1ZsqRLly6//WbFd/Sp8fPz8/Pzs+GA1YFgfL36wq+iZfNceuPZy3HMOYvbhqNNkeKXUKEXQvI826yztHVJHhnt9UWI+FWZzQXAXQZY+BHmpPEftLkGoNme9O10S553u4veuJWfWQ1VdK7wmDd/LRdHOkuHXlKev6v5t51zZ5tL5aCOEJ7v08qjnJaVEiWbk2cgpGcN2h0AEICWAX1MiZ2ApTDnFxpZzPphXcE3ktuMHRFxUHGFGFUezu4ZxqyMjKSu9R+KYikDrRc63OQcPF8gCLJq1aqIiIjBgweXWwM1IzCXoSWCTvOdKLnXNIrgItyAYH5r3a1wYPhUIY26LIEiafLIO15fhEjeKEvLZslcxZTcsjYYQgZtQkiv+srACfnBeLcdpfjJjzf4RfUXZ53fGkBKrbb8KWdPX8mgV8I9Sv2+3vSasjdzS4SwHV6jLnHQeOCyiVT+Llp1HJO8UlrD6O7SRRe57f6tS3dO0JbGlO4psa7gG8ltxr6YKFZvYfkEAgDB/UJvHLyVk51VQ3uWZhiNFnMRValP0MUeyzu4IPjzRpTVgYMmpGLuuDFjxgQHB3///fd2lOep8Qr8whT7Cuv+Gi6bAADKH85xfTFEwMddA5Pe8kSQO95fhbmMqLB2x0XK4mxZiy+RCs7VjOYqpfgJwa0r5ppBuT64dJQuZT6Xf5KDYhleyycVKne6Sats0acZkh+U3P08bHO5V3dLpzZyrvcFxcnBbpFPMW9jQykOsebcJ+sR1An3mFSXnCZ1xMgY9mZtneA9g9P0yXUQjPBfY05+nyceCCgXgDWnLSB8lyGYsOZ+Ogs754J6xxBJzc3sgnUF/6TbjEhUVc89+6QXU1/d0JYevJtoMNLszHNqJxIBAEWGCAAKsgtq6G5Oy1JtPyT/4qMq9aGClp7c5pct24GD6nj11Uq5TDp16tSpUyd7CdMQUF4YJhluyVpL+n9ZuDUDEywv/n0mv/UfwkFfBu73ojW06KVKapvwmFacsFPCUFzJgLIqlrakLyb9Vj519rYTHl/2Ux17seQYGRz1gUhsZjVTCpX7ZK68Cg8RP+Xs4qK83VmbK3bU0SXH83/qIxnEw565kDhU/m4AGhP2rFjJ6O8zhoe4+wRAbKbgT+T/fKf4uispG+nxlq3GrDuY8wuooLUl9zvCax5VeAgYE+72Rq299BY2ptBKOhyapbFHSUrthXUFX/787uzsrNPpfvnlFwCYPHly08llC6Q8bHBgmae71sz+mW7sJOcEiDAAINw8zwHk52mApaqziSD9vCzZ+YzOgAoqucvjCD7ee3pjC+/AQWNDEMTmzZvfffddgrCyLVzqw93sIHwWGaO7qi4PLfn3suhNlo0fpfx5mOowE7BbhBBPBEFDyA25H38jXgQufUrPWamC3QguKd+kfQo4GIfyXcMtOoK7vg4AC8TOB0t0FpatqOAjPd4sthQ92RdDcDusXOsA6b/GFD8Gb/FrheUsY7w/gPRdUfP5dL0oNOdfUl34JGjV16kreksGuJLuthq57pB+K433B+DSVyyZKzghO596c+J84fFbxdc+CV6FgD1D71nXbaUaHQBYls3Ly4uOjn711VebnYJ3IpGhQWV3i8LALLuMtHbDe/twPlSqxS284Sik5WOsOS8Xk89UqHbKpC6Vt9EQHCOD/UzxybyOEU8Ofldzo52zjWNtOnDQlCQlJUml0tI39pbFZiC42JwxhXCbLJusMed6OHUc5/yiDmgouQi4KwkogctnIJgTAKD8lig/4pL2RSB/pfJ34h7TWKrIkvWl1fwUAMCwkFJE1eApV8rLfB7w3wH5O+U1Y5yqxvkOFdQpQvazAypoh4n7UTnfED5lhllUwV5ASUxqywOFgzk7B7tF+vODB7oO+ylnz3v+dkijh3D8cdnbxgdDMFE/VNittJIFNln3sO6RzTRU8YmCn50w5xtFl7uK7Wl2Z/3Levr06YrF7du3R0VFNYk8jQgCrN7CAsAskfB9Xw8AyC1ACg0p7xjIcU4CF2t+LNxWwcbYJKsK/qecPSLcJYAf0thiO3DQSKxfv76Gq998802TSWJDVIfycld09d/2HaPj79F/9d4Ld6m8NcAQLIOwRhow1pK+hGZYDDHqEL9LgpNyMvtv8+Qu6TOvKdr4m3b7CHsjmIA1pSMcX6i89korpmadV595w81eH82+EL7LjNE9cLe3EG4QS5dYstZywvaD7ZancSX3sgxpM/w+BIDBsshF8e/Hl8S0cLLy29vY4F7z6ZIbhO+y8ppr6r92ZGxYHPJl+Q9+WjH11Y2y/DcmCjRm5v3zZdEVCRRpEXSop0u/jqLu36X/r51zZzvuytRp/2HcuHF37txpbFEaGwGBlmZ/8sfxRXkyAMjMNe0pjBnnJHhbaD2VjqBHx9JotU8y0WemiKjJKdaBg2cc7iMMBsOGDRuio6MZhklNTd26daurq6u9pXtKOIF8SsVmzF9DOQlMHgzp/zGCuxkS26e8tY+h3bgtT1JtM/dQx1gWOW5eHlw8+1DIiFb6BQxDddeO8rH8ypZcNz542XCnPWtMqTIyw4K1cDX/FRBChsvfM2csBwAqay0mfgkVtLfV4AzLHMj+frTnpFInAgIhX5ePP5Czg2EZW01RdxDMidvyJEKWRZM1McZfc3/oK31pf84OFsq+AW58bEggr/TVz4/LwZDyYifv4tiS66+4vxEkCAsRhJ8uONz0H6Gc2l3yWZY9cOBA6VZe80XMQWd3EgoIBAAKt2dxNmn4CE9bYmiV8nBoSLWJ8ghPGeFp3V82VNDSar0DB82FNWvWlL4ZOXLkpk2bZs6cWVrcvXv3sWPHqu/37FJiZlMT9QyGmvIl8enTh3sti81rz0HWykMnu83eRhW0RPitnDF0inAGgnWYFPEqU+KZdWesrOvfHJQx3u1IeC3B5TMsWV8whkSEG2TvT/PMQXjOMkT3oPJ3UoUHuG2v2nDki8rTQlzUQfTYbbmzuOdF5elLqvN9pS/ZcKKn4FTBb2FOrcZ5T1uV+PE19V/dXfoCgIB4fP5bqGfW33xc/CIpaqTbm6WWkm94TvwsYV4vSX9X0vZxF+qCdQUvFD52DKBp2mg0fv31100lUqOAoxDkgv2ebmz3lzr940TLa66ue2UZ5nT++bvHOHlX23AXiJ2tRpuqgRtFl10IaYggvJFkduCgafjjjz82b35s0T106NC5c+faUZ6n5q9M4/lk4yiG8fh2ZbAoHnCGSmmPGTCWjzr3/ANonuFmAOG/jjGkcDvcAwDUqdONku7D8jYivou5bS4hhAdrzqHydnBbXywdkAXQmMoWkVozw7BQ/KhIoAj/Sau954t/VBfvldyZ5vtBWRkhSd/PTAnjCb8VCGEzjcUCeyz/EIlyViTMr1ivpTVH8w7aV8GrLIqLyjOfhXyFADLWa8qWtC87iLrVsOV+vehvI2Po5dK/tOhCSPu7Dv0ld+90vw+bSuRKWFfwd+/erVh0cXGRSJ5FJ796kaCi0J/zJZvSja+5Tl/qykkKgj/T87m5/iPj2AOBopesh65ktDpTSgavrRUtzrDMucJjDgXvoLnj4+Nz4sSJqVOnlhZPnDjRSNFkG5uXg3gvL/HXdBcqdw3WTjBnL58atmS1LvYY7vIRP/w6Ju2D4CJL5me46xj0URCbDfkfDXWLZGVjEW4gAJjTl+AeUxCOT+nVa9nmmefKzlZpFgwW5sX9haVFJxK5+KashnzwzR0GmD3ZW82MuadLvwhhu9JKTPIKGbDOtuHWEUAWBH9uYqzEVuKgTRSntjoOZu8c6DpMSroBQBA/LMwp4lTBbyM8xlptbGbMv+TuHes5xcDoyyv7SAYtT5yfoHtgF8vKSgp+3rx5NTRtpkY35QwuMOZ8kyYdL9+53H00hp0sEAFADq2Q+ZGvjUlWZ3tJ+FYedyh1sWr7T17ffvbkpa4uL4Q5NTNrWAcOnmTNmjWjRo06depUWFjYw4cPT506VTH0TbPDeYAUYJop81TCCEUo8yIpnUv6ZuMuQyn1Kcx1DDAWMvhx6qx2Xl6YxwxzxgpO6G5G+y+jvc4J2lR+tbsXefudMiucJDU185z67Oj/ipHd9xkbUQRtK+q4I+Ob9a32lNfj7rZ3p5JzvG0+Zr1h9CxjrliRrE/I1T+Y7PX4874uH/9ZwrwXJAMqbrm7cNH3OwoBIM2QaKQNOzM3wRPc1dy0v4Lncst2HtRqdVRUVN++fVu3bp2enn727NnFixc3vXC2xVlK5mJgSjNsFLsgOJIp9LoOkJXPvMQWUQInl2oexUkfOWM0UQVKXFbVCgEBxIVo3qYJDhyYTCahUBgdHf3jjz9mZ2e3adNm7dq1oaGh9pbraTDRbIGOAQDoIv4n+bOXW72btWGd3+I5Ov8NrPYIhuC04gAZsK7i796mgS7AzDLc60EX/2HJXE36Lgf02YozQ2sozLmp85cUUarrRZcme7/fXtx9dsy44wU/vSKrPeRLs8YQ3aNKshkJrf8QWMvtM9eDdrGPwqV4crx/zt0zw+9xADQchbda8QEgVNBqU8QPTSx2zVT63jx/RjcVKQ7mP/y6RdiHDxNfvuN9ol2JjxyuQ1YSjoizWxx5G+FUs9eGINzWoYb7D4X9raScKaE0G1JXfxryhX2jGThw8NSQJDlhwoQbN26sXLnS3rI0lFPJxg03y5yXNJw2zkV9eJF/LM45Ky/IWOhx2yP4C7rggJUFKMohfZeaE95B+OGY68imFrpGik8rUt++7/1lqOukegfGbwgbU1a7czy7S14EgKGyUcfzfn7JNZJEmyRtq50g5O/RqpOclkdLixRr+S1nd3vlIRPmlGpMK28m53o74/VORWg3rGaJF4lEOTk55cX8/HyRSNTYqemfjmPHjnE4nLq0jM43D/+lUPOn8rbLH8devPbOrl8BoGuA5It/tl43GmvoqL14reCbXdVd/TJpidJcWF+xHTioAo/H0+v1dpn67NmzkyZNSkxMNJlM1CPsIkmt1P1+/z3NOPd0gv5mMK1/aIjuRSmP1NzelDSDLrlbQ4O0IsvQn5r0Ti86VRjtf0n1a9798MuF32c12byxmuhJ0ZEZhtTymtn3396UtqbJBLAPDGWI7kGpTpVX0CV39TfDGKq4MWZrmvvd+s7Pc2N0UxEhiWjNrLCP5MKOoBcnJ4/417ALIElnec1DOV6hPiGXWY11AwCC7u0JebUHbx85sjg7aOaMHj1ar9fv3LmzYiXb/J2+S1gp5vq6MboHoIQ5fRmkL6t4FZMMJf1WlxfJ2rKB+Ynw/a9Wa2vMGBKpXOsjoM7dcNfR9ZAbAAA055QZM+OCfmor6Ows6CRKGHqbpVm3qU1xVr014+u2ws4+XP/ymsk+szekrc53z3bnNulGQpOCYITf5+bUeTxRP0A5AKw5bSHhuwTBmnHiUOsK/jkzuilFyEG1ZgYAJgz34gidyHkEHourCkvc9Gm/hblVp90BAOGQnLBq8sYDAECRRSXERXbPK+DAwdMRHx9fpUahUNhFEptD+nxK5e8m5O/hsnHllSxjMcUNx0R96juaqLqDPAAEc6IUPxHy6eUW+KVYMteg/HpbV2nOKdOnxwYeaivo7AwApC839FSHhKG3AaCxdfxF5RktVfSw5MH7DyqZyrPARmV8syR0XaPObl8wUR+U18KSt5XwnEMpfgVGh7u9aW+hGoR1BT9s2LDnw+imIiIOUmJmAUCGYTBQEnG9u4dIlkXnpKbEtW3ZIN18PP8nN47Hs5nn0YGDWnF3d797965SqSwt6nS6iRMnqlQq+0rVQDgYcDAEUCdOyC5LxhLC+5PytCh07haUF4aJB9lwOoSUE15zGX0sx2dJeSWtOongLrj7xHoNRRdRyWPuBe5rXardSyF9uUH7W8e/8K/wBRdui2pjc9VKKkWVMGxr8nGGmHiLBQMkhCjTBW2FnY0eBqt9/Z2Cn3re5gLpt9oYMxCXDrdkruAEb2tgJlzFrmxhXwkngFd700aisc8AGpu6n8mxLPtQaalY7MbpBAC/rPeqtSOl1qh+OFrd1RxD5p+Ks3WUwYEDq9jxDH7p0qUkSYpEIqlUGhwcjCDIp59+ahdJaqVe97uJYkrfGONes+RsKX3PWFT6myG0Pq60+FdGmf3N3wajmWEqdr+oN1Qq1wpt0N9uSxdfLisyJv2djpT6Qr3GYFmWsRSVXJXp/hE/+So63oul6ydUFe6aTD2ycv81mkqL0SZTz6zcG4+KDliWNacv1d8MNSZObuA42cuT7oddvt/isjHFyn3dNPd7pccTgiBKk8oQ1rDJ84RWq42Kipo9e/bo0aNHjRo1a9as7du3azQamwxeK6GSSjsWfs4+AJCaWcLSJTV3RDiE9swl1mS2elXO9e4jteVqwIGDpmT79u0nT568dOlS3759ExMT169f7+LyPORZILEy3xbC73NLztcspQQAS+ZKzPV1lFeWGWz6GbWJZgHgsE4/T6m2PLI8WFuk+VajtRoJnVYXU4UqK4HpUS7pu8ycvhBYGgAsuVtQfgtM3L9eMhczzF2ah7sOM2dPT51+ivDL03dWpoTlZi373ZLbRdBpIqANcthpS5IbXSVzFarrJtMDs+U9hWqlRNyZ8zybx9cX3OtDhOtP+i5vyCC5q1KKjhaG/dXZ45OAxKG3TanWN0Uam0oKPikpaezYsaVvnqThk125csXLy2vr1q0Mw4SHh7du3RpF0R07dvj5+d24caPh49fK5tslBfrH96yfuy8AJOTxWFN6zR1RHpcM8jXGJFTXwMQYr6j+sJWcDhw0JUVFRW3atImIiEhMTASAKVOmbN261d5C2RKUF4JJX7Nkfs4Y4mnVCcLLStzQtRIxAMxSqM0s+3Wx5rrJtMNNWuXozpyRk/Pxlzkff5m3dH327BXG2Eq/iiytQUV9AeVb8rcxhkQq91vCaz5LFZXq+zpSzDDvK1XX3Rdz/H6WTUPjhtye+2928ej7on7XOIEa3H3CU3z8KnTkkBtcJXMK1ZMUipUu4hd5z2IGejuCYEJuq7MI+fTmhLmrUtSHC0JOdSDcSdeJnnbU8ZVXtH5+Vd4AAE3TKIoiiA38vGfPnr1q1arZs2dXqd+3b9+sWbOaQMdfyTJ1kZMyftnjalBoEMRAYg7KGlOgNkMYXruW+juxVlPHlnIoZ1eEsL0jxZyDZkd4ePjBgwdnz55tsVgyMjIYhiksLLS3UDaG8P7EGN2V0V4jvBciuJWbFEeQ9VKXuUr1sLxCAYrscpNWSU7BmswFX0aJRw12erEbABii4wq/2eW59iNMIgYARnPFGDsMwcXA0hbtDQu6HAHUFDeSpfW421gysK5hQH1xfIebdGqhcofrRHn/bWdy53/wUqLbZDfnQd8T/usAsU3EGy6CAAIsA/yG7Qf811hXpHHB0MlCJwCgVBatnppN6ha7iMIq7HDnrkopOl4YerYD7lqmaFwnegLFJg69HXqmI+nXpI9T1i0IEhISIiMjDQbDhQsXfH195XL5tWvXGj5ZcnLy8OHDn6yPjIy0yQ5BrXzeR9TO/fFfotOingCQlm1mjKm19uV3ijAnplV3lYNy5wUu5eNPb/ziwIG9+PzzzxcsWPDw4cNJkyZ16tSpe/fuVu/TZg2CiwnvBYBguGx8vNISU1j2Yll4oCh7n6ym/XFMzdAuCMp/YkljSkjF3SSl2h0AeG3D+R0j9LcelBZR5+6ooB0Z8A2vcwYum4BgYm7HBG77ewghqe+yuwVBbHeTziDeVmmuh87ICzvb0eOD8yi/FSZ6seH/DwDwwGyZplCukYi/c5POV6ivm6wEgXdglXeETod1+u80Wku+Oa7Pvw963uicSYdWPr/WXlI7dRfj0kqnHk69xYyeNibroWlBWGverr1795bJZAcPHuzXr19kZKTZbD5x4sTly5cbOFlkZCSPx1u/fr27++Mk60qlcuHChQUFBUeOHHmKMY8fP/76668bjcan6Jt9Ms17WADJIzX3p3OCNtTanrVQCNHUMSMd/Efg8/lKpZLHs4/BrV6vJwgCw7AzZ86o1eo33njDVmY3tqUh9zsAAGsuNODvnlExLABABOcqbUyVO5Wd1ZdIGL4YmegsOK43/us0fJ2bJ4kg5qR3ae2/AMAYTazJjInKMm0iKMeUNJulxeI3hpbWMNrrpqQpvLY3WNbCmrJQfktz+mKgdXVfvpejoJmxhYUdNCfmG3a7hv9svNeLG3HWJhls4y2WKQXKz6Xi3lwuAFwzmuYr1d+5SdqQjmP4OqGkmZnxee+PTsEKLBiCcDho6LmOFf0aaA2VNPyOoIvI+4vQ0gCnxkR94rDbXp8FScbKy5s1zf1uXcHz+fzExERXV1eJRFJQUGAwGPz9/UtKarFEq5WCgoJ33nnn7NmzAQEBUqkUQRCVSpWSktK/f//9+/c/XcK6et3wZ1ONPIGRGdkAACAASURBVBzp7VMWbVh3o9irp28xpUn/q59v79+fYvYqnCs85kJIO4t7NnwoB/81ml7BN8fkUvW63800W25nZ71B8syM7L+8vAZiCNwzm/Nppg+XQ1AFdPHFVb4XNbhsk6uEzouiCg9yQndacvKVWw+4L5mFEDil+IVWny06NMT51YH8zq3LBzQlvoPyWxJeHwEAa0wxxgzitr1a38yqCpqZWKh4hc/vwyO1MYODShRO3qPIACv5rp6CRAulYZiOFazq7prNHEDCyZqe51hKCbTWygWUb8O8sc0CqtAc0+9fQ575/Bqvea1kySOjUQKxquN5bYS+61sYk6xod2iq+936elQqlaanp9+6dat169YCgSA5OVkgsMHms0wmO3nyZHp6+oMHD3Jzc1mWdXd3b9u2bR3D5GVkZFAUVaUyPz/f6jOKVZJUlIlmyxU8J5jvy/e+r4lNSnro27tOI1hy8jUnLlqy8jCpi/PLfTnBfhWv+vGCzhQedih4B82C5zu51JUs04FY/beDarKJIXwW83NPo/K5JN8vsUT3Mp/nhKLmpKmE5+zl7mE/lugYANx9ElWwh9HHkQFDCL+kgi9POA/rDuw2w50xgBP8TpWMcki/VcZ7fXDXNxGOtzltAeE1r776T0kz4woUrzvxJwudgGHjf5kF7T67t2FAhx8YlGuD3LQhlfcgC6OyeIfygn5uCzUur4wxg4Aqgiox3Rg9S2v4ndLA3kldmwyq0BzX76Yp3xyz1u/GUKcf+Njbv7VNGhmdMOhWuY7/TF00nM9ve6R90vA76dNjiy6qLnzk8d6bHvaR2Krz3Jo1a4RCoUAg+O677+Li4gICAmbOnNkYXno6nc7b27suLTUaTatWrQKfwN3dHcOwOk63975u6aWiijWjR48GgO9XuDEWRa3dzdl5qa/PUu0/ZniQqDl3OWPqp/pbMXWc2oGDmrGjH/yIESO+/fbb8uKuXbtGjBhhF0lqpV6x6KecUtba7KujS/UPJ5YXac0N/a1WLK2r2IYq+lN/pz1LG1mG0V64WvTTiKLf+hUf+50xW54YjzVnrDIlTqWK/tDf6cDSNSW5sEq2hTpaomdZlmXY9DlxDwfcjFXor02MTnz1Nm2g6ztazRRsy7zf8nLGnPi4HtctSnMNLS35uw0xg1i2kgu+KWm6OWOFbUV6xkmYHHND/PvOPSkMyyoo+uXc/C3FGu3fqtsufzx86WZpmysGY/es3OtGI1VsiX3l1qJtD9cVWYlmb89Y9AsWLOjatavJZBo8eHBKSsqiRYsmTpzY8IeJ1NTUvXv3Vqwxm81ZWVnLly8HgM8+q2kPSigUxsTEPFlfumVXRwFEHKTIVGm5L7rGA4C0fGfWkIgIa8n9WnToFOnrRchl3JbB3JbBhJe7cvshrw5Vze/VFqUjjayDZsQff/yxefPm8uLQoUPnzp1rR3makuOaKdN1wxjNFdS5JwBjTl9I+C6rkjG2YgRTQS+58f49bpvLCGl9TXZU/O5LCS/imr/JwA2AcmiAKI12tJNAUn0w7Ip44tirOA9YyJgXb3ygCz7SDhVg7I7W6dNiU8bcCzzYxibreABQ7MzOX58ecrIDJ4CXszIl8eXbISc74BLrG/W47G0qfw+tPIJJR5TWMLq7dNFFbrt/bSJMc+GeK+JDov2uGpBxrBRDd7pJ3ylU9rpWguAIISs79ejB5ayTusxVqFdKRBujfHrzOPNFdotmX+13xWAwbN269ZVXXgEAgUCAYTYItI5h2A8//LBmzZq7d+/GxMTExMTExsYCQOn7ho9fK2IuqrNUil3hK/YGgNhcgjFUDcf9JOb0bEHPjvob0aVFbngQVaBkLZYqzVYnfpJnyrGRyA4cNDqlyaXKi89Hcqk6cnGcD+dRdBqq8AAgKO466slmpN9qKmcTa8k3py/B5TOr0+4A4IwL14o+0gm6YOJBNMCnKvW/JrNTvdyMWUifGWeM15dqdwBAMMRva0tMhKeMvcearEbfqR+Kndl569JCTnYgfbmWPJPnkkDRENfEYXcoVdVfs0egpP8X5vQlwJQ6c5cmYlmGYMKGC9OMGLk63G2oa9GxwvT34oBhZRj2/X49/VUmr5XA77uW5c16cDmfScRzFOoQErejdofqzuD37dv3ySefzJgxY8uWLTiOz507Nz8/f86cOQ2czNfX9+7du3PmzImLi9u7d29QUJBOp3Nycvr5558bOHId6eHF8RdV+siBAUEQDfGZZkZfu4LHXESEp3vx4XOsyYxwSLq4BOVykCeMjUfKx+msGqQ4cPBM8twklzqcYCjPB2+gQGem++4vKC0GiPBdL1s/Z8akIyz531N5Wy25mzmh+wCsKGOEG4DL3jI9HMdaFJzg7TXIMIjHBa/RkaqXvjNbfizR5VB0lJuUrI+Cp7VU8fFC73Whpdq9TAAccZvmnRR515xl5ATxa+heK8UnFTnLk1tc6kJ4cdImP9CcV4Ycb++5NIguolJG3ws939FqL1TYBRV2seRsIrw/phQ/AWPE3d5oiBjNEQRD/He0SpvyQH20AHCE9OYWbMzgteCHnOhQ8Y+lYpgtxdpBfN5Vg+mGydSFYz8bBasb9926dfvzzz9ZlvXw8GBZ9tKlS/7+/jY8GPjll198fX2joqK0Wm11MtSResWmfpLrCy4BgLOH1PjglVobay9ey56/RhF1gFIX0zp9/hfbVHt+e+qpHTioiB3P4FmWjYuLW7x48TvvvLN06dKHDx/aS4xaqfl+N1JMRjFV+joUq3/rmKK8qNDXdIBNl0TrrklNSTNqaMNQGv3NUEp5vC5yntYb2mbkvJFXaGCeJnS8PlpzL/CS+mhBeU3JtaJo37+KTtsgIb0p0xATcSVvU3rq5JjE4XfURwvuBVwqOl0Y0+Zq/qb0Gjoypiz9v0G0PkF/O4LW/NNwSZopDMWkjL9/1/PPu/I/Hw68SVf+ailp+tXcgtJz9/Lz+CcHsecZfFxcXMuWjzccwsPDyzNN2YRRo0Z169ZtwoQJP/30kw2HrRWKgc8uF6/uLSqvCWgbSGKkJl9Vooqp9SnLqW9XukhbfPisITqeKS4R9OkifvMVqy0LzHk5xsx2zp1tJ7sDB42F0Wi8fv26WCwWi8UAcPz4cQCYP3++veWqNxwM8XEuW0glqlEe/rhYM6igDRm4ERMPqKENggl57aMBrT0SGQ3wp8HoT+JZFJVsoVrV6IFWhXSK+stgGt9GGHy4XdKIuwCQ/JJIfa1INj7eb1tL0WDXug9VHaQ3N/hE+7iu10kPTotrXVEuyhiZlDH3pO94yWbVdDSDkF64+yTTg8GYuD8q7NZwSZopCIb472yVNuUBXWAO/KUdyqt00v2RUj2Ax31fJASAHlzO/6QuHyjU5z3debaIBltfrCv4rl27bty4cenSpaXFvXv3duxofd/mqfHy8jp//vymTZs8PJrUf+BIgqGiguf7O/lwvJL1qclZtMSSjxDuNfQFAFHkAOdX+tEKFSYRPbk5Xw7DMrszN3/V8ntHkngHzz7jx4+/cOHCwIEDSZtGO9m/f//ff/9d3dXvvvvOhnM1HNxtbO2N6qbdF6mKCmn6oMz1ktE0rVC5zU1adx0vQdEjen0BTX/Yxjno13bxI+6cGusy9Ee1//etnAfYxnSXpdncFSn8ds7mLGPhlkzJW/K8tamS0R7FRwt0b8kFnWo6Mya85jLaa4TvMptI0nxBMCRgl/Ww5eulLsIKBpU9uZzTcpldtDtUp+A3b948cODAnTt3qtXqXr16JSYmnjt3zuZzIwjyZFz6RgVH4cxot4o1nBB+oIt/sj41Nseto/4BJqpFwQMAgqGMyQxFWtytWtdRD47n+wELHdrdQbPg1KlTV69ebdOmjW2Hbd++/bFjxw4dOjRu3DhXVxssPesFAoBYO02vjstGUzcOiVf4If7LYOzN49b3h/l/RZpCmt7sKuEiyCAel3URzVCoDru7SbE6Wb8LUXSPm3RyoXJtkWZAOPd/23w/+iA7ZIfNtDsAqA7kaS4oI2J7UipL4tDbBd9myOb4uc/xzf08Jf3d2Ja3a1yao3xOy2O2kuS5RPiEu8STNU2GdQUfHBwcHx9/+vTp1NRUuVw+ZMgQkUhktWWzw0dYSeniUqLDm53Pr7t4L4vH6GMxUb+6DGK8n2DOyHad8VYNbYL4YQ0S1IGDpkIgEHh5PX3urOoIDw//4Ycf/vzzzwULFrRqVUsyJ5vT1ZOUO9Xjh/U3nf5giW691AVHENbCnn3rjkFloY51AtX/GMNDaz1Q0n8tQlR9cHldwPfGMc6jB4WX+LxAghDVJ6eLEEW/d5OOKVAcLNF911veMca/7n3rguQN96LjBWlTYwP2RISc7qD7V+MyQlZypahwe3bA3mqTaTlojlROl8Sy+/fvnzlz5urVq1NTUyMjI7t27arVardt29auXTt7iWhbvr6hzddVyt7ofJQDAA/SLKw+to6D8Lu109+4x1K1ZIH8LfeHf4uuPJ2cDhw0GcuXL586dWpSUpLRaDQ9wiYj4zj+/vvvP8XyQKvVent7S55g7NixliccU63CJ5AW0nocfn8pEQPATIXaZGbOj72jNtMRgU5po6JZs5k1JOKS4RVfCO7K6KIR3MpudhCBcyrvx4YQOF7PHdoEC1VEM3IM+8to+0wwCIkG7mvN0kzqhBjCneMyQlZytShl3P2AvRHCFxzJMK2jNFebX5FWFZX8dV37+1VL7jOXg7GSgl+6dOlbb7116dKlv/76q3///suWLevZs+f69evPnDnTo0cPe4loW27mmVOLKinmYFkQACjSNEydFTwuFRNe7sb7Vp/rH9PaueNdTVPkuXfgoCEsXLjw6NGjISEhPB6P+whbDb5o0SJvb+/69hIKhbGxsclPEBUV1UhZcErTxZIMu//t20V6avCB9iHbWnFC+Rnv92MtKoT0wKSRZS/JMEZ7hfRbBYgNTBb+LbpiYc0Va26ZzHMUqq+kLofcXW+ZTGuLNA2fpQoVdbz2L3XKW/cD9ji0e7Xc09xaED8915T15CXd5Zs5H6013kswJ6TlLfmm+OiFphevBipt0e/atWvfvn3jxo0DgEOHDo0ZM+bw/9m77/Aoqq4B4OdO3V7Sey8ESELoICWAooAiVVGxoSKIYhe7iCA2FNAX/BABRZAioiIIKEoRCL2GlkJ63WzvOzP3+2NDDCFACiRA5vfs4zM7O3Pv2ZB4Zu7csn79iBEjWim260IrIQwXzxSRPDAFdsO5rELebgDMNXDFZXmvzvZ9x6Rp7a9wTLw8KV6e1KxwRaLr79LFmnU63fWoaNCgQdu2NXRVJ5WqnvtjhUJxTSO6CCnA0BcLKi38n4tiR0hIQBAxt13BC2eqVj3iN/51Serf3jsirnwxogNI7V3NrzHLdvrr/M9GBj14d2D1dJw5Hm6qTv+5n7YHywLAN/6+j1dWLbFYJyiv8RdHDBHzfXLuQydy7jsWt76Torfm2pZ/y+Awt6pkSYqq66riJS/GvFv7I95o1i9bF/TBC3RIIABozNbSaZ9IU9ox0Y2+or1OLrqDLykpSU9P927369cPAIYNG9byMV1XPhKiynFRgk+a3ikoKNhqtRZW+TVkYXgv5e29lXc1aIGaXPs5DA1dDkckanm+vr6FhYVHL9i9e3evXr2uR0V79uy5HsVeK7+9dZrPcw5Z15mVEM/pDG6MAUHEF4nOnDtdBRynWwsAmDN4ir+gI2c3vzoMeFXJkrHBj2zVbahpAQ6myO8C/HpcmBpFTRDf+fumX7sGldoQS8SsTG5/sOf1y+6CxcaV6UC4BrPvtZZtuo0BbPCUyNf0Ht1x88HaH7nOnmcTY73ZHQBIlULeK81xtZbdllT3GXxN85d3wMyNuSZ0c/jJCNPFd/CmLVVxKAoAThT6YXtmA8tBLMNENahf0trS7w4ab+j/r4nauPfee69Hjx6jR4++//77J02aNGLEiMmTJ7d2UC1tjsn8x92qiAqe3FD1ha+WQvBSlQEDmP/W249YqYCPPAXTMW/xFM4ifUcSsmvQMvevfhuJyDsDRqT73rm+bIV3pwyhuIsXfFMSRAzdoGbFJkAMwYRfl6sHXm8s/+CroqkzymZ+VTjpHdveI9ejluvNzJk2VawbF/I4gcgHQp5YWfIth2t1AcG47kANggDhBrqda7Xu+61lQopifIeLJnqk/b5M638YAE4fy3BlPW7P0Na8PPnXYNHMB0KeVNPiwy3Rjeubb77ZuHHjzp0709PTs7Ky5s6dq9Vel9/YG23se20hJPlJ75CETWnF72RbVpXP9dV2YRnTNn3+xFOxq1PkPfqQqv7u3Km8fgMdNq351TkFx/qylQ+EPIEA3R0w5qwtM8t2uvnF3igwrvh8CZsYE/Ht7LCvpge+MVm/9Cd3bmFrh9Vo68tW9NYOCGJDAaC9MjWIDdmm21TzKZsQ5TqTw1VUzwIn2By2vUckHeNbJ9b61L0w/PLLL5VKJQDYbDYA+Pjjj2s+mjbtGvxatzolg+rMNU1qifaxJACcPE8jQlK9fjPmBHcpBne9hdSomP2139RHCbn0CsdESKObG7RIdD0ZjcaUlBQ/P7+srCwAePLJJ1NSUl555ZVrXtE1WZTyOnlAIQcASJDHb0jLuudIKMDoACb/qcyYVSny7moAoCPecxzrxoRPR9Q1uPr5rXxNsrJztCweABiCHRn04I/Fi99J+KxRY/dvWFylntebNGOHAEIAwESHqYam2/YcYmLCWzu0RihwnD9qOjCr3Vc1ex4MeXJm9rSe2n5qSgsApI9G++Dw0jc/k/VIQzRlzziiGNiLjYtsvZDruijB9+nTZ8eOHbXf1l5j6tZI8MUWfvVp+0vd/1sEiQ4e1T56DgAcyNcCAjpmHgB4cqYCAOV7lYVoEUPbMo4oB11liAGHuWWFX00If44Qp74R3XiSkpJWrVo1depUj8dTUFAgCEJl5Q034KfFSBLk8evTzgw4gHkcvyHNm90BADHBko7/EJKY5ldR4S77V7/tg4R5NXt6adN3VG3da9jeWzug+eW3Ot5gIrVqqDU4kPTReApusjU2fyxZnKLqkme/qAtqpDTml7IfHw17xvtWMbAX2z7OefQ05riA1ycxUaHTDcYRclmnC5NC6gXhHb3xM19t609Ve4V5JW8ZDg5vy3fVTvCEPDnS3JcgNhXllHiEaIk0nrcdEzxFpGYQobjKZPLyft3NG7ZdNcFTiDJzppOWIymqrtfgO4hE19SHH344cuTIO++8c8KECV27diVJ8t57723toFoPhspviiRxMiQjK78ukndTI6r6f82E9Nq0vq4pWaahfP7Ubai9U0bKfypd3lV9G0NcywmDWwUdEeIpKuWNZlJTPRTCefTUzXX77sFulpBWeSo3Vf5c5yMOc7Xf0kH+9F3/TZB6u1T6bKX+K3+fTgyjF4THK6oGSSWtNVUtwvgG6hHQBBs2bBg7dqzT6Wzg8RigzMoHK/67kzasKy96be+9rjHni/kjG25P7j3VfX4q5uzStGNXWPW5ujSeL3rqreCPXqUCrjKRJI95AhG3Rvub6HqQyWRVVVVS6ZUe91w/drudpmmSJDdv3mwwGO67774bs4NtY//er8yV9YRg+rvOTsHBYw4kKdtIZWzu+BOIIaKXdazJ8dfEHsN2o0d/6X4SkYP8hlLoRvzJN5bp563WXQc0o+4klHL7vqPO0zkhH7+G2Jv+2qUhdjtdr1YZZvpo5pksfSXsK5p6Bny2zN/79eqcecNCABdl9/UVRa9mIco/XhpxHs4fP1iUFL0Qe8xUyAtXze4AgEhSObS/p7j8qgmeRCQGXOWu9GX8r3ykSNTC1q1bN3r0aO/20KFDAWDt2rVjx17l+dQtgPK522M/yXbYBEACAGAofieboNdrx+6nfOMAUMwPybkPnzj/2Mlrm+N7a9OvVVE3LPWowXR4sG3XAcHuYBOig2e/0kayOwDcJmHf9dFM1envkknrze4tps31ogeAt3eabB4M3uz+8jll/0MRc2akpAEAnC7I4y17ARHYesB1eiSv//WqpWnGDLnydDc1LJxpRtbLNt7avPBFomvm8OHD+/btmzhx4r5atm7dOmHChNYOrSWQviOB9uf1GxClQZSm/AujNcOqHbGejfnI2xXXOxsMX+UpfiPL4sYLDot/vI0g65bs/9KEwLenaO4bSkivy2C8G5NeEBaaLINl0j1O1wHXVXpqX1dt7g4eAA6UuostfMC/+qJXzlH+lCc2jgw4FlExEOD8qVweAIzGCXRMb/n5SXTEu1ctreFUlGZU0HiX4JST13E2LpGo4SZNmlRZWWkymcaNG1d7/5QpU1orpBbGRM52nRlD+oxElFrRTyMYVwjO2wjFf6tjOzKtjjO2gKkR5Tb+1yzHM53FP95bkzt7EvaU1/MBqWTjv4UGPzfxPnfvJ2VfVqv2OF3P6/Tz/Hy6tVLrRVtM8L5SQu8QlKftnNmjHuab/XKnw9mjOiQUwA44kUVhD/1bRr9hCXvVfmMJeVpDCsS84M7KY9tdvXttf9/BzQ5fJLpm9u/fDwDp6enbt29v7VhaByFPJrVDPcWfMpEz5WlGAv7Om/JF+Gd65QAfALAfs+SMPRb5ZTv1EL9KA3fV0kQ3MUQAoqngZ2rv43VrsKu44dkdAF6rMtwulTynVgJAbwn7qa/2RZ3+z5DAVuln1xab6H2lRJWT95Q6mQDG+q/JXOZa7fNC+/tO0QydV+os3J/aXrVLrfuRDn+rgQVij6d89te8pUHNdxXusp8vzFolEt0IarJ7Xl5ebm5uq8bSCujwt3jdasGR5c5/m454LvJ//fOeyLT8o7cfs+SMOhoxr536brHfzK2PjpguWA8TbDipTve+CHkn3riNjvqwUeV87qv1Znev2yTspuCA1upF3xYTvJ+UNLtw0CtRQCA6hEkYffp2u+/viontQiQY44N7/ZJ6/y4NfR7RgQ0skJCwsm7Jtu0NWjhOQ2n3Grbr3BXN+AYi0bUxd+7cmpXaP/jgg+jo6NjY2P79+1+nxWZuTIj2o0NecJ8bL9gy6aDJ8p7q6B+Sz0/IzB5xVPVJ4pow2f8dsf7fEeua03aDU/Bu/98R64pM+809AEl0MUQHUMFT3AXv1+zxFM0mfYYTsg6NKkdF1M2ql+5pMW0xwb/YTdHRnyZDJPF/dHbnO6lgZsD47A4P9+gUxwDAv1FOOVTSQU83qkzl4D7mLTuhAWMOGYKdkTDPjwloYvQi0TWyYMGC+fPnv/nmmwCQnZ09c+bMb7/9tqysTKlUTp8+vbWja1FU0FMABBP1IRAsACh6a2JXp0Qv6cAN8jG4BJMbm9zY4sECBu+2yY0r7fyNNOm4qLkcRzt7Cmfw+g01U5VzZYu48sX2A2FQe/75m0pbfAavYom3d+pn91e3D5PE/9E5a8hhdyRbAUxk8QCAnyrO7cKIwtiNgG14mWxCNCGVOE9nS9pffSoMKSnjsEfv1gWwwc34HiJRs6xatWrRokW33347APz444+dOnXydp6fPn362LFjv/rqq6sVcAtBjCR1b+0d3gnslACv9ahubs02cIfL3DVvRbcYKuARwXqQ8rvfUzhLkvKv6+w4UjNYcGR5n823dnRN1Bbv4E/pPOU23nvxzYRJhN9SKnPsKh0X6RoMAMfPeI7RaSWG3Y0tNuDVp5iohi4DrHNXzM5508HbMeBztoYuYScSXUNHjx5NSUnxbv/111+jRo3ybgcFBZWX19edWCS6ddHBzwj2M0DIEB3oypqAXUWEqg+v//WarC3UWtpigrdz2OjENYvGVgXR/1sbk/97x2RTOElRuedtx1AXbG70rL1UgC8ha+i0REFs6JighznM7dH/83H229m2M42tTiRqJo1Gc+zYMQAwmUx79+4dNGiQd//58+djY2NbNTSRqMUhhol435P/Fh35AW/YREd96Cl4lw59FVE+rR1Z07WhBG/3YJNLMLmEOC3FCfhAqcv7tsrN3xuhfizJVxGmjotthzleVxgksfx7veO5zWcgTdA/l63opU1fWbIYg/hAT9Sihg8f/tFHH+3Zs2fq1KlxcXGdO3cGAJvNNn369IEDB7Z2dDccHwnRI6StzMXWNpE+wxAbJlj2STsdAt6BXUVU4GOtHVSztJUEjwGG/6QbsLJywMrKgSsrMcCiow7v2w/XW2KNNABIk+Q9IzsBgDLbLXfnYa6q8dXgomenCzZ7Aw//qfR7HnMZxh0Y4936unNii0TX1cyZMxUKRf/+/TMyMhYsWEAQxObNm8PCwsxm8+zZs1s7uhuOj5SY0Vfd2lGIri86cpan+FNArLvgPTpyVjOfvrsLndjTmndubSXBI4C/HvA//Hig9wUAz3SWe7cPPRbYP4IFAN/xIT169ACAjP0HCGV3wbyn8dUgSVKcZWuD7v4r3eX7jf86eHsHRSeEYF3ZDw6+oVcGIlHzaTSaX3/91WaznT17Nj09HQCSkpJ+/vnnjIwMmUzW2tGJ2iIBC3/qNrRicyYhTSR97nVm3klIE0nNoOYUZdllONU14/wjJ1oxx7eVBA8A35+01YxhBYDdRa6atwVmHgCUg3y6tu8KADsyMghVX77xj+EBQHXPQPMfOzHHX/XI1SVLoqSxKaquL8S8AxiCJWEbK35qQo0iUXMwzH/NzpGRkQMGDCBJ8grHi0TXz0791lXFS3bp/2rFGOjwNxAhYSI/uPSjn2z2g7XmlscAC8yWAq6eKQ6te43nHzkZ80MyAOSOO4YvdPlqYW0lwQsA8w5a5x2qfgHAoXJPzdtdhS4A8BQ5JbM4iVJZlpenc3UQTE1J8ExECBMWaNtz+MqHnbYez3fk5DlyxwY/AgCp6m7lrpJd+j/LXSVNqFQkEoludjbe+mvZ6icipq4vXdmKzZmI8pGk7kOSeqYej6So53X6/S4XAGCADwymXQ6X3yUXxNa9STQNdgAAIABJREFUxtwHT0Qv66i6wzf6+2TEELkPHm+VHN9WEjwBsGt8wN6Hq18AMDRG4t3e/0jgQx1kAMBES/kKrkOnLgCQcdSEPZXY05T55jQP3XvlpZMELKwsXoyAiJHFn7ae2KHbuqNqCwW0hJCtLf2uSd9PJBKJbm6/la3urO7RWzsgVdV1Q/ma1g6nHt1YZr6fz0s6w16na6bBdNrt+cbfV3bxNLQ12V3ZXwsAiEatmOPbSoIHABmF1CzhfZEIFEz1WwVT/c+DSBT2YfyA224DgD179hKqXk27iWdjI2Tdkq9wgABCiCTczJmUlDrPkZ3vzElUdAyTRBo5g684w51IJGp7Sp1FGcYdI4MeBIBRweN3G/4ucxW3dlD16MIyc/18ntHp9zld3/j7Koi6k8znTzwVMDncm929EI0iF7W37Tcb1rf0DOVtKMHXRpGoS1A9I16U/bX947sDwJ87dpCqfrx55/WonURklbuys7pnirJLe0Vqe0VqZ1XPntp+0dJ4G2e5HjWKRCLRjWxVyZK7A8cqKBUAqCj1kIBRq0uWtnZQ9cAAf9gdYSRlEIST7nrWeo/+Prny60LTpv9Wc8AuIW9CpjJdqx3T0PVNrpU2muBVDOEnree7m7frY3eEUgR94tBhM3TmjX9BU/tzmjdud2Xn1/sRhz0BbDCPuYOmPbVfpa5Ci5jgRSJRG3PMfFDnqRjoO7Rmz+1+d5e7Sk9YrtKZqYVhgJkG0ym358dAv/l+Pq9UGTKcrjrHyNKUses6FTx72pvjsUvIHX+CkBBRSzsiqqXXlGuLc9EDwKz+6n8KXH3C6s42z4RInL+bEwM7ZpYe+WvxuXvulgq2E4Q8pQlVEAqp4Ydfg6ZPvfQjGjETI168dH+hIy/HLk5pJxKJ2hAMeHXJUhkp/6H4/2rvl5Ky1SVLkxM7t1Zgl/rabDnl9nhb5ruwzOd+2hd1hhWBflHURZlUlqaM+TElZ9zxiPntdN8Wkxoq6tsOiGyFFWPbaIIPVRDHK+ouEOQpdxe9lQ0YBvTon/nLkd8/+WVI336UcUvTEry8TzfT2s3OU1kNWX7GK1waFS6NakJdIpFIdJNCgIYH3ucS6t4KR0njJGRDJ/9uGcNk0seUiprF3buz7MpAv8D6hpXKe6hjV6VkjziqutO3tbI7tNkE386XXjL0ohmGPeXurKGHfR8Iqvy/wtTtEQBwLPB02cf9Qt5aQ4e+2oQqEEmo7xtqWLkheOZLjTpxR9VWD/bc7jesCZWKRCLRTaentn9rh9AgEVTdjBl5yZ4a8h7q9od7UgFMa2V3aLPP4AFAyfz3Q8ccPnfnIe3YQMElYA++861hLCU5mZNJTxwk2PIcx841rQpF366Ur0ZwOBt1Vkdl2h7DP02rUSQSiUQ3CDqYbcXsDm05wb+4zVhmq55vDlFI0Utd9X2JaUNF0KtRQi4f2buHgIX1U9bZT/YmFH80sQ6E/F+ccOUx8ZfyZfzfjf+siTWKRCKRSAQAbbaJHgB0diHbwAXJqx+f0IEsb+XpQFYzKkBe6Oq3Of3czh37TIdGZg5RD1oB8HwLh2fmTPsMO+/wv6eF6xWJRCJRQwjWQ7z+93o/In2GEYquLRzPpdruHXyokiyxVt/BYw5XrSgNeSNamqrIeyzTusdwZ0ESABzzP2X8PVZwlAuOJrbSexl++BW76hkxeQUswW6v2nLGeqI59YpErQhjvGTJkuHDh48YMeKPP/5rBistLb3nHvHK9cYlYJiy1dDaUVQ7p+fO6uuZ7P1GgDmjp/R/QMqBUv/3IhWe0gWY07d2dABtOcEHK4gSy39N9Al/dC6fVyDvquJi2PxZuVG7g2ip7GzeOXa670mfYX8WrWxOXVxFlWlD41aDZQnJ2/GfxMvbN6dekagVzZo1a9q0aampqaGhoePGjVu3bp13v91u//33+u97RDcCHuNteXX7tLeWzbnOzbmO1o6ifqRmEKlOR4SUDnmh5oVIOanqRWoGt3Z0AC3fRG+xWH788ceTJ0+Wl5dzHBccHJyamnr//ferVKoWjiRKTR0t/2+kHBsni/89LevuI8FvR1eVuk5roGtRyl7ImIUy/Ni7ZhqmAUxvcl3a8feWTvtUMaAn5atp+FlSUgYAx82HYuWJclLR5NpFolaxaNGitWvXeheiHT169PDhwzt27JiYmNjacYlE1wwT9ZHz5GDSbyyiAwAAcwZP8Rds0i+tHVe1Fr2D3717d2ho6Ndffy0IQlJSUnJyMkEQixcvjoyM3L9/f0tGAgDD46Vv9lbW3sPGyeI2pJV8kBvbWZ22yy65dxAAVKx7Z6blY8Jd4jx+m+vkYO/LeWZMo+qiAnyVQ/oZlq1rQpw59rPfFS5owokiUeuyWCzx8dWTQAwcOHDy5MlTpkwRhNZZN1Mkuh6QJIb0H+cpnO196ymcSfqOImRJrRtVjRa9g586derMmTOnTq07udvy5cufffbZFs7xCIC+ZJ0ASbxM8Wvq2RFHglOVwc+NRqtm7dlTVeKRKchALW8FJhgABOtBhBs98kE98g7dgpUgCEA07qLq3sBxxa6CxlYnErW6bt26ffDBB19++SVN0wAwY8aMLl26vPrqqxMnTrzquYIgbNiwwX3JXN+HDh0SLxGuhyqHMO+ghccAAALGPIa3dpq8H7Eker6rQs223N1goZkvMFc/d/du7C6qfmQQraFCFPVMLNOK6LBpzmPdBdujQEh5/QZJakZrR/SfFk3wOTk5995776X7R4wY8fzzLd1NHQCG/6T7ZbRfnSwf3U65YV9a5YhMn+KAyHaheaeLHz074qe7e8K5MZLE1YKrxHm0Ex23sLF1IZr2f/7RJgRJICJcEuUWXCWuoihpbBNKEIlaxcKFC9PT01euXDl37twJEyZIpdLffvtt6NChy5Ytu+q5DodjzZo1Hk/d6SZLSkrEBH89SCnU3o/2rrzBY7wOnMl+tPcjhIBt2cHcf+Q6/i2qvrYrNPMYcIW9+h/9tlDm6bQb63klIpV02DR3/tsIUXTYNET5XP2cltKiCT49Pf3111+fO3duYOB/i+pUVVW98cYb/fr1a8lIvMxuwe7BCqae392sHrK4/ZZRvZyfn4bkn57RxAZgweY4GIEFFyDgij+j/O5ryVCtvGVu7oyXYqZHSKNbsl6RqMliY2NzcnL27dsXFBTk3RMXF3fy5MmNGzceP378yufK5fIVK1Zcun/Dhg1jx4699rG2eTIajUuSebc9Av5gt2Vce1lrBTOxk2Jip+rt+QetGPDzXZVXPKOVUf4Pc2XfYuCpgKbcxV0/LfoMftGiRWazOTQ0ND4+vmfPnr169UpMTAwKCiooKFiyZElLRuL117iAS7N7Acftcboevysscb9tVLoAAOu3uUw8C6QaCxxgHhCJ3WVNrrRq0SpPSaNXBfah/V6MeVdNNaKPnkjU6hiG6du3b82T+EGDBjEMM3LkyPfee691AxOJriVEMvFLmPilgG6sqWVaNJqAgICNGzfm5+dnZmaWlpZijAMDA1NTUyMiIq56rtVq7dat26XP5Gw226XteA1E1Xd5E0FRqwL9fiKtoadc9s6T28V8ciaXO3SYv2dSpv1AKKnowtuOU+FvNK1GAKDDg3VfLQ+e+WJjH8ZHSmMBoMxVrKI0MlLe5ABEotayZ8+e1g5BJLouCGlDFxVrSS2a4DHGS5cu/eWXXwiCePrpp4cMGeLdX1paOnHixA0bNlzhXIVCsXXr1ktz+d9//z1lypSmxfPzWUeFnZ90yROdtTb71x7b3GRFR91zw29feGaRYfWm4sHpoxEg3pmDaD866Kmm1QgAqrv62fccNv+xUzUsvQmnHzUfOGo68HrcrCYHIBKJRFdAEWhy5xvlFsJXSgi4tYO4abVogp81a9a8efMmTZqk1+vHjRu3ZMmS0aNHQ4MnvggPD790Z2ZmJkJN7AAipdHRSxaNLeC4RWbLMn8/apDNccLd/tF30Tcv/rKdn1e5WxF4O2/8m06YL1gP85b670Uon3uQ5Ipd4RDynTK+7M050s4d6GD/xsZ8p/+9AUxwY88SiW4ECxc2uneqqOUhgBvnmfdDHVqtK8AtoEUT/I028UWMhjpvrDsJYgRF/RkcCAB4WjRgGADPdmj/zslM619n7rhHvh/R/tiZI3BVXPEcKvDJ2idiwc6VfUMqb7tKggegg/z9XngckU3pAIEAdVb3AIAs2+k4eTsErblUkUjUKI899lhrhyAStSEt2snuRpv4IkpNFpp5/jL1IxKdu+NgiAUrR08AgFVr/0K8k4ldwJUuIH1GIjaSUHSmI96reSFKS/qNIpTdGlK1NCWRCvBtTvDry1b8Xr62OSWIRCKR6BbWogneO/FFzXP0GTNmlJSUvPrqqxzXOmsJsCRq70dbPJe9wqACGMtf+sEPTiFItHmXySZ7gNQMIlR9uIrv6KjZ7vx3QKhe6x27S7iyxUx44/oGY57n9camBf9c1JupqtZfrUgkEolEN6YWTfALFy7cuHGjr6+vd1Ccd+KLDRs29O7duyXDqO2nkb6ay8/QpB0d6Mp3jImODu/R3enGGw+2BwA67DWu9H+kvDMhT/aUVj9TdOe/QwU9hdh6eglcAVemK3ntE07XlIWbpKQsQhrjFlwHjLvrPcDCmW28tQkli0QikegW0KIJ3jvxxcaNG/v27evd4534YvHixdOnT2/JSBrI574g/4lhHRn63ScmAsC3S38AAELajlSne0rmMZEfcKX/w+4ywbJfsOyjQ55rbPl0aKDq7gGVny/BHN+0CDHg38pX7zFsv/Sj/+V99H8FnzWtWJFIJBLd7Fp6udg6E19497TixBcbsh0zdpuvcMD58ScsOwxj7rtPpVbv2rXr5MmTAECFPM+V/Z9gP0f63OnOnerOfY4OnCA4zgm2oyA0bmVD9b23k1qV/ts1TYufJSRvxn3USVX3wf9B054CZ2629cxx86GmlSwSiUSim1rbXQ/eK0JFlVn5c/rLdgJQ3emnX1WaTTOhY+8DgAULFgAAr9+ABbs7ZxJvPcmb/hHcxZx+gzv3BeeJgbxxW+MiQMjvuUc8pZWOo2dcZ3MFZ6OXYZaSMhkpL3MV/1q+CgMGAA92/1C0SE6qJIR0WfECDjdxIiCRSCQS3bzaeoJPDaB9pMTaM/bLHaAdHWDcUJkkkNRDDyOEli9fbjAYqKCnEakFRLHRH0va/y5N3i5J/ocOmUrIk0mfoY2NwZNXzFVUGX/8Tf/9L8XPvm/be6QJX0RL+561njxhPgwAmyp+dguuR8Mmjw+b5ORtf1VefY4BkUgkEt1ibqyJc1vFuSpOQl92NDkdxMpSlK4/9UP7JMPAQSe2/bVo0aJp06bRYa9x5ctcOc9JU3YBIQHB6S54n41d2NhrJsHuqPxiie+kB6Vp7QHAdb6oYtYCJiqssXPgsITktdiZAGDwVG2u+CVWlthRmQYA4ZXRv5av7uWTrqa0jSpQJBKJRDe1tn4HDwDnDB6r+0pj8bVjAw1ry0bIpJqJTwPAvHnznE4nFfg4IECMn7twJgB4SuYSiq6EqtHDAVzn8ujwYG92542WitlfS1OTHEcym/pt4JuCuW7sGhH8gPfto2HPYMA/lSxvcoEikUgkuhm10QSfZ+J2F7m8Lw8PhRa+5m2huW6Hds2IAPM2fVeeIm/r0yEtrbS0dPHixYBIOupD7CrmdWt5/UaubDETMb0JkQg2OyGvnouR1Ci14++1HzjGleua9r1y7GezbKfCJFFF9rxTlmOnLMeMHn2Con2GYXuB43zTyhSJRCLRzaiNNtGvPWPfW1zd9cwjYJMTz9lfPWS8fyT7fJeLlp+htHTEV+0IBCMU8oMvvZz58PiPP/74qaeeYlX9CHkKIpWu7IlU0JONHQTvxcZG6r/7uSbNy2/rbFzxq33PEfXIwaRG1djScmxnBIwxCDv0W3ngPYJHQkh44AWAXPs5cS15kUgkajvaaIKnCcTj6iWKEIAAUPNWUl+jhup2X8Pa8tEPBa5JH5icknLi+PFFH6VPfLAdCG7euA4w5it/dDnzASE66JkGzlbrRQX5KQf2Kn1zjvKufoiirNv3se1imOgIx7Eziv7dG/u9tLRfkjLZu+3G7nJndrA0TEVqVEqNhhafwYtEIlEbgjC+uZfi27Bhw9ixY51OZ5NL6PV9hd2Djz0ReIVjBBt/IvHf9od67VXjyk0bx4weHehLnNn3uUIhE8z/IkrDm3YjJoC3HpIm70LShMbG4Dh2xnHwBPZwkuREee80aOr6eHVYOQtFUBJCek1KE11XMpmsqqpKKhX/sa6k+X/vItGNoGX+3tvoHfzaM/aahWJtbgER6K2dJu/brkHMyIS6P3RCTmpHBepXlfV7PgJGjerdNXLPwfyPP3r/3WciLxzCC+Z9AIJgP0U2PsFLU9tJU9tdut9TUo5YlvLVNLZALwWlBAArZ557fuYTEVOD2bCmlSMSiUSim04b7WQXraGS/Wjvi6UQS0KiT/WeKHX9Fz2hM+N87g8EgPkmy3OffYUQzFturNR8J007Ik07wib8AJQcEImFyw6pbwL3+eKyt+a4C0qaU4iCUt3uN0zsZCcSiURtShtN8F2DmHHtZd5XO19azRJmF/a+TQuk6z2FVFG6pSWmP3QBJPFvh96jhqU6XPjV5+/3furOfx0hOR31madwFqdr4ryzl5Lf1ln78IjyGV86z+Q0p5ye2v49NH0BYEfVVjtvu0bRiUQikejG1Uab6GtLC6QNLkp9+TXlasg6KUtn5Y7Y1e0rk2X2nJWbtyWv2XBm/LoFQ9ODwJlPyBLpwMdIVS/X6REISNJvdDMD8xR9IjhOUwHgO8niOHI/dsQSCu+AOsREfYTogCaUWeYqXlTwxQvRbzczNpFIJBLd4MQEDwAQJCce7ii76mHqu/xKpue4t+kf6a74TcK+/9bjr7z97cTJLx1ZG62WO5nI2QBASBPZpPWu06NoTwUVPLl5cWFsP02HvU75AOVjISQsYhneksEbtiBS3bQS7w953Lth9OgVlJJC9TdXiEQikehm10ab6GvrHcr2DGEKLbzFfbUBBQg0IwKK38l+WCHPcLmGvvxV91SfskrXW1/qSP8HCFmS9yhC2k7SYQtX+YP7/EuA3U0OjA55HgsORAeQviOYmIepkPtIn3sE4y4q4C0g2CYX67W9asu7Z19wNnLtO5FIJBLdLMQEDz1DmS5BzPcnbGsuv+SMl2mTruKrAudpm/X/ih5TKr6xORZ/+y3LoKU/6/44nFz7SMSGsx02Y0+FM3Modja1dxshYSLec+e/Abh6cj2ufCl2S8umH3EcO9PEMi8YEfTA05EvsYQEALxr0IlEItGNDWNXPnbl1fPi9K0d241ITPDVOgUyB0qvdLdt3lqVPzETMHAsUfB61uh8fNDlZlKGvT9tMMbwyBNT8vLyah+PSCWbsJzyG+vMHMxVrmxaVKTvKCAVXOUKAMCc0VP8Kdtxnv+Lj1UtXKFf9jP2NGsd2EhpLAKUYz/72umJufZzzSlKJBKJrjfszHUcSXNmDnOeGnnR63hfd84zrR3djUhM8NW6BzMHStzCZW5lzVur8iacBITifukkzI/zsOjc3Ue+KGdoQEeeWR4zZIjZYBgzckzVvoqLz0NU0NNs0i9c2SLXmTHYmd2EwJjI2Z7CWZg3e4pmkz7DCVkHSfv4kDlv8AaTbv73TSiwjlhZ4sSI6lt5kUgkumEhSSzpO5IKeMQ7OLn6lboHKA0d8kJjS3NwuNBSd+WRW4yY4Kv5ywiNhMgxcJd+ZPpDd/7xk4BQ7LpUeXf1iSGaXz4ORSaOv/f45/vLVCTh/9kXUVGxh44eenjAQ8bNddeJIWQdJB3/QpJo54kBztMjPWVfcxXLar8wV1VvSLxxG1exTLAdRtJo16l7+MqVSBrFVSzjKr4jpOD/4uN+Lzx+Tb57vDwpVBIBAB/nvPVz2YorH5xpOSo26YtEolbBRH7AlX2DXYU1ezzFc0jlbYSyZ2OL2lno+jTDfE2ju+GIvej/83J3pd4pPPen4cs7Lpq2vWJ+geAUEjamyburAeBJlWLuqKDcNca43Zb4n/TfvOT/jSqkUj7jIXbiH46/po6b8uWK/6mH+V1UNKKw8zzGHmw9JJh3AalCpAoAAXiwq5RlwknNoEvjcWc9iXkTAAGAAQQAwpP3HoAAgBEbSarTEVl9fWb4fj0dGaLo172Zc9xOjnzthPkQXHgqj6BuaWcsJz/PnT46+JGhAaOaU5FIJBI1AWJCqKAn3YXvs3GLAQC7irjyZZLk7U0oSsBwuSbbW4Z4B/+fobGScjufUVz3STzpQ5NKqmJhEearfx2eIGS0VRAQlMtQihlc957oNr7Xut9+ZhhmuWnNG+NfM2285D5elQ7Yw4S+SEfMIKTxwBkJeSdE+wNBEvKUeuPx7qcj3mNi5jPh7zLRc5mYeYj2AaBI5UXr0Mj7dLH8sbP0zTmuM7nN+QmoKPVtPgMB4KTlyKunnsq2XdSVDwP+pvALimB+LV/l4K/lhH0ikUjUQHTIC4Jlv2DeAwDu/Leo4MlInIT7MsQEf5FN2Y5LB8tFfdNekiiz7jHmTcjEPLZYuH/uORRQ4t4zSuOOZB59MGfnYGXm88GDeg38Yf5yiqIWGpfOeOTdOjme0g5GpMZTvoSQJdFhb1CRs7CnUrAeRUwottffJZ5N/AEQ4nSrqICHqNAXqcCHgfHDHj0T+jIQF43aZ2Iigme/ohrav3LuUtP6rc3/OSQrO0+NflNL+wKAzl0hYB4Atlb+ZuKM7yXMIYH8rmhB82sRiUSiRiMkTPh77vw3BPMewXaYDm5E9zoeg8kleF92D/YIuOYtL1y/iFuN2ER/kZM6z6WNNoSMjFvfKXvkUetuY/7Tp3LPWXyK3cf7KgYs7FD+UKa6yL3nMd/t+frAiUUJ2YEL3/zq6ZnPfKH/mn1F+uGwOTWFeMoWAfZgV4kn7y3EBAIAtp8ARGFnnjv3WSBY0u8+ym8sYiP/q5hU0sHPeUrmC8a/Cc1AwLwn60lEKqjwafWEjpC8bzdZz7Rmdq2vESGN8W6sL1uRZ895O+GT9WUr+2oHhbDh40ImfF+8sMRZGCIJvyZ1iUQiUcORfqM85YtdZx9gYudDY1bL/PGU/fP9Fu+2RwAPLwxYWel928GPXn6Pz7WPtVWJy8XC7iLX/x2tnp49S++ptAu9w6qnkdGwaP6F5/GCjc8afsRd6HQLOKOvvO/CDikSFni8t92/bkGAYDa3veTe+8KLnsjcELvtnX8+wIBfe+21jz76CCEEANhT4TzWm2DDeWeurMsZ3viXO/tJQns3ALDx3wrWg5xuDV/1K2ICSe1QUjv0Qru94NgfCQQp7ZrrKfzQU/w52241qbmjId+rct53gs2uGpouTW3XzGfzRo/+p9LvD5r2jg1+pI/PIJaQTDvzNIskMxLnNadYUW235HKxmZmZ+/fv79ChQ/fu3TMyMlauXGm320eNGjV06NCmFSguFyvyEmwnuLKFTGzTmxL/yHVuzHZ8NVh79UOvg5b5exeb6MHFY6NT8L4EDBig5q3d89/VDyEn439LY2NkjsHa/gs7pkhYAAASdd7SRVHJqXNcx98Mm9eFVNzjf9fB/vOe/5Km6U8++WTcuHGHDcZPjGbsPE+oemLBCYLddfYBV84zQPoIhi2EvAPm9ISiKxP1ibTLaSbqUxAcrqwJjiMp7vMv8/qNdPQXmDO5C973lMxDssQGZncA8HvmIXnPToYffi1+fqZ1x77m/Ijc2J1h3DUy6IFztlNbKn8FgEkRr5a6Cg+a9jSnWNGtbcWKFampqTNnzhw8ePCsWbNGjBhRXFwsk8kefPDBpUuXtnZ0opsbIU9uTnZvI8QEDwiBnEHeF00CAsBQvUdCXfTzIeRkwubOfRZ07ChhvHt4M5f3RKbPmEDg8ZOjszt+XmrfY1r0WWjaug7LP1ypUqnWrFnTp2fPradO8YY/BMt+wV2C6CDetBMEBxAUkoR7Cj/Enpqn9QSh7EFHvC/tdJBtt5aQxHAVyz15LwHBciXzAfNs7NeN+F40pRjYK+Sz130nPyjYm3XHszDvU5ZgWELaXpGqprQ7qrZuqfyFQsyyov8J+FZ8ciW6FmbMmDF//vycnJzTp0/Pnj37iSeeWLdu3fz589esWfPpp5+2dnQi0a1PfAYPHfyYhztUb/+SZS+zuSQkPNxBDgAK5iot21U/lHrK3QmbuzhfsJ+9/WD7DThhc+eRKmHvYVv8b+y3u3Y9PGaM49zZfXcNXjHv0/tTCCbsZa58BVBKQtFTsGcSyj6Esjci4i4tmZAmEtJEKngKYA+v3+jKmoCYANfpEUD5kIpuhKIroehEyDpCAyaokSTFSpJivdvOk+eqFq2W9+ki79eNDvIHwJ789zBvufQsRPvQ4e94t/WeSpfgWln8Te0DBMy7MXIK9vP27BBJuLdHnkhUo7CwcMSIEQAQHBzcrl27gQMHevd37dq1sLDwiqeKRNcdTQBJNOvZ5Y1PTPAQICOGxlanyeOV7u357hwj3yOE8ZVevXnD/+kw+0FzzrjjMT+mpBb1c+U5DOsqDnOO/v/Ypi+LKPGhU37fFPTu27+tXv34hEnrBnf64rX14SGhpO9w3riNCnqaK52Pqb9Odd/p90RI2EcJ9deBaNJ3hIQNI+SpgCjBcU6w7Besh7nKHwTHWUISQ8hTCVlHJOtAyJMRdZVOIpKOCf4vPGrdsb/s7S9ItdJvykOCYx+ifEjtnbUP4/Wbas+790nSIg7XMwUQAYSUlJ2znVpR/M2sdl9dOm5e1JYlJiauWbPmhRdeAIBt27bJ5XLv/r///rtDhw5XPFUkuu4GREp6hDR31a4bnNjJ7iJLj9s+22cdmySR08SrPZQNOQXzOP+pU5zBE/NjCnYJZ/oecJe6PlwffSaK4QCHkeRPAf545BMSAAAgAElEQVRL7/703S0zLdiqlJMfvDFywqBdtDIVkUre1vfcnSmUH+0pc/s/HRb28WVy/GXrdgv2U4LtuGA/Kdgzsf0UIJaQJSFporcBAEkTEO13mXOxKyuPDg4AItt15n4sLAMkl3RMICQs5kzOY93ZdqsJeaer1O/xAACiq9ecdQqO109PTve9c0TQA437IqJbsZPd33//PXr06ICAgO3btwcHBwOA0Wh85JFHtm3btmrVqnvuuecK51qt1vvuu89zyZAQnU534sQJjqvnclMkuom0zN+7mOAvsqPQOXO35bu7fV7fbvr+7oYOmcAcPv/YSewSVIN8yufmC3f7vfCI3AE4Plh20uV5clb5bZvMFfryT/kFfzq2A0ByUsin08f2jdqdNfxDJlqV+E83/crSwpfO+j0RGj4nsTnxY3eJ4DiD7WcExznBcRo7sgAwksQR0ngkiSekMYiNISQxQMprn+XOmcJVCJY/27uy8pnIUHn6QTYmgIn54goVeYrKKuct8xSUAgI6MtR/6qN0aCAAmDmT3qOLksYWOvP+0f0xwG9IuCSqOd+o7bj1EjwA6PX6jIyMfv36KRQKADAajfPnzx8+fHinTle7dsR4165dbnfdWafy8vI++OCD/Pz86xWxSNQixATfINc2wRucws5C173xjf6he3O8/aA5bFPaHaxZY+Y/HZZ7bLSP0+hpt8Voi2AHfZeaPfLotpIdc6hZBeU2AOjpE/ds8KsPHHySkBAAoFtSfE1yfN3APDrszBYcWdiZIzhzsDNXcOYiUoUkMYQkCrFRvCUDASdYDhCagQAy3lQOfAbpM4RgQuioj0w/byF9NExMOB0aVDMzruBwFk1+FwhCdVc/wNi8eRfCQujCGYT0vw4BLsG5s+pPfzaok6pbviOnzFWSpurBEMw1/Gq3mFsywdcxaNCgbdu2NaeEU6dOjR07NjMz81qFJBK1ipb5exefwV9EKyFqsvuJSk+8lpJQDXqujCgU/V1Hwc67FGR4uW1+vH/gXn96yCFXtkMfTK9bEjM0VpbwZ1e4A/qRkb+PfnrOMmeGPjtD//SiO1e8+uqrw4YN85sQKtiFojfPkZ0UIQ+H1i7cIghKoonjHRDth2i/OisxYHcJdp4XXHnYeV4w7wBAgDFv2AxAA+YB0bxhCw8C6XsvqbI5jhWZft7KVRlkXZP9X3wcAKzb9mC3O3zxbEImBQDV3QMLn3rT+k+Gamh6TRUsIbnDv7oNlsf8fuO/KkqdpEgpdORhEGpm0RG1KXv2iOMqRaKWIyb4y1qZaY9Qk5PTFA08HpGIVFIygJ+D/AHAUe5wZzkkcdLASMnUYVnlk52qqeEJf3Y91Ie/P3/aXWnRS45tW0Wu37lz586dO4Nj416eNKn/tq4kgab5ukbZ7CPl1ZPRrrbZvzZb/gkOvIZfDTEhiAkh4DYAzJX+DwtORKkwb/F2kkMIsOBGiHIXzCB9i+Q9KxR9fRAVAlDqzs8imHCuIpMK0JW98zqljSYDg6Wdkuggf9eZXKiV4GuLkSU8F/WGd/u8I+uvyo3PRL0WxIact2epKI0v438Nv5pIJBKJvMQEf1nPd1Oc1jWjLw8BgIC3cYnzkngLp19V9mBhxcNvlkSaOOO/vbftS352XMxDxWPeeH7/vnVLSnOyX3n1FQaYIX3vMlSMmlGejgP9R8lla232jwymNPb6tWwjKvRlT/FnpGoA6TfWdW48226Np/xbbPqHCnuLDpkKAIB57KnArkLsLsbuUsFdSEecI30KmDASc5UgyMHjI+vBkdpo17k88+/HERWApKGkPJzt0E2amuytBvOCt4W/n88d/Xyq5+o5aj4QWzxLChwCxGEPAQSByAs/PZJN+PFyy/CIblILFy5s7RBEojZEfAZ/HemWFhe/m0PIiMStXZlIifGYOevOw+AUDAmSaSsiXlxvj1pcThs55bZOe576/rvDKzK4Q955Yxi1WnHXkDvGjj2b1iVZIf/G35e92lyzJTNyFL00qjsaPxhdcNqPpAKnk6RmYGcBYkKdJ24Dyl+WdgyI+seQcBVVxVM/kKQmasbcBUKVectvXOkp3ycHItYiWIsEWwF2V2ChEpFmRKsR5QfYx3G0TPDIMa9FhC8bnyrr3h/RAdgjdx4fCugcYgcWQakNbEmKVAxgtRyQuktkXY4jOrTeAG5JbeEZfPOJz+BFtwaxk12DXO8E/8k+i4REU7sqxq7X3RMvfaSj/Orn1KJbWlzybjYhp8LnJOZPPoV5HLSo/R/9pCvLLQM/K+uQYQvNcQEAEOA/MZwfRa7P+G3lypUHDx70nk7K5cPuuGPYkCFDhgwJD7/syi55T50yrCrFJIpbnaK68zLj4i6P1//qyn4asTHS1D3OY92wq4iJX0pq77rCKdbt+/WLVwNJAgDwvM9T9yv6d7/0MOzRYU8l9pRjTwW2lgi2IsFZikgLkEbs0WF3FWAPIIwxAowQIoEkABAITgBwHB3mssf/3r4sQdMtvecEAOCNZuuO/YRMihgaMTQbHU4F+QEACAJXqQcAJJMggiCkEmhql4VWJCb4hhATvOjWICb4BrneCd7gFMasr3oiVf7ZPkt6BPP5oHpWJqiwCwGyy2aUysVFpe/n8BaekJPRSzuqBvsCwMTKqj0ul4Dhbhs14ck8Rzcl8uDgVJWyu/rswIPZPuVzI3edLNhuzjpVU05seGy/Hn3T7xqQfkd6REREzf6CKaerfiiN+r6j6fdKw5ry6BXJmrsb/VTbefIOwXqYDHiYr/ieUHaXdNh81VN4i9V15jwAsO2iSWVDeyrU4cp5gdd9D0DSgU9hwYZ5q2DZD+4ijEhExmLBJAh6QEDSfgKpynfrGZc6wBSM3YwJCLVfe0XH7ojSekod+sWbBSfLW9wgYNWwdM19QwHAlZVXPnMBoinEMgCgHNxHfe/tAOA+X2RYvt4bAGIYWe/Oin7dAMBTXG7+/e+a2KRdOsq6JgMAV6m3bNkFAEAQhEzCJsZ4ZwbkDSbrjgMAmJDLAICNjWBiwr0/GXvGsZpy2AR/OogBAMHudJ3LlXRMQBTlrRsxITWHiQm+IcQEL7o1iAm+QVqgib7AzI/7tUrvEG4Lp78dUrcNvNzOD1xZmflk0KUnungMACyJKhcXFb2eFbsypSa7Z7jcqwP8dIIwWVc1Ri5LZpjlFlsJz3dgaIlHyDlvD9Mwn/lrv/xk7/HD/1Tm7D5css/G2WpKDkIBKWyHnrf3SrBERRwI8onxpXxpQkk5Ttv4clfAcxE+DwWXfXSeUFK/9JU4YqUP/mAEAEJOYhr9arO3Z+hudwWxkZLKJcUAoB7sS4fk8mVDgXBjgbUf/0GwxwOAoo+GDmENa8rrbNem6K1hwiX61WUAoOinZUJY/aqyusf00TBhEu/+2tsAgJBb3n0gIpwYDWciFhjWZSq6DgYElHaWYWN1EwIiXIg2AmWCVDOpNcEhg4eoOO93yEdNBlXKBFLPkZUs7SJoKwAgQoNoDaeXYY+S8vVDEpXzNCG45eBRANJirAZeKU0Owi6naTNgnpF2JGWpAeZ/OMCA3R53QQlgAABpR0LWOdiaQQEGWSfak3/KshtAwNjDUUH+dLC/9xh31lHrPgq7PGysW9En1nHWBzDwFpvrVPU8gGysWz1sBiKKQaABAWABKBIAAGEQXGy7taTmdu+RYoJvCDHBi24N4jC5G0WEipx/h2bcr1X1flpo4p1c/RdJz2w2UiT8311a/yfDfO4PIpUUALylN+5zud5FmiSGBoB5vr5Tq6oSaPqXIH+jIJxwe16q0lNRbBZCIzjTb5/0n1yZzHHPrFDKzSdOnNuz+8iuXbt37SozVpQ5K7b+/g8AIISiDJHJ/skdfJOSxif5/C13/Y+T91BrRgUKFq53O+lU0qJJIMbI5JyNX6q3OBTE3SopYhGiCUpLAwYgEKI7uIuH0GGb3IV3u0uiADwAINh44IE3cRhj3srTHPZu1/6OvI0XXAJn9AAAb+IEX7qeY8yc4OCrj6m1DQAAyJ75ojx5FoJfBNtrkrgXMSDAAZh5mDOWXDiGAPAB8FHGall/qe5MCQC07/ca6yPVbSjxYE+RI09OKgPYIJNQujV2RbcOKXGZAR5CZyDyg4PVyGNCtIGQFQBlJSgroi2A7YTMqhluBkAIKbFHLWvPYo9S4OWSeLngkQMvZcL9SaUvIfVgXgooQNbb311hw5wC8yzmlNXxE1rlXYNc5SUAoOinZaOllgxvzAyT2N4buqKflqDfxoIg8HJEk945DwAAPEYMAHChU6FIJBJda+Id/JWM+aWq0Mx7t3V2gaGwkiYBAQIYmyh9pYcSAA6Wuu//tSpnUvClp/f9oYIi0D8PXtRgfsrtURBo4Le6XQ/5BytIADjmdgeQZDBZ/f/6lVbbGLmMQciJsQQhJ8avVxlJBOU8X87zFbwgx5g5n5uz/WDvz3ac5s6dEXI9rou+PgFEqCo0uU9yQkJCWHQ0Hxa2WOtzZ2K78wRRzvN/BAf61feIGnt0rnPj2cQfEdXSCyQ7j3bDrgJEBwjuEkBIkvQ7oep59dPq4xbcAMAQTIW77K/K39spkjurexw3H/w6f879IY/39x1c6Mg7bM7oqu4dKongeKvdXaZEgHkL5kzAWzBvAd6KeSvwFsybLmzbMG/GvAUEG/B2zJuBlCNCDoQMUWogZIiUAalEhBwIKSIVQCoQIQFSjgglECxX+QNv2EKwUaTvcEzQiFBg3sYVzwGEpJ3P1swlLN7BN4R4By+6NYh38K0vUk0SFy6A9HZeRiKGwCYXjtdSIYomduNqz9AAgDG2XriJTWUuGgX3oKK6H58EIe9/5/pdlHH1glAVFDCkOPbZvyeo7jli80Mb/sdkn8ssysw0HMy0780sEcoKzYWFmwo3bdpUc1YWAOUfQIeEJAQHq8PCpiQmBAUFLZPJHb5+bFAgpVIzCEn8lkMVpyGqFvv7AsAknV7H8wBAApJfWHZJTRBf+GoB4JUqg16ou1asliDm+GoB4LUqg+6ST30I4jNfLQBM0xsq+f8+DfX/YlrRCMJdhBBDqHq9z7czVxnqnCtHaKaPBgDeMxjNQt2rUhlCs3w0ADDLZDcLGMAGwIJk9AEOllcZZChuTvtvSUS9ZzBWcqyOS9pgwkqbweCpynfkxEoiFgenHTcfnG4VtGyyilY7SYeNs6pkGhrRUgTvaaQSQlK7XkawMdjBYLtWMD+joDBvX2Mu4zkbLTgZ3iYR9BR2MrxDhi0pNAbejgEJrjxDxVqMCKrdL6q8ZwGACn35sisFiEQiUbO1dIK3WCw//vjjyZMny8vLOY4LDg5OTU29//77VSpVC0fSEN2CmEBZ9Y31sQpOJSGGxEhLLLwAwru7zO/sMtccGft1qXeDIWHPw0FayXVcV82HIHwIApsABzBJh3qe7pIxfipOynjclW078/1BeT9l1KbknPO5Z86cyc7Ozs3NzT1/fndWlr2wkKus4CorHMeOGgBevbhMViLx8fP3CQzw9Q/A/n4v+fn5+PiEa7UJPr4qrUau0TBqjUKjVmq0igsT1k5QKkyXpHDpheuAR5QKy+U/Ha9QWC/6tJvFlK4y/wOAmbhFwwSpnq97ruZCq8M9Mpn3sqM29YVPh8qkhkvOVROElGQBYKgMG3gW5JoLAYWCKtR7bpw8aQyfw1AyBSkpdVWdchxPZDuGSSKLbMefz5zzePhzQ2XdD5hPZVqPdFX3DpVHlrtKi5xVscokQhFs4gwMqmCpYArRGDBgzCPCAcAQBCthAaCg5JvQgmkMIT2StLsvd5K37Eekggl5ue4/rUgkEl07LdpEv3v37iFDhsTFxfXu3dvPzw8A9Hr9vn37zp07t2XLlu7d6xlndVUtNg6+wzflPcMobye73cWul7aZTC6B5zFFIjePfaWkd6S62c0DX71uqkfAAIi+cKufFkR9nF59L56+smLZMN8otbe/FYQpycZeEcR+Xbp0qLZfhMRd4DzVbR8dSHuKXbI0VcLWzlBrkWMB4N6yyjKe2xjof7SwePKJE+2rqgaZDAUFBeXl5UVFRRUVFUVFRVartYH1KhQKlUqlVCqVSqVGo1GpVDKZTCaTabVaiUQilUrVajXLsgqFQiqVSiQSpVJJUZRKpSJJ0rstk8lYtu4Ie8xVOQ93IAOfYCJnNfIn0XIcvD3fkRsmiVRQygLH+YPG3d00fcKlUYdNGVsrf7s7cGxHZdr2qs0/FC+aEjktTd1jl/6vbbpN94U81l7R0XywHcVVmqM+URd9CpyOSVhO+gyrXbjYRN8QYhO96NZwC/ai79Kly6OPPjp16tQ6+5cvX/7ll1/u37+/CWW2bIKnvx1y0RJz5Tb+22O2b4/bpnRRvNRNWWDmn9ykt3FYQiEMUGjmCEBhKhIA3AIuNfM1C6ZjDLWnrvmov3pMO9mVA7C68UO/Vbkv3J2e03vClKSMJgBAo3O/MiNLmaxI2HJRdgeASTr9Ead7S0iA9w54v8s9oVL3pkZd8yDAy+FwVFZWlpaW6nS6qlp0Op3RaDQYDAaDwbtxrX5hKIpSKpUAoFAoaJomCEKtlADBIERoNNV32N4LgtoHe2m1/z2z8F5G1C5ZrVYTF3cyqHN6bbWLqtelpV2VRqNBCAGAR/BUuEv9mUCGYI3GXcz55xH6//buPqiJc98D+JPNGy8hJECIgAekGHHkIHqKVutt1cFeUbEy9aU6MnrQKeo4rXL7JsofjsXaTkcGR8UyrZRapaeteqWi9+Kl2lGstk5toXKgKghaIEBAEMJLNsneP7aNEYIilX3S5fv5w8m+ZPP12efZH9lkN0zoKCL3NnjEfN/nWSjwg4ECD+Igws/gq6qqFi1a1H9+YmLipk2bHv7cnp6ejIyM/r8DffPmTVu/E7bDYbSamajre8tYvbf0P8M9DpaZ/2uKDyFE68Ek/d3rKY3sP0Yry5rYpf9tsnHc7Xarj4JZG+OtlJI77bazt3v/rpOfudXz4ljPyhaWECKRkG9qe73kzPwIj6o2a26ZeXaocs4Yj1tt1o/L7l8aNzVI2WC2WWz2XhuRMYQQ0tJtazLbiIQQBbPvwwlJk3zGMZKaduvBUvOsMGVcmEdNu9XnunSWVflBdYdSKnn+b8pZocpPvQOKyru/DeqdFaqsbbd+Udn9xytopwbpFzzz+8w5QYp+K5CpQYp/aHr/Xdd6tMw01rNnrFfPzYbWb6va2Z6u7s52trdHJ2fV9g7Tve4bTR2e9m4fqbX5brvJzPZ0tHOE6zV3MJy9t9tssVisVuvdu3cJIfy/I8c/X1Qe/Nch2ikAQPwELfCzZs3asmVLVlaWXn//p1NaWlrS0tKef/75hz9XJpPp9fqurq4+80NCQh75JuyJWBnlFTvKxT3hnd+I+ygkjlvdTQyUB3pLZYykeIWu02L3lEkUUonVThKb2WAVc+ZWzz8nquo7rI6vi43zkxFC/D2ZiTp5sEpKCNF4MNE6OfljhQiN9IfV+pZu+//d6onSyROPmd6bpem03H8/HegtJYR4y5moAHmgl5QQ4qNgntbI+aW9Nk7jwRBCQpUynVLGP/aSM1olY//jTbnzzIFWUKvVETJVZK9uSrBiUqA8pssuvd7tWOGZEOWkQHlzl/3E9W5+Bf5x/xWO/fveBLUlWievaWr/nxuddru9u7OdEDIhQKZnzO293MXfekMU3aEqcq+Xu3zH3N31+4cIY3ylPrYOM8uVN7Oj1VKt1NLa0X3j7v1r83w4s5+S9Fi5mnabzovx92Q6e9hfG9ocDeXnyWiUTK+Nq+uwyXrv+SgkFhvX3HX/k3u1knHMZHo7PKSc1U7aeu5/tc9bIfGSSfiZXk6P+RW6O9qUUqKQSmwc6WI5x+NuC+tN6if9Ywrj+SR/ERgAwCVBT9E3NTUlJycXFRWFh4f7+/tLJJLW1tbq6uq4uLj8/Hw/P79Hb6If6qfsmrvsc/7VVLrGxY1uXF4mx4v4sOF/Xw40aId+GbTjM/ghbwGEZ793UeI9USJ18XkBTtEPBvXxDvBEiPAUfWBg4KlTp2pra8vLyxsaGjiO0+v1MTExzjde/cvReTEuqzshZFqIUjHQl+ckRP7Xu106/FmMegbtCAAwUlC4Dj4sLCwsLEz41xXeB7N9B1q0fLzn39R/6i5m3nIm3Ff+Z7YAAAAihhvd0LFzpubRKz1U2Vr9o1cCAICRCqeJAQAARAgFHgAAQIRQ4AEAAEQIBR4AAECEUOABAABE6C//LXqpVFpdXR0bG9t/kclkamho4G9m7iY4jrNarXK5G13exnEcy7IKhYub9FFksVjcLRLLslFRUVLpn7q48SEsFsvj3vR+BHLz8c6yrFQqpbsfqY8du91ut9uxI6xWa3R09EBLhRnvgt7JbjhwHFdaWurydvTHjx8vKSl55ZVXhE81kPLy8pMnT27ZsoV2kPuMRuPu3bs/+OAD2kHuY1l23bp1ubm5tIM8YOPGjceOHRu++yJ7enpOmDBhmDYuGm4+3nfv3h0XFzdp0iRaASwWy4YNGw4ePEgrACHkwoULFRUVKSkpFDNkZmbOnj178uTJtAKwLLt+/frvvvtuoBUEGu+ceOXm5iYnJ9NO8YDi4uK4uDjaKR7w66+/jhs3jnaKB3R3d3t4eNBO0ZdOp2tqaqKdAgbkDuN9wYIFhYWFFAOYzWYvLy+KATiOy8vLW716Nd0MCQkJJ0+epBigq6vL09OTYgAeTgkCAACIEAo8AACACKHAAwAAiBAKPAAAgAihwAMAAIiQmAu8TCYbvquWh8Y9I7nVrQIIIQzDuNWtAnhu2FDgzB0Gl1QqpdtJ3GHsYEcQ99gRRATXwT9Eb29vZ2env78/7SD32e32pqamUaNG0Q7ygPr6+uDgYNopHoBI8LjcYbw3NjYGBATQLW/UO6rFYrl3715AQADFDNgRPDEXeAAAgBFLzKfoAQAARiwUeAAAABFCgQcAABAhFHgAAAARQoEHAAAQIRR4AAAAEUKBBwAAECEUeAAAABESbYFvamp66aWXtFrt008/ffHiRVox1qxZI3FSUFBAN1tFRUV+fr5j0mUSgeP1iUS3xaqrq+Pj47VabWhoaEZGBj/THVoJHo7W7hh8hxlut2/fvnXrFpUANpvtzTffDA0NDQkJ2bt3L5UMP//888yZM1Uq1fjx448cOSJwBjc8rvJEW+CTk5MVCsVPP/20atWqefPmdXR0UIlRVVW1Z8+eyj/ExcVRzGaz2Xbs2HHhwgXHHJdJhIzXPxLFFrNYLDNnzgwLC/vll18+/fTTrKys3NzcgV7dTToY8KjsjsfqMMPKbDbHxcUdOnSInxQ4QGpq6sWLF4uKivbu3fv666+XlJQInMFqtSYkJERFRZWWlm7dunX16tVXr14VLIMbHlfv48SopqZGKpUajUZ+cvLkyTk5OVSSBAcHl5WVOc+hlW3nzp38PfDXr1//kCRCxusfiaPaYufPn/fx8bFYLPxkenr6woULqbcSPBKt3TH4DjPcSdauXatUKrdv384J3hpms1mlUl27do2f3LVr1xdffCFwhurqakLI7du3+cno6Ojs7GxhMrjhcdWZON/Bl5eXh4eH6/V6fvLZZ5+9du2a8DG6urrq6+vT0tLUarXBYMjJyaGYbdWqVcXFxUlJSY45LpMIGa9/JLotNmrUqKysLMdvQLW2tjIMQ72V4JFo7Y7Bd5hhjXH8+PGKiop58+bxkwIHuHTpkq+vb1RUFMdxhJAtW7YsW7ZM4AxhYWERERHZ2dltbW2nT5++efPmjBkzhMnghsdVZ+Is8A0NDc4/KhUQENDY2Ch8jKqqKoZh5s6dW1lZuXPnztTU1NOnT9PKNnr06KioKOefeHKZRMh4/SPRbTGDwbBmzRr+8blz5w4fPpySkkK9leCRaO2OwXeY4ctQV1eXmpp66NAhx6+jChyA/922jRs3+vn56XS6tLQ0u90ucAaGYT7//PP3339fq9UuWLAgPT194sSJwmRww+OqM3EWeI7jJBKJ8xyr1Sp8jOjoaIvF8uqrrwYHBy9btiw5Ofmzzz5zk2xkgFaiG88dWqy7u/utt95auHBhbm7u/Pnz3bCVoA+6u2MwHWaYXprjuNWrV6enp0dERDjPFLI12traSktLtVptTU1NcXFxXl5edna2wBmMRmNiYuJHH33U0dFRUlJy4MCBU6dO0eoVbnXEEGeB1+v1ra2tjsnW1tagoCAqSZx/kHj8+PENDQ3uk81lEurx6LZYVVVVbGzs1atXf/zxx8WLFxN3bSVwRnF3DLLDDNOr7927VyKRrFixwmw2W61Wi8XS1dUlcGv4+fnp9fqMjAxfX9+YmJgVK1YUFhYKnKGwsPCpp55au3atSqWaMWPGunXr8vLyaPUKtzpiiLPAx8TEVFdXt7S08JPff/99TEyM8DHy8/NXrlzpmKyqqoqIiHCTbGSAVqIbj26LsSw7b968+Pj4M2fOREZG8jPdsJWgD1q7Y/AdZpgCXLlypbi42MfHR6VSnThx4t13342MjBS4NcaNG8eyrM1m4yc1Go2Xl5fAGSwWi91ud0xyHMeyLK1e4V5HDAG+yEfF3LlzU1JSOjo6Dh8+rNFo2trahM9QXl4ulUp37dpVV1dXUFCgVquvXLlCN9vmzZudv7LuMonA8Zwj0W2x48eP+/n5VVZW3vxDfX39QK/uDh0MHKjsjsfqMMNtyZIl/LfohQ8wffr01157zWg0fvvttzqd7ujRowJnqKmpUalU+/btM5lMZ8+eDQwMzM/PFzKDGx5XeaIt8C0tLS+++KJGo4mNjb18+TKtGGfPnn3mmWdUKlVMTMzXX39NPVufjugyicDx+kSi2GLbt2/v8+dvQkLCQK/uJh0MeFR2x2N1mOHmXOAFDmA0GhMSEnx9fQ0Gw4cffkglw/nz56dNm+bt7W0wGPbv3y9wBjc8rvIkHMcJcaIAAAAABHA7MvUAAAT3SURBVCTOz+ABAABGOBR4AAAAEUKBBwAAECEUeAAAABFCgQcAABAhFHgAAAARQoEHAAAQIRR4AAAAEUKBBwAAECEUeAAAABFCgQcAABAhFHgAAAARQoEHAAAQIRR4AAAAEUKBBwAAECEUeAAAABFCgQcAABAhFHgAAAARQoEHAAAQIRR4AAAAEUKBBwAAECEU+JHrhRdekPRjMBhqa2vlcjntdAAwWFOmTHEMYR8fnzlz5lRUVAxhO2lpaampqU/2CMBv80ltDR4LCvzI9eWXXxqNRqPRuGHDhvj4eP7xpUuX/P399+/fTzsdADyGHTt28EO4pKSkp6dn6dKlQ97UI48AU6dOvXr16pC3D4KR0Q4A1Gi1Wv6Bt7e3h4eHXq93LEpJSaEUCgCGQq1W80NYr9dv27Zt/vz5DQ0NQUFB/FKO41iWVSgUg9mUSqV6+BGgubmZZdk/nxmGG97BQ1+OE3QtLS0ajWbr1q2+vr7BwcEHDhzIyckZM2aMVqt97733+JWrqqri4+M1Gs2MGTMOHz5MNTgA3MeybGNjo1arPXPmTHh4+KVLl1yO1sLCwujoaK1Wu2TJkrt37xKnIwAh5PLly9OmTVOr1dOnT79y5QohZNasWXfu3Jk/f/6xY8fIAEeA/tsEOjgY8d54443ExETHZE1NjUwm4zjOZDIRQpKSkurq6jIzMwkhy5cvb29vz87OZhims7Ozp6cnPDw8PT29qanp3LlzQUFBp0+fpvf/ABihYmNjMzIyTCaTyWQqLy9/7rnnDAaD3W43Go0KhSI5OdloNLocrZWVlQqFIicnp66ubt++fRKJZPPmzY4jQGNjo1qt/vjjj+vr69955x2dTseyLMdxY8aMuXz5Msdxg98m5QYaqVDg4REF/s6dOxzH8X+Gl5aWchzHn5377bffTpw4ERwcbLFY+Cfu2LFj0aJFNP4HACNabGys4z2bXC6fPn16WVkZx3FGo5EQcv36dY7jXI7W9PR057E/e/Zs5wKflZUVFxfHL7LZbAcOHDCZTJxTgR/8NgVoBOgPn8HDI+h0OkIIf8ouJCSEECKT/d5tqqurW1paQkNDHSs7H2gAQDBZWVmbNm1yuSgsLIwMMFrr6uoiIyMdc6Kjo52fWFtbazAY+McMw6xfv77PloewTRASCjwMXVBQUGxsbElJCT95+/Ztq9VKNxIA9CGVSskAozUvL+/atWuONWtra8PDwx2TISEh33zzDf+Y47jt27evWrUqIiLCscIQtglCwpfsYOji4+Nv3LiRmZlpMplKSkqmTp36ww8/0A4FAC64HK0rV648derUJ5980tzcnJeXV1RU5PyUpUuXnj9//siRIyaTaffu3Xv27PH39+cXtbe3D22bICQUeBg6jUZTVFRUUFAQHh6elJT09ttvL1++nHYoAHDB5WiNjIw8evRoZmbm2LFjT5w4sW3bNuenhIaGnjx5MjMzMywsLD8/v6CgQKPREEJefvnlxYsXf/XVV0PYJghJwnEc7QwAAADwhOEdPAAAgAihwAMAAIgQCjwAAIAIocADAACIEAo8AACACKHAAwAAiBAKPAAAgAihwAMAAIgQCjwAAIAIocADAACIEAo8AACACKHAAwAAiBAKPAAAgAihwAMAAIgQCjwAAIAIocADAACIEAo8AACACKHAAwAAiBAKPAAAgAihwAMAAIjQ/wOkL6IbmI5dzAAAAABJRU5ErkJggg==" /><!-- --></p> -<p>While this is visually much more satisfactory, such an average procedure could introduce a bias, as not all results from the individual fits enter the population curve with the same weight. This is where nonlinear mixed-effects models can help out by treating all datasets with equally by fitting a parameter distribution model together with the degradation model and the error model (see below).</p> -<p>The remaining trend of the residuals to be higher for higher predicted residues is reduced by using the two-component error model:</p> +<p><img src="data:image/png;base64,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" /><!-- --></p> +<p>While this is visually much more satisfactory, such an average +procedure could introduce a bias, as not all results from the individual +fits enter the population curve with the same weight. This is where +nonlinear mixed-effects models can help out by treating all datasets +with equally by fitting a parameter distribution model together with the +degradation model and the error model (see below).</p> +<p>The remaining trend of the residuals to be higher for higher +predicted residues is reduced by using the two-component error +model:</p> <pre class="r"><code>plot(mixed(f_parent_mkin_tc["DFOP", ]), test_log_parms = TRUE)</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> -<p>However, note that in the case of using this error model, the fits to the Flaach and BBA 2.3 datasets appear to be ill-defined, indicated by the fact that they did not converge:</p> +<p><img src="data:image/png;base64,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" /><!-- --></p> +<p>However, note that in the case of using this error model, the fits to +the Flaach and BBA 2.3 datasets appear to be ill-defined, indicated by +the fact that they did not converge:</p> <pre class="r"><code>print(f_parent_mkin_tc["DFOP", ])</code></pre> <pre><code><mmkin> object Status of individual fits: @@ -1659,23 +1730,50 @@ Status of individual fits: model Calke Borstel Flaach BBA 2.2 BBA 2.3 Elliot DFOP OK OK C OK C OK -OK: No warnings C: Optimisation did not converge: -iteration limit reached without convergence (10)</code></pre> +iteration limit reached without convergence (10) +OK: No warnings</code></pre> </div> <div id="nonlinear-mixed-effects-models" class="section level2"> <h2>Nonlinear mixed-effects models</h2> -<p>Instead of taking a model selection decision for each of the individual fits, we fit nonlinear mixed-effects models (using different fitting algorithms as implemented in different packages) and do model selection using all available data at the same time. In order to make sure that these decisions are not unduly influenced by the type of algorithm used, by implementation details or by the use of wrong control parameters, we compare the model selection results obtained with different R packages, with different algorithms and checking control parameters.</p> +<p>Instead of taking a model selection decision for each of the +individual fits, we fit nonlinear mixed-effects models (using different +fitting algorithms as implemented in different packages) and do model +selection using all available data at the same time. In order to make +sure that these decisions are not unduly influenced by the type of +algorithm used, by implementation details or by the use of wrong control +parameters, we compare the model selection results obtained with +different R packages, with different algorithms and checking control +parameters.</p> <div id="nlme" class="section level3"> <h3>nlme</h3> -<p>The nlme package was the first R extension providing facilities to fit nonlinear mixed-effects models. We would like to do model selection from all four combinations of degradation models and error models based on the AIC. However, fitting the DFOP model with constant variance and using default control parameters results in an error, signalling that the maximum number of 50 iterations was reached, potentially indicating overparameterisation. Nevertheless, the algorithm converges when the two-component error model is used in combination with the DFOP model. This can be explained by the fact that the smaller residues observed at later sampling times get more weight when using the two-component error model which will counteract the tendency of the algorithm to try parameter combinations unsuitable for fitting these data.</p> +<p>The nlme package was the first R extension providing facilities to +fit nonlinear mixed-effects models. We would like to do model selection +from all four combinations of degradation models and error models based +on the AIC. However, fitting the DFOP model with constant variance and +using default control parameters results in an error, signalling that +the maximum number of 50 iterations was reached, potentially indicating +overparameterisation. Nevertheless, the algorithm converges when the +two-component error model is used in combination with the DFOP model. +This can be explained by the fact that the smaller residues observed at +later sampling times get more weight when using the two-component error +model which will counteract the tendency of the algorithm to try +parameter combinations unsuitable for fitting these data.</p> <pre class="r"><code>library(nlme) f_parent_nlme_sfo_const <- nlme(f_parent_mkin_const["SFO", ]) # f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const["DFOP", ]) f_parent_nlme_sfo_tc <- nlme(f_parent_mkin_tc["SFO", ]) f_parent_nlme_dfop_tc <- nlme(f_parent_mkin_tc["DFOP", ])</code></pre> -<p>Note that a certain degree of overparameterisation is also indicated by a warning obtained when fitting DFOP with the two-component error model (‘false convergence’ in the ‘LME step’ in iteration 3). However, as this warning does not occur in later iterations, and specifically not in the last of the 6 iterations, we can ignore this warning.</p> -<p>The model comparison function of the nlme package can directly be applied to these fits showing a much lower AIC for the DFOP model fitted with the two-component error model. Also, the likelihood ratio test indicates that this difference is significant as the p-value is below 0.0001.</p> +<p>Note that a certain degree of overparameterisation is also indicated +by a warning obtained when fitting DFOP with the two-component error +model (‘false convergence’ in the ‘LME step’ in iteration 3). However, +as this warning does not occur in later iterations, and specifically not +in the last of the 5 iterations, we can ignore this warning.</p> +<p>The model comparison function of the nlme package can directly be +applied to these fits showing a much lower AIC for the DFOP model fitted +with the two-component error model. Also, the likelihood ratio test +indicates that this difference is significant as the p-value is below +0.0001.</p> <pre class="r"><code>anova( f_parent_nlme_sfo_const, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc )</code></pre> @@ -1683,7 +1781,10 @@ f_parent_nlme_dfop_tc <- nlme(f_parent_mkin_tc["DFOP", ])</code></p f_parent_nlme_sfo_const 1 5 796.60 811.82 -393.30 f_parent_nlme_sfo_tc 2 6 798.60 816.86 -393.30 1 vs 2 0.00 0.998 f_parent_nlme_dfop_tc 3 10 671.91 702.34 -325.96 2 vs 3 134.69 <.0001</code></pre> -<p>In addition to these fits, attempts were also made to include correlations between random effects by using the log Cholesky parameterisation of the matrix specifying them. The code used for these attempts can be made visible below.</p> +<p>In addition to these fits, attempts were also made to include +correlations between random effects by using the log Cholesky +parameterisation of the matrix specifying them. The code used for these +attempts can be made visible below.</p> <pre class="r"><code>f_parent_nlme_sfo_const_logchol <- nlme(f_parent_mkin_const["SFO", ], random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1))) anova(f_parent_nlme_sfo_const, f_parent_nlme_sfo_const_logchol) @@ -1693,41 +1794,49 @@ anova(f_parent_nlme_sfo_tc, f_parent_nlme_sfo_tc_logchol) f_parent_nlme_dfop_tc_logchol <- nlme(f_parent_mkin_const["DFOP", ], random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1))) anova(f_parent_nlme_dfop_tc, f_parent_nlme_dfop_tc_logchol)</code></pre> -<p>While the SFO variants converge fast, the additional parameters introduced by this lead to convergence warnings for the DFOP model. The model comparison clearly show that adding correlations between random effects does not improve the fits.</p> -<p>The selected model (DFOP with two-component error) fitted to the data assuming no correlations between random effects is shown below.</p> +<p>While the SFO variants converge fast, the additional parameters +introduced by this lead to convergence warnings for the DFOP model. The +model comparison clearly show that adding correlations between random +effects does not improve the fits.</p> +<p>The selected model (DFOP with two-component error) fitted to the data +assuming no correlations between random effects is shown below.</p> <pre class="r"><code>plot(f_parent_nlme_dfop_tc)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> </div> <div id="saemix" class="section level3"> <h3>saemix</h3> -<p>The saemix package provided the first Open Source implementation of the Stochastic Approximation to the Expectation Maximisation (SAEM) algorithm. SAEM fits of degradation models can be conveniently performed using an interface to the saemix package available in current development versions of the mkin package.</p> -<p>The corresponding SAEM fits of the four combinations of degradation and error models are fitted below. As there is no convergence criterion implemented in the saemix package, the convergence plots need to be manually checked for every fit. We define control settings that work well for all the parent data fits shown in this vignette.</p> -<pre class="r"><code>library(saemix)</code></pre> -<pre><code>Loading required package: npde</code></pre> -<pre><code>Package saemix, version 3.0 - please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr</code></pre> -<pre><code> -Attaching package: 'saemix'</code></pre> -<pre><code>The following objects are masked from 'package:npde': - - kurtosis, skewness</code></pre> -<pre class="r"><code>saemix_control <- saemixControl(nbiter.saemix = c(800, 300), nb.chains = 15, +<p>The saemix package provided the first Open Source implementation of +the Stochastic Approximation to the Expectation Maximisation (SAEM) +algorithm. SAEM fits of degradation models can be conveniently performed +using an interface to the saemix package available in current +development versions of the mkin package.</p> +<p>The corresponding SAEM fits of the four combinations of degradation +and error models are fitted below. As there is no convergence criterion +implemented in the saemix package, the convergence plots need to be +manually checked for every fit. We define control settings that work +well for all the parent data fits shown in this vignette.</p> +<pre class="r"><code>library(saemix) +saemix_control <- saemixControl(nbiter.saemix = c(800, 300), nb.chains = 15, print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE) saemix_control_moreiter <- saemixControl(nbiter.saemix = c(1600, 300), nb.chains = 15, print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE) saemix_control_10k <- saemixControl(nbiter.saemix = c(10000, 300), nb.chains = 15, print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE)</code></pre> -<p>The convergence plot for the SFO model using constant variance is shown below.</p> +<p>The convergence plot for the SFO model using constant variance is +shown below.</p> <pre class="r"><code>f_parent_saemix_sfo_const <- mkin::saem(f_parent_mkin_const["SFO", ], quiet = TRUE, control = saemix_control, transformations = "saemix") plot(f_parent_saemix_sfo_const$so, plot.type = "convergence")</code></pre> <p><img 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" /><!-- --></p> -<p>Obviously the selected number of iterations is sufficient to reach convergence. This can also be said for the SFO fit using the two-component error model.</p> +<p>Obviously the selected number of iterations is sufficient to reach +convergence. This can also be said for the SFO fit using the +two-component error model.</p> <pre class="r"><code>f_parent_saemix_sfo_tc <- mkin::saem(f_parent_mkin_tc["SFO", ], quiet = TRUE, control = saemix_control, transformations = "saemix") plot(f_parent_saemix_sfo_tc$so, plot.type = "convergence")</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> -<p>When fitting the DFOP model with constant variance (see below), parameter convergence is not as unambiguous.</p> +<p><img src="data:image/png;base64,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" /><!-- --></p> +<p>When fitting the DFOP model with constant variance (see below), +parameter convergence is not as unambiguous.</p> <pre class="r"><code>f_parent_saemix_dfop_const <- mkin::saem(f_parent_mkin_const["DFOP", ], quiet = TRUE, control = saemix_control, transformations = "saemix") plot(f_parent_saemix_dfop_const$so, plot.type = "convergence")</code></pre> @@ -1752,13 +1861,21 @@ DMTA_0 97.99583 96.50079 99.4909 k1 0.06377 0.03432 0.0932 k2 0.00848 0.00444 0.0125 g 0.95701 0.91313 1.0009 -a.1 1.82141 1.65974 1.9831 -SD.DMTA_0 1.64787 0.45779 2.8379 +a.1 1.82141 1.65122 1.9916 +SD.DMTA_0 1.64787 0.45772 2.8380 SD.k1 0.57439 0.24731 0.9015 -SD.k2 0.03296 -2.50143 2.5673 -SD.g 1.10266 0.32371 1.8816</code></pre> -<p>While the other parameters converge to credible values, the variance of k2 (<code>omega2.k2</code>) converges to a very small value. The printout of the <code>saem.mmkin</code> model shows that the estimated standard deviation of k2 across the population of soils (<code>SD.k2</code>) is ill-defined, indicating overparameterisation of this model.</p> -<p>When the DFOP model is fitted with the two-component error model, we also observe that the estimated variance of k2 becomes very small, while being ill-defined, as illustrated by the excessive confidence interval of <code>SD.k2</code>.</p> +SD.k2 0.03296 -2.50195 2.5679 +SD.g 1.10266 0.32369 1.8816</code></pre> +<p>While the other parameters converge to credible values, the variance +of k2 (<code>omega2.k2</code>) converges to a very small value. The +printout of the <code>saem.mmkin</code> model shows that the estimated +standard deviation of k2 across the population of soils +(<code>SD.k2</code>) is ill-defined, indicating overparameterisation of +this model.</p> +<p>When the DFOP model is fitted with the two-component error model, we +also observe that the estimated variance of k2 becomes very small, while +being ill-defined, as illustrated by the excessive confidence interval +of <code>SD.k2</code>.</p> <pre class="r"><code>f_parent_saemix_dfop_tc <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, control = saemix_control, transformations = "saemix") f_parent_saemix_dfop_tc_moreiter <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, @@ -1791,9 +1908,21 @@ SD.DMTA_0 2.06075 0.4187 3.7028 SD.k1 0.59357 0.2561 0.9310 SD.k2 0.00292 -10.2960 10.3019 SD.g 1.05725 0.3808 1.7337</code></pre> -<p>Doubling the number of iterations in the first phase of the algorithm leads to a slightly lower likelihood, and therefore to slightly higher AIC and BIC values. With even more iterations, the algorithm stops with an error message. This is related to the variance of k2 approximating zero and has been submitted as a <a href="https://github.com/saemixdevelopment/saemixextension/issues/29">bug to the saemix package</a>, as the algorithm does not converge in this case.</p> -<p>An alternative way to fit DFOP in combination with the two-component error model is to use the model formulation with transformed parameters as used per default in mkin. When using this option, convergence is slower, but eventually the algorithm stops as well with the same error message.</p> -<p>The four combinations (SFO/const, SFO/tc, DFOP/const and DFOP/tc) and the version with increased iterations can be compared using the model comparison function of the saemix package:</p> +<p>Doubling the number of iterations in the first phase of the algorithm +leads to a slightly lower likelihood, and therefore to slightly higher +AIC and BIC values. With even more iterations, the algorithm stops with +an error message. This is related to the variance of k2 approximating +zero and has been submitted as a <a href="https://github.com/saemixdevelopment/saemixextension/issues/29">bug +to the saemix package</a>, as the algorithm does not converge in this +case.</p> +<p>An alternative way to fit DFOP in combination with the two-component +error model is to use the model formulation with transformed parameters +as used per default in mkin. When using this option, convergence is +slower, but eventually the algorithm stops as well with the same error +message.</p> +<p>The four combinations (SFO/const, SFO/tc, DFOP/const and DFOP/tc) and +the version with increased iterations can be compared using the model +comparison function of the saemix package:</p> <pre class="r"><code>AIC_parent_saemix <- saemix::compare.saemix( f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so, @@ -1810,7 +1939,10 @@ SFO tc 798.38 797.13 DFOP const 705.75 703.88 DFOP tc 665.65 663.57 DFOP tc more iterations 665.88 663.80</code></pre> -<p>In order to check the influence of the likelihood calculation algorithms implemented in saemix, the likelihood from Gaussian quadrature is added to the best fit, and the AIC values obtained from the three methods are compared.</p> +<p>In order to check the influence of the likelihood calculation +algorithms implemented in saemix, the likelihood from Gaussian +quadrature is added to the best fit, and the AIC values obtained from +the three methods are compared.</p> <pre class="r"><code>f_parent_saemix_dfop_tc$so <- saemix::llgq.saemix(f_parent_saemix_dfop_tc$so) AIC_parent_saemix_methods <- c( @@ -1821,9 +1953,19 @@ AIC_parent_saemix_methods <- c( print(AIC_parent_saemix_methods)</code></pre> <pre><code> is gq lin 665.65 665.68 665.11 </code></pre> -<p>The AIC values based on importance sampling and Gaussian quadrature are very similar. Using linearisation is known to be less accurate, but still gives a similar value.</p> -<p>In order to illustrate that the comparison of the three method depends on the degree of convergence obtained in the fit, the same comparison is shown below for the fit using the defaults for the number of iterations and the number of MCMC chains.</p> -<p>When using OpenBlas for linear algebra, there is a large difference in the values obtained with Gaussian quadrature, so the larger number of iterations makes a lot of difference. When using the LAPACK version coming with Debian Bullseye, the AIC based on Gaussian quadrature is almost the same as the one obtained with the other methods, also when using defaults for the fit.</p> +<p>The AIC values based on importance sampling and Gaussian quadrature +are very similar. Using linearisation is known to be less accurate, but +still gives a similar value.</p> +<p>In order to illustrate that the comparison of the three method +depends on the degree of convergence obtained in the fit, the same +comparison is shown below for the fit using the defaults for the number +of iterations and the number of MCMC chains.</p> +<p>When using OpenBlas for linear algebra, there is a large difference +in the values obtained with Gaussian quadrature, so the larger number of +iterations makes a lot of difference. When using the LAPACK version +coming with Debian Bullseye, the AIC based on Gaussian quadrature is +almost the same as the one obtained with the other methods, also when +using defaults for the fit.</p> <pre class="r"><code>f_parent_saemix_dfop_tc_defaults <- mkin::saem(f_parent_mkin_tc["DFOP", ]) f_parent_saemix_dfop_tc_defaults$so <- saemix::llgq.saemix(f_parent_saemix_dfop_tc_defaults$so) @@ -1834,12 +1976,14 @@ AIC_parent_saemix_methods_defaults <- c( ) print(AIC_parent_saemix_methods_defaults)</code></pre> <pre><code> is gq lin -668.27 718.36 666.49 </code></pre> +669.77 669.36 670.95 </code></pre> </div> </div> <div id="comparison" class="section level2"> <h2>Comparison</h2> -<p>The following table gives the AIC values obtained with both backend packages using the same control parameters (800 iterations burn-in, 300 iterations second phase, 15 chains).</p> +<p>The following table gives the AIC values obtained with both backend +packages using the same control parameters (800 iterations burn-in, 300 +iterations second phase, 15 chains).</p> <pre class="r"><code>AIC_all <- data.frame( check.names = FALSE, "Degradation model" = c("SFO", "SFO", "DFOP", "DFOP"), @@ -1880,7 +2024,7 @@ kable(AIC_all)</code></pre> <td align="left">DFOP</td> <td align="left">const</td> <td align="right">NA</td> -<td align="right">671.98</td> +<td align="right">709.26</td> <td align="right">705.75</td> </tr> <tr class="even"> @@ -1896,19 +2040,31 @@ kable(AIC_all)</code></pre> </div> <div id="conclusion" class="section level1"> <h1>Conclusion</h1> -<p>A more detailed analysis of the dimethenamid dataset confirmed that the DFOP model provides the most appropriate description of the decline of the parent compound in these data. On the other hand, closer inspection of the results revealed that the variability of the k2 parameter across the population of soils is ill-defined. This coincides with the observation that this parameter cannot robustly be quantified for some of the soils.</p> -<p>Regarding the regulatory use of these data, it is claimed that an improved characterisation of the mean parameter values across the population is obtained using the nonlinear mixed-effects models presented here. However, attempts to quantify the variability of the slower rate constant of the biphasic decline of dimethenamid indicate that the data are not sufficient to characterise this variability to a satisfactory precision.</p> +<p>A more detailed analysis of the dimethenamid dataset confirmed that +the DFOP model provides the most appropriate description of the decline +of the parent compound in these data. On the other hand, closer +inspection of the results revealed that the variability of the k2 +parameter across the population of soils is ill-defined. This coincides +with the observation that this parameter cannot robustly be quantified +for some of the soils.</p> +<p>Regarding the regulatory use of these data, it is claimed that an +improved characterisation of the mean parameter values across the +population is obtained using the nonlinear mixed-effects models +presented here. However, attempts to quantify the variability of the +slower rate constant of the biphasic decline of dimethenamid indicate +that the data are not sufficient to characterise this variability to a +satisfactory precision.</p> </div> <div id="session-info" class="section level1"> <h1>Session Info</h1> <pre class="r"><code>sessionInfo()</code></pre> -<pre><code>R version 4.2.1 (2022-06-23) +<pre><code>R version 4.2.2 Patched (2022-11-10 r83330) Platform: x86_64-pc-linux-gnu (64-bit) -Running under: Debian GNU/Linux 11 (bullseye) +Running under: Debian GNU/Linux bookworm/sid Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/openblas-serial/libblas.so.3 -LAPACK: /usr/lib/x86_64-linux-gnu/openblas-serial/libopenblas-r0.3.13.so +LAPACK: /usr/lib/x86_64-linux-gnu/openblas-serial/libopenblas-r0.3.21.so locale: [1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C @@ -1919,39 +2075,44 @@ locale: [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C attached base packages: -[1] parallel stats graphics grDevices utils datasets methods -[8] base +[1] stats graphics grDevices utils datasets methods base other attached packages: -[1] saemix_3.0 npde_3.2 nlme_3.1-158 mkin_1.1.0 knitr_1.39 +[1] saemix_3.2 npde_3.3 nlme_3.1-161 mkin_1.2.2 knitr_1.41 loaded via a namespace (and not attached): - [1] highr_0.9 bslib_0.3.1 compiler_4.2.1 pillar_1.7.0 - [5] jquerylib_0.1.4 tools_4.2.1 mclust_5.4.10 digest_0.6.29 - [9] gtable_0.3.0 tibble_3.1.7 jsonlite_1.8.0 evaluate_0.15 -[13] lifecycle_1.0.1 lattice_0.20-45 pkgconfig_2.0.3 rlang_1.0.3 -[17] DBI_1.1.3 cli_3.3.0 yaml_2.3.5 xfun_0.31 -[21] fastmap_1.1.0 gridExtra_2.3 stringr_1.4.0 dplyr_1.0.9 -[25] generics_0.1.2 sass_0.4.1 vctrs_0.4.1 tidyselect_1.1.2 -[29] lmtest_0.9-40 grid_4.2.1 deSolve_1.32 glue_1.6.2 -[33] R6_2.5.1 fansi_1.0.3 rmarkdown_2.14 ggplot2_3.3.6 -[37] purrr_0.3.4 magrittr_2.0.3 scales_1.2.0 codetools_0.2-18 -[41] htmltools_0.5.2 ellipsis_0.3.2 assertthat_0.2.1 colorspace_2.0-3 -[45] utf8_1.2.2 stringi_1.7.6 munsell_0.5.0 crayon_1.5.1 -[49] zoo_1.8-10 </code></pre> + [1] highr_0.9 bslib_0.4.2 compiler_4.2.2 pillar_1.8.1 + [5] jquerylib_0.1.4 tools_4.2.2 mclust_6.0.0 digest_0.6.31 + [9] jsonlite_1.8.4 evaluate_0.19 lifecycle_1.0.3 tibble_3.1.8 +[13] gtable_0.3.1 lattice_0.20-45 pkgconfig_2.0.3 rlang_1.0.6 +[17] DBI_1.1.3 cli_3.5.0 yaml_2.3.6 parallel_4.2.2 +[21] xfun_0.35 fastmap_1.1.0 gridExtra_2.3 dplyr_1.0.10 +[25] stringr_1.5.0 generics_0.1.3 vctrs_0.5.1 sass_0.4.4 +[29] tidyselect_1.2.0 lmtest_0.9-40 grid_4.2.2 deSolve_1.34 +[33] glue_1.6.2 R6_2.5.1 fansi_1.0.3 rmarkdown_2.19 +[37] ggplot2_3.4.0 magrittr_2.0.3 codetools_0.2-18 scales_1.2.1 +[41] htmltools_0.5.4 assertthat_0.2.1 colorspace_2.0-3 utf8_1.2.2 +[45] stringi_1.7.8 munsell_0.5.0 cachem_1.0.6 zoo_1.8-11 </code></pre> </div> <div id="references" class="section level1"> <h1>References</h1> <!-- vim: set foldmethod=syntax: --> -<div id="refs" class="references hanging-indent"> -<div id="ref-efsa_2018_dimethenamid"> -<p>EFSA. 2018. “Peer Review of the Pesticide Risk Assessment of the Active Substance Dimethenamid-P.” <em>EFSA Journal</em> 16 (4): 5211.</p> +<div id="refs" class="references csl-bib-body hanging-indent"> +<div id="ref-efsa_2018_dimethenamid" class="csl-entry"> +EFSA. 2018. <span>“Peer Review of the Pesticide Risk Assessment of the +Active Substance Dimethenamid-p.”</span> <em>EFSA Journal</em> 16: 5211. </div> -<div id="ref-ranke2021"> -<p>Ranke, Johannes, Janina Wöltjen, Jana Schmidt, and Emmanuelle Comets. 2021. “Taking Kinetic Evaluations of Degradation Data to the Next Level with Nonlinear Mixed-Effects Models.” <em>Environments</em> 8 (8). <a href="https://doi.org/10.3390/environments8080071">https://doi.org/10.3390/environments8080071</a>.</p> +<div id="ref-ranke2021" class="csl-entry"> +Ranke, Johannes, Janina Wöltjen, Jana Schmidt, and Emmanuelle Comets. +2021. <span>“Taking Kinetic Evaluations of Degradation Data to the Next +Level with Nonlinear Mixed-Effects Models.”</span> <em>Environments</em> +8 (8). <a href="https://doi.org/10.3390/environments8080071">https://doi.org/10.3390/environments8080071</a>. </div> -<div id="ref-dimethenamid_rar_2018_b8"> -<p>Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria. 2018. “Renewal Assessment Report Dimethenamid-P Volume 3 - B.8 Environmental fate and behaviour, Rev. 2 - November 2017.” <a href="https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716">https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716</a>.</p> +<div id="ref-dimethenamid_rar_2018_b8" class="csl-entry"> +Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria. +2018. <span>“<span class="nocase">Renewal Assessment Report +Dimethenamid-P Volume 3 - B.8 Environmental fate and behaviour, Rev. 2 - +November 2017</span>.”</span> <a href="https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716">https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716</a>. </div> </div> </div> diff --git a/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png b/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png Binary files differindex 4999e72c..505072ce 100644 --- a/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png +++ b/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png diff --git a/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png b/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png Binary files differindex b59764b1..505072ce 100644 --- a/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png +++ b/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png diff --git a/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png b/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png Binary files differindex da7ceeb6..0dd4da39 100644 --- a/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png +++ b/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png diff --git a/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_sfo_const-1.png b/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_sfo_const-1.png Binary files differindex 467c3c1a..0ed7448d 100644 --- a/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_sfo_const-1.png +++ b/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_sfo_const-1.png diff --git a/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_tc-1.png b/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_tc-1.png Binary files differindex 800c320b..d941f3e6 100644 --- a/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_tc-1.png +++ b/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_tc-1.png diff --git a/vignettes/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png b/vignettes/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png Binary files differindex 4d2dc94e..a799b14c 100644 --- a/vignettes/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png +++ b/vignettes/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png diff --git a/vignettes/web_only/mkin_benchmarks.rda b/vignettes/web_only/mkin_benchmarks.rda Binary files differindex c07def65..64ac2680 100644 --- a/vignettes/web_only/mkin_benchmarks.rda +++ b/vignettes/web_only/mkin_benchmarks.rda diff --git a/vignettes/web_only/saem_benchmarks.rda b/vignettes/web_only/saem_benchmarks.rda Binary files differindex 992b58cf..73459b36 100644 --- a/vignettes/web_only/saem_benchmarks.rda +++ b/vignettes/web_only/saem_benchmarks.rda |
